KR102097715B1 - Online waypoint path refinement method and recording medium storing program for executing the same, and computer program stored in recording medium for executing the same - Google Patents

Abstract

The present invention relates to a method for improving a real-time waypoint path, a recording medium in which a program for implementing the same is stored, and a computer program stored in a medium to implement the same. More specifically, according to the present invention, by determining a waypoint in the advancing direction of a robot outside a sensor sensing area of the robot as a target waypoint, it is possible for the robot to autonomously travel more challengingly by finding a more optimized path.

Description

How to improve the real-time waypoint path, a recording medium storing a program for implementing it, and a computer program stored in the medium to implement it {ONLINE WAYPOINT PATH REFINEMENT METHOD AND RECORDING MEDIUM STORING PROGRAM FOR EXECUTING THE SAME, AND COMPUTER PROGRAM STORED IN RECORDING MEDIUM FOR EXECUTING THE SAME}

The present invention relates to a method for improving a real-time waypoint path, a recording medium in which a program for implementing the same is stored, and a computer program stored in a medium to implement the same, more specifically, a waypoint in the robot progressing direction outside the sensor detection area of the robot. Is determined as the target waypoint, and more challengingly, the robot finds a more optimized route and improves the real-time waypoint route that enables autonomous driving, a recording medium storing a program for implementing it, and a computer program stored in a medium to implement the same. It is about.

An autonomous driving robot refers to a robot that looks around itself and detects obstacles and uses the wheels or legs to find the optimal route to the destination.

Path planning or obstacle avoidance is an important element technology for autonomous driving of mobile robots. The robot creates a path to the destination and moves, but must reach the destination without colliding with nearby obstacles. A good route means a shortest route that minimizes a route to a destination or a safety route that minimizes the possibility of collision with surrounding obstacles.

In the robot application, the safety path is more important, but the most ideal path will be the safest and shortest possible path.

In general, as a way to secure a safety path, use an obstacle detection sensor (a device that can measure the distance from surrounding obstacles, such as lasers or ultrasonic waves) mounted on a robot, find the direction with the most empty space, and find the destination direction together. Considering this, a method of determining a moving direction of a robot has been mainly used. The weight between the direction toward the empty space and the direction toward the destination is determined experimentally. The possibility of collision with obstacles can be minimized by applying a lot of weight to the empty space, but it is necessary to travel a long path or, in extreme cases, fail to reach a destination. Conversely, if a lot of weights are applied to a destination, safety is deteriorated.

The basic driving ability that an autonomous driving robot must have is an intelligent navigation capability that can move to an optimal route without a collision to a desired target point, and route planning technology and position recognition element technology are required for such an intelligent navigation.

Here, the route planning technology can be divided into a global route planning and a local route planning technology.

First, global route planning is to search for an optimal route from a starting point to a target point using a given environmental map.

Local route planning means generating a real movement trajectory to avoid dynamic obstacles recognized by sensors.

In addition, the position recognition technology is a positioning technology that finds the current position of the robot on the map when driving.

As an example of the prior art for the global route planning method as described above, representatively, for example, "A note on two problems in connexion with graphs (EW Dijkstra, Numerische Mathematik, Volume 1, Number 1, 269-271, 1959).

More specifically, the Dijkstra algorithm described above is proposed as the first route plan and has been widely used in various fields to date, but has a disadvantage in that it requires a lot of computation time because it searches all spaces.

In addition, as another example of the prior art for the global route planning method as described above, for example, "A Formal Basis for the Heuristic Determination of Minimum Cost Paths in Graphs_A star (PETER E. HART, VOL. Ssc-4, NO 2, 1968).

More specifically, the A * algorithm described above, as a complement to the Dijkstra algorithm, can achieve a faster search time than the Dijkstra algorithm based on depth search and area search by adding an appropriate evaluation function.

Local route planning is non-environment because the local trajectory planner follows the route generated by the global plan given to the robot based on the current state of the robot and local environment information. It is a technique to plan the trajectory so that it can move to avoid collisions caused by enemy obstacles.

Local route planning is an essential technique for moving robots to their destinations without obstacles and collisions in everyday spaces where environmental changes inevitably occur, and real-time properties are required unlike the global route planning.

As an example of the prior art for the local route planning method as described above, there is an incremental (gradual) planning method of planning a route by giving waypoints that the robot should pass.

The incremental (incremental) planning method is capable of quick calculation, but if the distance between waypoints is short or the angle between waypoints is small, the robot passes the target waypoint and then turns to the next target waypoint. There is a problem in that an overrun phenomenon that becomes large occurs.

Korean Patent Registration [10-1079197] discloses a method for tracking a path of an autonomous vehicle.

Korean Registered Patent [10-1079197] (Registration Date: 2011.10.27)

Therefore, the present invention has been devised to solve the above-described problems, and the object of the present invention is to determine a waypoint in the direction of the robot outside the sensor detection area of the robot as a target waypoint, and if an overrun phenomenon does not occur Therefore, it is to provide a real-time waypoint path improvement method capable of autonomous driving in search of a more challenging and more optimized path, a recording medium storing a program for implementing the same, and a computer program stored in a medium to implement the same.

The purpose of the embodiments of the present invention is not limited to the above-mentioned purpose, and other objects not mentioned will be clearly understood by those skilled in the art from the following description. .

A real-time waypoint path improving method according to an embodiment of the present invention for achieving the above object is a real-time waypoint path improving method consisting of a program executed by a calculation processing means including a computer, A waypoint path determining step (S10) in which the processing means determines a waypoint path which is a set of waypoint path segments forming an optimal path to a target point among waypoint path segments that are a path connecting the waypoint and the waypoint (S10); In the local visible position space, which is a set of areas that do not collide with obstacles when the calculation processing means moves straight from the robot among the movable areas of the robot sensed by the sensor of the robot, in the waypoint path determining step (S10) A local goal determination step (S20) of determining a next waypoint of a last waypoint or a next waypoint of a waypoint pass segment as a target goal point local goal on the determined waypoint pass; A path planning step (S30) in which the calculation processing means plans a path from the robot to the local goal determined in the local goal determination step (S20); And a robot control step (S40) for controlling the movement of the robot by calculating a command for controlling the robot according to the path planned in the path planning step (S30).

In addition, the path planning step (S30) enters the local goal accessible pass segment, which is an area from the current position of the moving object to the closest obstacle from the local goal in the waypoint pass segment connecting the local goal and the previous waypoint. It is characterized by planning the route.

In addition, the route planning step (S30) is characterized in that a route is planned by planning an accessible waypoint, which is a temporary waypoint entering the local goal accessible pass segment.

In addition, in the path planning step (S30), if there is an intersection of the local visible position space and the local goal accessible pass segment, the point where the distance to the local goal is the shortest among the local goal accessible pass segments is the accessible waypoint. It is characterized by determining.

In addition, in the path planning step (S30), if there is an obstacle on the local goal accessible pass segment, there is no collision with the obstacle on the path between the local waypoints, and the temporary way toward the direction in which there are no obstacles on the local visible position space It is characterized by planning a local waypoint, a point, planning an accessible waypoint at a point after passing an obstacle on the local goal accessible pass segment, and planning a route through the local waypoint and the accessible waypoint. Is done.

In addition, in the path planning step (S30), when there is an obstacle on the local goal accessible pass segment, setting the local way point and the accessible way point with the smallest cost of the local way point and the accessible way point is set. It is characterized by.

In addition, the path planning step (S30) is characterized in that when there is an obstacle on the local goal accessible pass segment, it is characterized in that to set a local waypoint at which the angle formed with the direction of the current robot is minimal.

In addition, according to an embodiment of the present invention, a computer readable recording medium storing a program for implementing the real-time waypoint path improvement method is provided.

In addition, according to an embodiment of the present invention, in order to implement the real-time waypoint path improvement method, a program stored in a computer-readable recording medium is provided.

According to a method of improving a real-time waypoint path according to an embodiment of the present invention, a recording medium storing a program for implementing the same, and a computer program stored in a medium to implement the same, the waypoint in the robot progressing direction outside the sensor detection area of the robot By determining as the target waypoint, the robot is more challenging to find a more optimized route and autonomous driving, thereby minimizing the occurrence of overrun of the robot and at the same time having a small amount of computation, thereby enabling real-time route computation.

In addition, it is possible to minimize the occurrence of overrun or vibration of the robot by planning the bi-directional path using the forward path and the reverse path, and has the effect of real-time path calculation.

In addition, by applying a function decomposition-based cost minimization algorithm that reduces the amount of computation by decomposing the cost function required for movement, it is possible to further reduce the amount of computation required for path calculation.

을 근거로 경로계산을 함으로써, 경로계산에 필요한 연산량을 더욱 줄일 수 있는 효과가 있다.In addition, the time coordination safety area

By calculating paths on the basis of, it is possible to further reduce the amount of computation required for path calculation.

In addition, a waypoint setting step, a reverse path calculation step, a forward path calculation step, and a robot control command generation step are performed. However, by performing the reverse path calculation step before the forward path calculation step, the path is smoothly passed through the target waypoint. It is capable of forming.

In addition, by using a connection path connecting the forward path and the reverse path, there is an effect that the forward path and the reverse path can be connected so as not to force the robot.

In addition, by performing a connection path verification step between the reverse path calculation step and the forward path calculation step, there is an effect of further reducing the amount of computation required for the path calculation.

In addition, if there is a connection path, the forward time indexes for the connection path and the reverse path are set to check the collision with the dynamic obstacles in the connection path and the reverse path that were not considered in the calculation. have.

In addition, by planning the path to the local goal accessible pass segment, there is an effect of reducing the occurrence of overrun compared to the path to the waypoint.

In addition, by planning an route by planning an accessible waypoint entering the local goal accessible pass segment, there is an effect of further reducing the occurrence of overruns compared to the route entering the local goal.

In addition, when there are no obstacles, by planning a route based on an accessible waypoint, it is possible to reduce the amount of computation and simultaneously plan an optimized route to enter the local goal accessible pass segment.

In addition, if there is an obstacle, the route is planned based on the local way point and the accessible way point, thereby reducing the amount of computation even when an obstacle appears and simultaneously planning an optimized route to enter the local goal accessible pass segment. It has the effect.

In addition, when there is an obstacle, the cost of the local waypoint and the accessible waypoint is set to the smallest, thereby setting the local waypoint and the accessible waypoint, thereby minimizing the risk factor.

In addition, when there is an obstacle, there is an effect of minimizing overrun by setting a local waypoint at which the angle formed with the current robot's traveling direction is minimum.

1 is a flowchart of a method for real-time waypoint path improvement according to an embodiment of the present invention.
Figure 2 is an exemplary view for explaining a local workspace applied to the real-time waypoint path improvement method according to an embodiment of the present invention.
3 is an exemplary diagram for explaining a local position space applied to a real-time waypoint path improvement method according to an embodiment of the present invention.
4 is an exemplary diagram for explaining a local visible position space applied to a real-time waypoint path improvement method according to an embodiment of the present invention.
5 is an exemplary view for explaining a process of determining a local goal in a local goal determination step of a real-time waypoint path improvement method according to an embodiment of the present invention.
6 is an exemplary diagram for explaining a local goal accessible pass segment applied to a real-time waypoint path improvement method according to an embodiment of the present invention.
7 is an exemplary view for explaining a local waypoint and an accessible waypoint applied to a real-time waypoint path improvement method according to an embodiment of the present invention.
8 is a flowchart of a method for planning an online bidirectional route in a time state domain according to an embodiment of the present invention.
9 is a flowchart of a method for planning an online interactive path in a time state domain according to another embodiment of the present invention.
10 is a flowchart of an online bidirectional route planning method in a time state domain according to another embodiment of the present invention.
11 is an exemplary view showing an example of calculating a route in real time according to a movement of a robot to which a method for improving a real-time waypoint route according to an embodiment of the present invention is applied.
12 is an exemplary view showing a path the robot has moved as a result of calculating the path of FIG. 11.
13 is an exemplary view showing a simulation result in a place where the door is opened and closed.
14 is an exemplary view showing a simulation result changed according to an update of the sensing data and a path calculation cycle.
15 is an exemplary view showing a result of autonomous driving simulation according to a general route plan to which the present invention is not applied.
16 is an exemplary view showing a result of an autonomous driving simulation according to a route plan to which a real-time waypoint route improvement method according to an embodiment of the present invention is applied.

The present invention can be applied to various changes and can have various embodiments, and specific embodiments will be illustrated in the drawings and described in detail. However, this is not intended to limit the present invention to a specific embodiment, it should be understood to include all modifications, equivalents, and substitutes included in the spirit and scope of the present invention.

When an element is said to be "connected" or "connected" to another component, it is understood that other components may be directly connected to or connected to the other component, but other components may exist in the middle. It should be.

On the other hand, when a component is said to be "directly connected" or "directly connected" to another component, it should be understood that no other component exists in the middle.

The terms used in this specification are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this application, terms such as “include” or “have” are intended to indicate that a feature, number, process, operation, component, part, or combination thereof described in the specification exists, and that one or more other features are present. It should be understood that the presence or addition possibilities of fields or numbers, processes, operations, components, parts or combinations thereof are not excluded in advance.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person skilled in the art to which the present invention pertains. Terms such as those defined in a commonly used dictionary should be interpreted as having meanings consistent with meanings in the context of related technologies, and should not be interpreted as ideal or excessively formal meanings unless explicitly defined in the present application. Does not.

Hereinafter, the present invention will be described in more detail with reference to the accompanying drawings. Prior to this, the terms or words used in the present specification and claims should not be interpreted as being limited to ordinary or dictionary meanings, and the inventor appropriately explains the concept of terms in order to explain his or her invention in the best way. Based on the principle that it can be defined, it should be interpreted as meanings and concepts consistent with the technical spirit of the present invention. In addition, unless there are other definitions in the technical terms and scientific terms used, those skilled in the art to which this invention pertains have the meanings commonly understood, and the subject matter of the present invention in the following description and the accompanying drawings. Descriptions of well-known functions and configurations that may be obscured are omitted. The drawings introduced below are provided as examples in order to sufficiently convey the spirit of the present invention to those skilled in the art. Accordingly, the present invention is not limited to the drawings presented below and may be embodied in other forms. In addition, the same reference numbers throughout the specification indicate the same components. It should be noted that the same components in the drawings are denoted by the same reference numerals wherever possible.

1 is a flowchart of a method for improving a real-time waypoint path according to an embodiment of the present invention, and FIG. 2 is an exemplary diagram for explaining a local workspace applied to a method for improving a real-time waypoint path according to an embodiment of the present invention 3 is an exemplary view for explaining a local position space applied to a real-time waypoint path improvement method according to an embodiment of the present invention, and FIG. 4 is a real-time waypoint path improvement method according to an embodiment of the present invention 5 is an exemplary view for explaining a local non-visible position space, and FIG. 5 is an exemplary view for explaining a process in which a local goal is determined in a local goal determination step of a real-time waypoint path improvement method according to an embodiment of the present invention. 6 is a local goal accessible pass segment applied to a real-time waypoint path improvement method according to an embodiment of the present invention. 7 is an exemplary diagram for explaining a local waypoint and an accessible waypoint applied to a method for improving a real-time waypoint path according to an embodiment of the present invention. FIG. 9 is a flowchart of an online bidirectional route planning method in a time state domain according to an embodiment of the present invention, and FIG. 9 is a flowchart of an online bidirectional route planning method in a time state domain according to another embodiment of the present invention. It is a flowchart of an online bidirectional route planning method in a time state domain according to another embodiment of the present invention, and FIG. 11 is an example showing an example of calculating a route in real time according to a movement of a robot to which the method for improving a real-time waypoint in the present invention is applied. Fig. 12 is an exemplary view showing a path the robot has moved as a result of calculating the path of Fig. 11, and Fig. 13 is a place where the door is opened and closed. Is an exemplary view showing the simulation results of, and FIG. 14 is an exemplary view showing the simulation results changed according to the updating of the sensing data and the path calculation cycle, and FIG. 15 is an autonomous driving simulation result according to a general route plan to which the present invention is not applied. 16 is an exemplary view showing a result of autonomous driving simulation according to a route plan to which a real-time waypoint route improvement method according to an embodiment of the present invention is applied.

As shown in FIG. 1, the real-time waypoint path improvement method according to an embodiment of the present invention is a real-time waypoint path improvement method in a program form executed by a calculation processing means including a computer, a waypoint path It includes a determination step (S10), a local goal determination step (S20), a path planning step (S30), and a robot control step (S40).

Prior to the description, the terms used in the specification (and claims) will be briefly described.

The waypoint means a point that the robot can designate as a target for driving, and is indicated by a dot in the drawing. Targeting can be passed via waypoints, but you can also pass nearby without passing.

The waypoint pass segment means a route connecting a waypoint and a waypoint. In general, the waypoint pass segment may be a line segment connecting points (waypoints) and points (waypoints), but may also be curved. In addition, the waypoint pass segment is a relatively safe route that does not collide with obstacles on the map. In other words, it is a path without collision with a fixed obstacle displayed on the map.

The waypoint pass means a set of waypoint pass segments that form an optimal path to a target point. That is, the waypoint path is a path consisting of waypoint path segments to a target point, and may be a set of segments.

The local workspace refers to a workspace (area) based on sensor data (see FIG. 2).

Local workspaces include local free workspaces, local obstacle workspaces, and local unknown workspaces.

는 센서로 감지된 장애물이 없는 워크스페이스를 의미하며, 도 2의 주황색 면으로 표시된 영역에 해당된다.Local Free Workspace

Means a workspace without an obstacle detected by a sensor, and corresponds to an area indicated by an orange surface in FIG. 2.

는 센서로 감지된 장애물로 제한된 워크스페이스를 의미하며, 도 2의 보라색 선분으로 표시된 영역에 해당된다.Local Obstacle Workspace

Means a workspace limited by obstacles detected by the sensor, and corresponds to an area indicated by the purple line segment in FIG. 2.

는 센서로 감지되지 않는 지역이라 장애물과의 충돌을 예측할 수 없는 워크스페이스를 의미하며, 도 2의 색이 없는 영역에 해당된다.Local Unknown Workspace

Is an area that is not sensed by a sensor, and means a workspace in which collision with an obstacle cannot be predicted, and corresponds to an area without color in FIG. 2.

The local position space means a sensor data-based work space (area) in consideration of the collision according to the shape of the robot.

The local position space includes a local obstacle position space, a local preposition space, a local unknown position space, and a local non-visible position space.

는 센서로 감지된 장애물 영역

과 충돌하는 워크스페이스를 의미하며, 도 3의 주황색 영역에 해당된다.Local Obstacle Position Space

Is the obstacle area detected by the sensor

Means a workspace that collides with, and corresponds to the orange region of FIG. 3.

는 센서로 감지된 장애물과 충돌이 없는 워크스페이스를 의미하며, 도 3의 하늘색 영역에 해당된다.Local preposition space

Denotes a work space free from collision with an obstacle detected by a sensor, and corresponds to the sky blue region of FIG. 3.

는 센서로 감지되지 않아 충돌을 예측할 수 없는 워크스페이스를 의미하며, 도 3의 색이 없는 영역에 해당된다.Local Unknown Position Space

Means a workspace that cannot be predicted because it is not sensed by a sensor, and corresponds to an area without color in FIG. 3.

는 현재 로봇의 위치로부터 로봇이 직진으로 이동을 했을 때 장애물과 충돌이 없는 영역(공간)의 집합을 의미하며, 도 4의 붉은색 영역에 해당된다.Local Visible Position

Is a set of areas (spaces) where there are no obstacles and collisions when the robot moves straight from the current robot position, and corresponds to the red area in FIG. 4.

는 로컬골과 그 이전 웨이포인트를 연결한 웨이포인트패스세그먼트 중 장애물과 충돌 없이 로컬골로 바로 접근할 수 있는 영역을 의미하며, 도 6의 보라색 선분으로 표시된 영역에 해당된다.Local Goal Accessible Pass Segment

Denotes an area directly accessible to the local goal without collision with an obstacle among the waypoint pass segments connecting the local goal and the previous waypoint, and corresponds to the area indicated by the purple line segment in FIG. 6.

Although basic variables and terms are summarized, not all variables and terms necessary for explanation are listed, and newly emerging variables and terms in addition to the variables and terms defined above will be described later.

The waypoint path determining step S10 determines the waypoint path, which is a set of waypoint path segments that form an optimal path to a target point among waypoint path segments, which are paths in which the calculation processing means connects the waypoints and waypoints. .

The waypoint path determining step S10 determines an optimal route connecting waypoints based on previously input map information.

The waypoint pass can be regarded as an initial route, but it should be noted that the robot does not follow the waypoint pass.

That is, the waypoint pass is only information that is referenced to find the optimal route.

The local goal determination step (S20) is a local non-visible position space that is a set of obstacle-free collisions (on the map) when the calculation processing means moves straight from the robot among the movable areas of the robot detected by the robot's sensor. Among them, the next waypoint of the last waypoint or the next waypoint of the waypoint pass segment on the waypoint pass determined in the waypoint path determining step S10 is determined as a local goal that is the target waypoint.

The local non-visible position space is narrower than the local pre-position space.

This is because the collision according to the shape of the robot is considered.

For ease of understanding, the local non-visible position space is shown in FIG. 4 in red color, and the local pre-position space is displayed in sky blue to overlap.

The real-time waypoint path improvement method according to an embodiment of the present invention is to find a more optimized path for a robot to be more challenging,

Since it is clear that the local optacle position space will collide, if you think about finding an optimized path considering the local unknown position space,

There are no collisions with obstacles on the map that were included in the local unknown position space and on the waypoint pass where the global pass was made (that is, the intersection of the local unknown position space and the waypoint pass), but there were no obstacles (such as people) on the map. ).

Therefore, in the order of the lowest risk among the local position spaces, the local non-visible position space, the local preposition space, the local unknown position space, and the local obstacle position space are in this order.

Assuming that all the workspaces are given and the robot's position and waypoints are specified,

When the robot (operation processing means) is located on the waypoint path, it follows the waypoint path, and when it falls on the waypoint path, it plans and moves based on the detected information.

In other words, when a robot falls on a waypoint pass for reasons such as avoiding obstacles, the robot can designate a local goal at a certain point based on the result recognized by the robot and plan a path based on the local goal.

In general, a local goal is determined on a basic waypoint pass and a route (path) is planned to go to the local goal.

The important thing at this time is the question of where to set the local goal.

It is good to take the first waypoint because it is safe to go straight on the local visible position space when it is important to improve the planned route (refine the route).

In the local goal determination step (S20), the waypoint at the rear of the waypoints that can safely go straight can be determined as a local goal, but it is not challenging and does not consider it, and the waypoint is placed on an invisible area, that is, a local unknown position space. I was worried about filming,

In order to plan the path more challengingly, the last waypoint or the next waypoint of the waypoint pass segment identified on the local nonvisible position space is determined as the local goal.

At this time, if there is an obstacle in the local goal, a path can be planned for a path closest to the local goal without colliding with an obstacle in the local goal accessible pass segment on the local unknown position space.

Among the local invisible positioning spaces, the next waypoint of the last waypoint on the waypoint path or the next waypoint of the waypoint pass segment is determined as a local goal that is the target waypoint.

When the local goal determination step S20 determines the next waypoint of the last waypoint corresponding to the intersection of the local non-visible position space and the waypoint path, as an example,

In both the left and right figures of FIG. 5, since the last waypoint on the local non-visible position space is a point indicated by a blue dot, the next waypoint indicated by a green dot can be determined as a local goal (W _M ).

That is, when determining the local goal based on the waypoint, the next waypoint of the last waypoint that can be confirmed (visible) can be determined as the local goal.

In the path planning step S30, the calculation processing means plans a path from the location of the robot to the local goal determined in the local goal determination step S20.

Prior to the description of the route planning step (S30), the terms used in this specification will be briefly described.

'Time state region' refers to a set of state values considered with respect to time.

The 'state value' refers to a value including coordinates for position and orientation (heading angle, etc.) and any one or a plurality of information selected from steering angle, speed (linear velocity, angular velocity), and acceleration. For example, (x coordinate, y coordinate, orientation, speed, acceleration), and the like.

'Time coordination region' refers to the set of coordination values considered over time.

The 'coordinate value' refers to a value including a position and an orientation (heading angle, etc.). For example, (x coordinate, y coordinate, orientation), and the like.

For the explanation, various variables and terms were specified and explained, and the variables and terms are as follows.

The status value of the robot's current position is denoted by 's _k ',

The status value calculated by f step from the current position of the robot in the forward path is indicated as 's _f ',

When the robot arrives at the current target waypoint, the robot's status value is indicated as 'g _w ',

The status value of step b calculated from the current target waypoint in the reverse path is denoted by 's _-b ',

The time index indicating how many steps corresponds to the calculation is indicated by 'n',

The time period assigned to calculate one step is denoted by 'T',

The time at time index n is expressed as 't' (t = nT),

The state value of the robot is indicated by 's' (lowercase s),

The state area, which is a set of robot state values, is denoted by 'S' (uppercase letter S),

The time status value, which is the status value at time step n, is denoted as s _n (lowercase s),

The time status area is denoted by 'S _n ' (s _n ∈ S _n ),

The coordinates of the robot are denoted by 'q' (lowercase q),

The coordinate region, which is the set of coordinate values of the robot, is denoted by 'Q' (uppercase Q),

The time coordination value, which is the coordination value in time step n, is denoted by q _n (lowercase q),

The time coordination region is denoted by 'Q _n ' (q _n ∈ Q _n ),

'로 표기하였고,The coordinate region that collides with the static obstacle is'

'

(

= Q -

)로 표기하였고,The free static coordination region that does not collide with static obstacles

(

= Q-

).

으로 표기하였고,The coordinate region that collides with the dynamic obstacle at time index n is

Is denoted as

'(

=

-

)으로 표기하였고,The free dynamic coordination area that does not collide with static obstacles and dynamic obstacles is'

'(

=

-

).

A state in which collision could not be avoided was referred to as an 'inevitable conflict state'.

Although basic variables and terms are summarized, not all variables and terms necessary for description are listed, and newly emerging variables and terms other than the variables and terms defined above will be described later.

In the following description, it is assumed that the robot is moved from a point of the current state corresponding to the state value s _k to a target point (current target waypoint) corresponding to the state value g _w .

The route planning step (S30) of the real-time waypoint route improvement method according to an embodiment of the present invention plans a bidirectional route to gradually plan (calculate) a forward route and a reverse route, and is used to plan the forward route and the reverse route The state value (s _n (n represents a time index, a natural number)) in the time state area to be included includes coordinate values for position and orientation (heading angle), and selects steering angle, speed (linear velocity, angular velocity), and acceleration It may be characterized by including any one or a plurality of information.

Forward path calculation calculates the forward path from the current position of the robot or the last calculation point of the forward path to the current target waypoint or the last calculation point of the backward path, in the time state domain considering the state value (s) and time value. .

Here, the time value is a value that can check the future time progressing from the current time, but can be used only with a time index (n), but by multiplying the calculation cycle for calculating one step by a time index (n) (calculation cycle * Of course, various methods can be used if the actual progress time can be checked, such as using a time index (n)).

The forward path calculation may calculate the forward path in the time state region from the current position of the first robot toward the current target waypoint. Thereafter, if the forward path and the reverse path have been calculated, the forward path in the time state region from the last calculation point of the forward path to the last calculation point of the backward path can be calculated.

The backward path calculation calculates the backward path in the time state domain from the current target waypoint or the last calculation point of the reverse path toward the current position of the robot or the last calculation point of the forward path.

The backward path calculation may calculate a backward path in the time state region toward the current position of the robot at the initial current target waypoint. Thereafter, if the forward path and the reverse path are calculated, the forward path in the time state region from the last calculation point of the reverse path toward the last calculation point of the forward path is calculated.

That is, the forward path calculation and the reverse path calculation are planned to face the last calculation point on the opposite side.

Planning the reverse path allows the robot to pass smoothly as it passes the target waypoint, so that if the distance between the waypoints is short or the angle between the waypoints is small, the robot passes the target waypoint and moves toward the next target waypoint. This is to reduce the overrun phenomenon that the turning path becomes large.

The forward path calculation and the backward path calculation in the path planning step (S30) of the real-time waypoint path improvement method according to an embodiment of the present invention

Decompose the cost function (H) required for movement, give priority (r (r is a natural number)) to the decomposed cost function (H ^r ),

By applying an approximate optimal state time function (N) to calculate a path such that the sum of the decomposed cost functions (H ^r ) is minimized according to a predetermined criterion (r * value can be set or calculated). ,

Based on the given state value (s _n ) at the n (n is a natural number) step, the state value at the target point (s ^to ) and the number of steps to be calculated (m), at the point where the state value (s _n ) is given It may be characterized in that the state value s _{n + m} in the step to be progressed by the number of steps m to be calculated toward the target point s ^to may be obtained.

When planning the state value s _{n + m} with the given state value s _n , we try to minimize the moving cost to move from the state value s _{n + m} ^to the point corresponding ^{to the} target state value s ^to .

That is, the forward path calculation and the backward path calculation can be defined as follows.

(Where s _{n + m} is the state value at n + m time, H is the cost minimization function, s is the given state value, s ^to is the state value at the target point, and m is the number of steps to be calculated)

When the cost minimizing function H is used, there is a problem that it is difficult to apply it to real-time path calculation because it takes a lot of time for calculation.

The input u _n of the robot can be obtained as follows.

here

은 최소 노력에 대한 입력에 대한 주어진 이동비용 함수로, 어떤 입력 u를 이용하여 로봇의 상태를 s_n에서 s_n+m으로 천이시킬 때 발생하는 비용 값을 반환하는 비용 함수이다.

Is a given moving cost function for the input to the minimum effort, and is a cost function that returns the cost value that occurs when the state of the robot is transitioned from s _n to s _{n + m} using some input u.

을 최소화하는 u_n은 최소한의 노력으로 로봇을 목표 상태값으로 변화시킬 수 있는 입력 값이 된다.therefore,

U _n that minimizes is the input value that can change the robot to the target state value with minimal effort.

Since s _{n + m} is planned inside the dynamic safety condition limited to the input area of the available robot, there is always an available input.

If the moving cost function that minimizes the moving cost is too complex to calculate the path in real time, a function decomposition-based optimization method can be used.

The function decomposition-based optimization method first decomposes the function into simple parts and then minimizes the dominant part. Assume that H is composed of R parts as follows.

Here, H ^r is arranged in a predominant order. That is, H r with small ^r is more dominant. When sequentially minimizing H ¹ to H ^R one by one, the usable safety area becomes smaller, and in the end, the decomposed cost function (H ^r ) is minimized by increasing the value of r until one state value is derived.

을 다음과 같이 정의할 수 있다.Time state safety area to minimize the r part from H ¹ to H ^r

Can be defined as

은 시간상태안전영역

과 동일하다.Time State Safe Area Calculated by Unresolved Movement Cost Function

Is the time state safety area

Is the same as

이라 하며,

을 찾을 때까지 분해된 이동비용함수를 계속 최소화 시킨다. 여기서 사용 가능한 시간상태안전영역은 다음과 같이 단일 상태로 축소된다.R value from which one state value is derived

It is called

Continue to minimize the decomposed moving cost function until find. The time state safety area available here is reduced to a single state as follows.

here,

Although the time state value obtained from the function decomposition based minimization does not minimize H, the result is close to the minimum because it minimizes the dominant part of the function.

In addition, it can be executed in real time, and calculation is efficient.

Therefore, applying the approximate optimal state time function (N),

(Where s _{n + m} is the state value at n + m step, N is the approximate optimal time state value calculation function, s _n is the given state value at n step, s ^to is state value at target point, m is Number of steps to be calculated)

As a result, forward path calculation and reverse path calculation can be performed.

For further explanation, using the approximate optimal state time function (N), in order for the robot having the current state s _n to transition ^to the state s ^to , the optimal state value at the mth time index after n is s. _It is calculated as _{n + m} .

An example of a robot that can move in all directions,

The approximate optimal state time function (N) is

(Where H ¹ is a fast-reading independent travel cost function, H ² is an angular-travel direction partial cost function, H ³ is a velocity magnitude partial travel cost function)

Can be calculated using.

To guide the robot to move quickly toward the target and effectively pass the target point, the moving cost function H (s ^from , s ^to ) can be composed of three parts, and D is a predefined critical distance.

Each time state is determined based on the speed-independent moving cost function H ¹ because each position of the reachable region is matched with an allowable velocity region 1: 1.

The moving direction partial moving cost function H ² and the velocity magnitude partial moving cost function H ³ are used to set the state of the current target waypoint.

Based on H ¹ , each time setting is determined to be closest to the target.

H ² and H ³ are designed to set the status of the current target waypoint.

The speed at the current target waypoint is determined by considering the position of the robot, the position of the target waypoint, and the position of the next waypoint.

The direction is determined by the average direction of the two vectors up to the position of the robot, the position of the target waypoint, and the position of the target waypoint and the position of the next waypoint.

In addition, the magnitude of the speed at the target waypoint is determined by the cabinet of the route at the target waypoint.

The larger the angle, the faster or vice versa.

If the Euclidean distance between the position of the robot and the target waypoint or between the target waypoint and the next waypoint is less than D, the magnitude of the speed of the current target waypoint is set in proportion to the distance so as not to vibrate near the waypoint. .

As an example of a robot that can be moved by wheels, the approximate optimal state time function (N) is

Next

(Where H ¹ is a fast-reading independent travel cost function, H ² is an angular-travel direction partial cost function, H ³ is a velocity magnitude partial travel cost function)

Can be calculated using.

As in the example of a robot capable of omnidirectional movement, each time state is determined based on a speed independent movement cost function H ¹ .

To take into account both the position close to the target position and the direction towards the target position, H ¹ is composed of two parts with a predefined weight α.

The moving direction partial moving cost function H ² and the velocity magnitude partial moving cost function H ³ are used to set the linear and angular velocity of the current target waypoint in a manner similar to the example of a robot capable of moving in all directions.

이 존재하면, 타임인덱스(n) 에서 로봇의 배위값(

)과 타임인덱스(n) 에서 시간배위안전영역

을 근거로 순방향경로를 계산하는 것을 특징으로 하며,The forward path calculation of the online two-way path planning method in a time state domain according to an embodiment of the present invention is a time coordination safety area that is a safe area on a coordination area that does not collide with a static obstacle and a dynamic obstacle.

If present, the coordinates of the robot at the time index (n) (

) And time index (n) in the time coordinate safety zone

It is characterized by calculating the forward path on the basis of,

)과 시간배위안전영역

을 근거로 역방향경로를 계산하는 것을 특징으로 할 수 있다.When calculating the reverse path, the coordinate value of the robot at the time index (n),

) And time coordination safety area

It may be characterized by calculating the reverse path based on.

이라 하고 '환경장애물영역'(

⊂ Q)이라 하면, 배위영역 Q에서 환경장애물영역

을 뺀 차집합을 '여유정적배위영역'

이라 할 수 있다. A set of coordination values that collide with a static environmental obstacle, such as a wall,

'Environmental obstacles area' (

⊂ Q), from the coordination area Q to the environmental obstacle area

The difference set minus is 'Yeo-static coordination area'

It can be said.

The robot can predict the path of each dynamic obstacle, such as a person and another robot, based on a tracking algorithm using sensor data.

이라 하고 '동적장애물영역'(

⊂ Q)이라 하면, 여유정적배위영역

에서 동적장애물영역

을 뺀 차집합을 '여유동적배위영역'

이라 할 수 있다. Therefore, at time index n, a set of coordination values that are expected to collide with dynamic obstacles such as humans

'Dynamic obstacle area' (

⊂ Q), the marginal static coordination area

Dynamic obstacle area

The difference set minus the 'free flowing coordination area'

It can be said.

은 타임인덱스 n에서 동적장애물 및 정적장애물을 포함하는 장애물과 충돌하지 않는 로봇을 위한 안전한 영역이라 할 수 있다. In other words, the free dynamic coordination region at time index n

Is a safe area for robots that do not collide with obstacles including dynamic obstacles and static obstacles in time index n.

The motion of the robot system with the input value u _n (u _n ∈ U) of the robot at the time index n in the input area U of the robot can be defined as follows.

(Where f is the motion model function of the robot)

Since the boundary is defined between the robot's state area S and the robot's input area U, the robot motion is constrained and this is called motion constraint.

및

을 얻을 수 있다.The environmental detector (sensor) uses a sensor such as a vision sensor or a laser range finder (LRF) to detect and track obstacles based on the sensor system, and classifies them into static obstacles (environmental obstacles) and dynamic obstacles (non-environmental obstacles).

And

Can get

내부에 일련의 웨이포인트와 목표를 계획한다. The global path planner that assigns waypoints to plan a route is a free static coordination area.

Plan a series of waypoints and goals inside.

Every moment t = kT, k = 0, 1, ... The algorithm uses the reference input k to plan the path.

및

을 사용하여 충돌 없이 목표에 도달하는 경로를 계획할 수 있다.With this input, the robot passes through the waypoint sequentially,

And

You can use to plan the path to reach your goal without collision.

When the time state value s _n is given, the reachable time state area at the time index n + m can be defined, and at this time, the reachable time coordination area can also be defined.

For real-time route planning, it is desirable to reduce the amount of computation by approximating the time state region or time coordination region that can be reached.

It is because real-time calculation can be difficult to calculate and use the time state region or time coordination region that can be reached accurately.

In order to simplify the reachable area, the robot can be controlled with a uniform input value of the robot for m time steps.

The reachable time state region accessible by the robot's uniform input can be defined as follows.

(Where, g is a motion model function of the approximated robot)

At this time, a region approximating a time coordination region reachable by a uniform input value of the robot may be defined as follows.

과

은 실제 도달 가능 영역 전체를 커버하지 않지만, 실제 도달 가능 영역 전체를 계산하는 것에 비해 계산 부하가 매우 작기 때문에 실시간 경로 계획에 유용하다.

and

Does not cover the entire real reachable area, but is useful for real-time route planning because the computational load is very small compared to calculating the entire real reachable area.

은 다음과 같이 정의할 수 있다.If a state value s _n is given, a safe time state safe area at time index n + m

Can be defined as

은 다음과 같이 정의할 수 있다.In addition, given the state value s _n , the safe time coordination safe area at the time index n + m

Can be defined as

은

내에서 계획되지만

은 예상 충돌을 확인하고 사용 가능한 안전 영역이 있는지 확인하는 것으로 충분하다.

silver

Plan within, but

It is sufficient to check for possible collisions and to ensure that there are safe areas available.

계산 시,

은

로 대체된다.The time coordinated safety area of the reverse path from the current target waypoint

When calculating,

silver

Is replaced by

에 대한 실제 시간을 알 수 없기 때문에 동적 장애물의 데이터를 확인할 수 없기 때문이다.this is,

This is because the data of the dynamic obstacles cannot be checked because the actual time for it is unknown.

As shown in FIG. 8, the route planning step (S30) of the real-time waypoint route improvement method according to an embodiment of the present invention includes a waypoint setting step (S32), a reverse route calculating step (S33), and a forward route calculating step. It may include (S35).

The waypoint setting step (S32) sets the state of the robot at the current target waypoint.

The current target waypoint is the waypoint to which the forward trajectory is headed.

The waypoint setting step (S32) is to set the state of the robot when the robot passes the current target waypoint, considering the position of the robot, the position of the target waypoint, and the position of the next waypoint, the current target way It is desirable to set the state of the robot when the robot passes the point.

Therefore, the waypoint setting step (S32) is characterized in that the current target waypoint is a waypoint to which the forward path is directed, and the state value (g _w ) of the current target waypoint that can minimize the moving cost is set as follows. can do.

는 현재 목표 웨이포인트의 상태값,

는

를 세팅하는 동안 다음 목표 웨이포인트의 상태값, G_w는 현재 목표 웨이포인트의 상태 범위, G_w+1는 다음 목표 웨이포인트의 상태 범위, H(a, b)는 a에서 b까지 이동에 대한 비용함수,

는 로봇의 현재 위치

는 현재 목표 웨이포인트

는 현재 목표 웨이포인트 다음 웨이포인트를 나타낸다.)(here,

Is the current target waypoint status,

The

While setting, the status value of the next target waypoint, G _w is the status range of the current target waypoint, G _{w + 1} is the status range of the next target waypoint, and H (a, b) is for moving from a to b. Cost Function,

Is the current position of the robot

Is the current target waypoint

Indicates the waypoint following the current target waypoint.)

In the reverse path calculation step S33, the reverse path is calculated.

The reverse path calculation step (S33) plans a path from the current target waypoint toward the current position of the robot. At this time, if the forward path is calculated, the backward path toward the last calculation point of the forward path can be calculated.

The variable B indicated in the conditional statement of FIG. 8 means the maximum value of the time step for the reverse path planning, and the variable b means the time step of the reverse path planning (increased by 1 when iterative calculation), but the variable and the conditions for the variable are arbitrarily set. Of course, it can be used in a variety of ways. (The same applies to the variables in FIGS. 9 to 10)

The forward path calculation step S50 calculates the forward path.

In the forward path calculation step (S50), a route is planned from the current position of the current robot to the target waypoint. At this time, if the reverse path has been calculated, the forward path toward the last calculation point of the reverse path can be calculated.

The variable F indicated in the conditional statement of FIG. 8 is the maximum value of the time step for forward path planning, the variable f is the time step of the reverse path planning (incremented by 1 when iterating), and k means the current time step, but the variable and the Of course, it can be used in various ways by arbitrarily setting conditions. (The same applies to the variables in FIGS. 9 to 10)

The reverse path calculation step (S33) and the forward path calculation step (S35) may be performed by reversing the order, but performing the reverse path calculation step (S33) first passes the path so that the target waypoint passes smoothly. It can be formed, it is preferable to perform the reverse path calculation step (S33) first.

The reverse path calculation step (S33) and the forward path calculation step (S35) may be alternately repeated, and the reverse path calculation step (S33) or the forward path calculation step (S35) is first repeatedly performed a certain number of times. The reverse path calculation step (S33) and the forward path calculation step (S35) may be repeatedly performed in various ways, such as the forward path calculation step (S35) or the reverse path calculation step (S33) may be repeated later. .

In conclusion, in the reverse path calculation step (S33) and the forward path calculation step (S35), the bidirectional path planning from the current location to the current target waypoint is completed only when the reverse path and the forward path are connected.

The route planning step (S30) of the real-time waypoint route improvement method according to an embodiment of the present invention calculates a connection route connecting the forward route and the reverse route, and gradually calculates a forward route and a reverse route. It can be characterized by planning.

If you plan the bi-directional path continuously, the forward path and the reverse path may meet, but the speed may be different.

Therefore, in order to overcome the speed difference between the forward path and the reverse path, it is preferable to be connected by the connection path.

That is, one side of the connection path connected to the forward path side is calculated to have the same speed as the last calculated point of the forward path, and the other side of the connection path connected to the reverse path side has the same speed as the last calculated point of the reverse path. It is desirable to calculate the connection path.

At this time, in order to simplify the calculation, the calculation of the connection path, which is the section between the last calculated status value s _f of the forward path and the last calculated status value s _-b of the reverse path, is the same as the following equation by making the robot input values the same. It can be characterized by doing.

(Here, f (a, b, c) is a motion model function of the robot that calculates the state value of the robot that is moved to the input value of the b robot by c time steps at the point corresponding to the a state value.)

When the maximum time interval used for calculating the connection path is C,

(c ₁ + ... + c _N ) ≤ C.

9 to 10, the route planning step (S30) of the real-time waypoint route improving method according to an embodiment of the present invention includes a waypoint setting step (S32), a reverse path calculation step (S33), and connection It may include a path verification step (S34) and a forward path calculation step (S35).

The waypoint setting step (S32) sets the state of the robot at the current target waypoint. The current target waypoint is the waypoint to which the forward trajectory is headed.

The waypoint setting step (S32) is to set the state of the robot when the robot passes the current target waypoint, considering the position of the robot, the position of the target waypoint, and the position of the next waypoint, the current target way It is desirable to set the state of the robot when the robot passes the point.

Therefore, the waypoint setting step (S32), the current target waypoint is the waypoint to which the forward path is directed.

는 현재 목표 웨이포인트의 상태값,

는

를 세팅하는 동안 다음 목표 웨이포인트의 상태값, G_w는 현재 목표 웨이포인트의 상태 범위, G_w+1는 다음 목표 웨이포인트의 상태 범위, H(a, b)는 a에서 b까지 이동에 대한 비용함수,

는 로봇의 현재 위치

는 현재 목표 웨이포인트

는 현재 목표 웨이포인트 다음 웨이포인트를 나타낸다.)(here,

Is the current target waypoint status,

The

While setting, the status value of the next target waypoint, G _w is the status range of the current target waypoint, G _{w + 1} is the status range of the next target waypoint, and H (a, b) is for moving from a to b. Cost Function,

Is the current position of the robot

Is the current target waypoint

Indicates the waypoint following the current target waypoint.)

As described above, a state value g _w of a current target waypoint capable of minimizing moving costs may be set.

In the reverse path calculation step S33, the reverse path is calculated.

The reverse path calculation step (S33) plans a path from the current target waypoint toward the current position of the robot. At this time, if the forward path is calculated, the backward path toward the last calculation point of the forward path can be calculated.

In the reverse path calculation step S33, the reverse path may be calculated step by step, or it may be calculated in a predetermined step unit.

In the connection path checking step S34, it is checked whether there is a connection path that connects the robot's current position or the last calculation point of the forward path and the current target waypoint or the last calculation point of the reverse path.

The connection path checking step S34 may be performed whenever the reverse path is calculated by one step, or may be performed whenever the reverse path is calculated by a predetermined number of steps.

At this time, if the connection path does not exist in the connection path checking step (S34), it is repeatedly performed a predetermined number of times from the reverse path calculation step (S33) to the connection path checking step (S34), and the connection path checking step (S34) is performed. If a connection path exists, it may be characterized in that the process returns to the waypoint setting step (S32).

That is, every time the backward path is calculated by one step, it is checked whether a connection path exists, and if there is no connection path, the backward path is first calculated a predetermined number of times.

Planning the reverse path ahead of the forward path allows the robot to pass smoothly as it passes the target waypoint, so if the distance between the waypoints is short or the angle between the waypoints is small, the robot passes the target waypoint and then the next target way This is to reduce the overrun phenomenon that the path turning toward the point becomes larger.

In addition, the calculation of the reverse path first as a predetermined number of times is also to make the robot pass smoothly when passing the target waypoint.

At this time, if it has been repeatedly performed a predetermined number of times from the reverse path calculation step (S33) to the connection path confirmation step (S34), it may be characterized in that the process proceeds to the next step even if the connection path does not exist.

In the forward path calculation step (S35), if no connection path exists in the connection path confirmation step (S34), the forward path is calculated.

The forward path calculation step (S35) plans a path from the current position of the current robot to the target waypoint. At this time, if the reverse path has been calculated, the forward path toward the last calculation point of the reverse path can be calculated.

In the forward path calculation step (S35), the forward path may be calculated step by step, or it may be calculated in a predetermined step unit.

The connection path checking step (S34) of the online two-way path planning method in the time state region according to an embodiment of the present invention sets a forward time index for the connection path and the reverse path if the connection path exists, and the connection path And after confirming that the inevitable collision state occurs in the reverse path, calculate and change the time variable of the forward path, the time variable of the reverse path, and the time variable of the forward path through the next waypoint if the inevitable collision condition does not occur. Can be done with

The forward time index applied to the forward path can know the absolute time (time based on the robot). Therefore, it is possible to predict the collision of dynamic obstacles.

However, the reverse time index applied to the reverse path and the time index applied to the connection path cannot predict the collision of the dynamic obstacle because the absolute time is unknown.

Therefore, if the forward path and the connection path and the reverse path are connected, in order to check the collision of the dynamic obstacle, the time index used in the connection path and the reverse path must be replaced with the forward time index applied to the forward path, thereby connecting You can know the absolute time of the path and the reverse path, and check if there is a predicted collision in the reverse path and the connection path.

If there is no predictive collision in the reverse path and the connection path, the bidirectional path from the current robot position to the current target waypoint is completed.

The robot control step (S40) controls the movement of the robot by calculating a command to control the robot according to the route planned in the path planning step (S30).

In the robot control step (S40), if the reverse path calculation step (S33) and the forward path calculation step (S35) are repeatedly performed a predetermined number of times, a command for controlling the robot according to the bidirectional path may be calculated. Reference)

Alternatively, if the forward path calculation step (S35) is repeatedly performed a predetermined number of times, a command for controlling the robot may be calculated according to the bidirectional path. (See FIG. 9)

Most robot control commands are calculated according to the forward path.

Most robot control commands are calculated according to the forward path.

This is because a real-time bi-directional path is not planned until the calculation of the new bi-directional path is completed before the robot moves for one time step.

The input value of the robot generated in the robot control step (S40) may be calculated as follows.

는 k시간에서의 로봇의 입력값,

는 k 번째 스텝에서 1 스텝 진행 가능한 로봇의 입력영역에 포함되는 로봇의 입력값,

는 로봇의 입력값에 대한 최소 비용 함수,

는 k시간에서의 로봇의 상태값,

는 k+1 시간에서의 로봇의 상태값을 나타낸다.)(here,

Is the input of the robot at k hours,

Is the input value of the robot included in the input area of the robot that can proceed from step k to the first step,

Is the minimum cost function for the input value of the robot,

Is the state value of the robot at k hours,

Indicates the state value of the robot at k + 1 hours.)

The route planning step (S30) of the real-time waypoint route improvement method according to an embodiment of the present invention is an obstacle closest to the local goal in a waypoint pass segment connecting the local goal and the previous waypoint from the current position of the moving object It can be characterized by planning a path to the local goal accessible pass segment, which is the area up to.

The local goal accessible pass segment refers to an area of the waypoint pass segment that connects the local goal and the previous waypoint, which can be directly accessed to the local goal without obstacles and collisions, as shown in the left figure of FIG. If there is no obstacle on the waypoint pass segment that connected the previous waypoint, the local goal and the whole waypoint pass segment that connected the previous waypoint become the local goal accessible pass segment (the purple segment indicated by the arrow),

As shown in the right figure of FIG. 6, when there is an obstacle on the waypoint pass segment connecting the local goal and the previous waypoint, there is no collision with the obstacle after the obstacle among the waypoint pass segment connecting the local goal and the previous waypoint. The area from the point to the local goal becomes the local goal accessible pass segment (the purple segment indicated by the arrow).

That is, since the safest place on the local unknown position space is the waypoint pass segment, the area that can safely access the local goal directly on the waypoint pass is defined as the local goal accessible pass segment.

The route planning step (S30) of the real-time waypoint route improvement method according to an embodiment of the present invention is characterized in that a route is planned by planning an accessible waypoint, which is a temporary waypoint entering a local goal accessible pass segment. can do.

In other words, on the intersection of the waypoint pass and the local unknown position space, the local goal (W _M ) is determined, and the accessible waypoint (W _m , m) is the temporary waypoint before entering the local goal (W _M ). Natural number) should be decided on the local goal accessible pass segment.

This is to avoid collision and to find the optimal route.

That is, the movement of the robot along the waypoint path is guaranteed safety, but there may be a path loss, and in order to shorten the path further, the path is planned for the accessible waypoint determined on the local goal accessible path segment. will be.

In the path planning step (S30) of the real-time waypoint path improvement method according to an embodiment of the present invention, when there is an intersection of the local visible position space and the local goal accessible pass segment, the local visual position space and the local goal accessible pass The point of minimizing a specific cost function within an intersection with a segment may be determined as an accessible waypoint.

The specific cost function refers to a cost function calculated in consideration of time, distance, speed, acceleration, and energy consumption.

In the path planning step (S30) of the real-time waypoint path improvement method according to an embodiment of the present invention, when there is an intersection of the local visible position space and the local goal accessible pass segment, the local visual position space and the local goal accessible pass In the intersection with the segment, it may be characterized by determining the point at which the movement distance from the robot to the local goal is the shortest as an accessible waypoint.

Referring to FIG. 7, in the picture on the left, W ₁ indicated by an arrow may be an accessible waypoint.

If there is an intersection with the local visible position space and the local goal accessible pass segment, you can find a safe route by determining only one waypoint. Therefore, in order to take an accessible way point so that it is as safe and short as possible, the area where the distance to the local goal is the shortest among the areas corresponding to the intersection of the local visible position space and the local goal accessible pass segment is accessed. It is desirable to decide by waypoints.

The route planning step (S30) of the real-time waypoint route improvement method according to an embodiment of the present invention, when there is no intersection between the local visible position space and the local goal accessible pass segment, includes an obstacle on the route between the local waypoints. To avoid collisions, a local waypoint, which is a temporary waypoint, is planned toward a direction in which there is no obstacle on the localvisible position space, and an accessible waypoint is planned at a point after passing the obstacle on the localvisible position space, so that the local way It can be characterized by planning a route through points and accessible waypoints.

Referring to FIG. 7, in the picture on the right, W ₁ indicated by an arrow may be a local waypoint, and W ₂ may be an accessible waypoint.

If there is no intersection with the local visible position space and the local goal accessible pass segment, the path is determined by determining a local waypoint in the direction toward the local visible position space and an accessible waypoint on the local goal accessible pass segment. Can plan.

The route planning step (S30) of the real-time waypoint route improvement method according to an embodiment of the present invention includes a local waypoint and an accessible waypoint when there is no intersection between the local visible position space and the local goal accessible pass segment. It can be characterized by setting the local way point and the accessible way point with the smallest cost (distance, etc.).

At this time, the safest area is the local non-visible position space, the next safe area is the local goal accessible pass segment, and the next safe area is the local unknown position space.

Assuming that the target to be considered as a distance is a distance, the distance traveled within the local non-visible position space area and the distance traveled within the local goal access pass segment area is increased, and the distance passing the local way point and the accessible way point is increased. It is desirable to keep it to a minimum.

11 shows an example of calculating a path in real time according to the movement of the robot, and shows that the path changes as shown in the right side as time passes in the figure on the left.

The yellow radius of FIG. 11 shows the sensing range according to the movement of the robot.

Therefore, if the updating period of the sensing data and the path calculation period are shortened, it can be seen that the vehicle is approaching an obstacle and avoiding the obstacle, as shown in the green dotted line in FIG. 12.

The black dotted line in FIG. 12 shows the waypoint pass.

At this time, in order to plan the path more challengingly, in the case of determining the next waypoint of the last waypoint pass segment that can be checked on the local visible position space as the local goal,

As shown in FIG. 13, as a result of the simulation in the place where the door is opened and closed, it was confirmed that even if a path for returning the door is planned, it can proceed with a shorter path through the door when the door is opened.

Even if the return route is planned, if the updating of the sensing data and the calculation of the route are not added, it is confirmed that even if there is a fast road, as shown in the upper left two figures of FIG.

When the updating period of the sensing data and the path calculation period were set to 15 seconds, it was confirmed that the path was shorter than before, as shown in the two upper right figures in FIG. 14.

When the updating period of the sensing data and the path calculation period were 7.5 seconds, it was confirmed that the path was shorter than when the period was 15 seconds as shown in the two lower left corners of FIG. 14,

When the update period of the sensing data and the path calculation period were 0.5 seconds, it was confirmed that the path is not a planned path but a fast path that can be shortened as shown in the lower right figure of FIG. 14.

The blue dots shown in Fig. 14 indicate the movement path of the robot.

15 shows the results of autonomous driving simulation according to a general route plan to which the present invention is not applied, and FIG. 16 shows the results of autonomous driving simulation according to a route plan to which a real-time waypoint route improvement method according to an embodiment of the present invention is applied It is showing.

In the path planning step (S30) of the real-time waypoint path improvement method according to an embodiment of the present invention, when there is no intersection between the local visible position space and the local goal accessible path segment, the angle formed with the current direction of the robot is minimal. It may be characterized by setting a local waypoint to be.

This is because the direction change of the robot is minimized, and the larger the direction change is made by the robot, the more it can become a returning path.

In the above, the distance and angle are described as examples of items that can be considered as cost, but the present invention is not limited to this, and the local waypoint is set so that the cost is the minimum route in consideration of the speed and time of the robot. Of course, various implementations are possible, such as setting a local waypoint so that the cost is the minimum route in consideration of various items.

In the above, the online bidirectional route planning method in the time state domain according to an embodiment of the present invention has been described, but the computer readable recording medium and time state in which a program for implementing the online bidirectional route planning method in the time state domain is stored. Needless to say, programs stored in a computer-readable recording medium for implementing the online two-way path planning method in the area can be implemented.

That is, those skilled in the art can easily understand that the above-described online bidirectional path planning method in the time state domain may be provided by being included in a computer-readable recording medium by tangibly implementing a program of instructions for realizing it. There will be. In other words, implemented in the form of program instructions that can be executed through various computer means, it can be recorded on a computer-readable recording medium. The computer-readable recording medium may include program instructions, data files, data structures, or the like alone or in combination. The program instructions recorded on the computer-readable recording medium may be specially designed and configured for the present invention or may be known and available to those skilled in computer software. Examples of the computer readable recording medium include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs, DVDs, and floptical disks. Included are hardware devices specially configured to store and execute program instructions such as magneto-optical media, and ROM, RAM, flash memory, USB memory, and the like. Examples of program instructions include high-level language code that can be executed by a computer using an interpreter, etc., as well as machine language codes produced by a compiler. The hardware device may be configured to operate as one or more software modules to perform the operation of the present invention, and vice versa.

The present invention is not limited to the above-described embodiments, and the scope of application is various, of course, and various modifications can be implemented without departing from the gist of the present invention as claimed in the claims.

S10: Waypoint pass decision step
S20: Local goal determination step
S30: Route planning phase
S32: Waypoint setting step S33: Reverse path calculation step
S34: Connection path check step S35: Forward path calculation step
S40: robot control step

Claims (10)

In the real-time waypoint path improvement method consisting of a program executed by a calculation processing means including a computer,
A waypoint path determining step (S10) in which the calculation processing means determines a waypoint path that is a set of waypoint path segments forming an optimal path to a target point among waypoint path segments that are a path connecting the waypoint and the waypoint (S10);
The waypoint pass determination step included in the local non-visible position space area, which is a set of areas that do not collide with obstacles when the calculation processing means moves straight from the robot among the movable areas of the robot detected by the sensor of the robot (S10) Among the waypoint passes determined in), the next waypoint of the last waypoint included in the local nonvisible position space or the next waypoint of the last waypoint pass segment included in the local nonvisible position space is determined as a local goal that is the target waypoint , Local goal determination step (S20) of determining a local goal outside the local non-visible position space area;
When the calculation processing means plans a path from the robot to a waypoint pass segment connecting the local goal and the previous waypoint, or when there is an obstacle on the waypoint pass segment connecting the local goal and the previous waypoint. A path planning step of planning a path to a local goal accessible pass segment, which is an area from the local goal to the closest obstacle among the way point pass segments connecting the local goal and the previous way point (S30); And
A robot control step (S40) of controlling the movement of the robot by calculating a command for controlling the robot according to the path planned in the path planning step (S30);
Real-time waypoint path improvement method comprising a.
delete According to claim 1,
The route planning step (S30)
A method for improving a real-time waypoint path, comprising planning a route by planning an accessible waypoint, which is a temporary waypoint entering the local goal accessible pass segment.
According to claim 3,
The route planning step (S30)
If there is an intersection between the local visible position space and the local goal accessible pass segment,
A method for improving a real-time waypoint path, characterized in that a point that minimizes a specific cost function is determined as an accessible waypoint within the intersection of the local invisible position space and the local goal accessible pass segment.
According to claim 3,
The route planning step (S30)
If there is an intersection between the local visible position space and the local goal accessible pass segment,
A method for improving a real-time waypoint path, characterized in that a point where the distance from the robot to the local goal is the shortest is determined as an accessible waypoint within the intersection of the local invisible position space and the local goal accessible pass segment.

According to claim 3,
The route planning step (S30)
If there is no intersection between the local visible position space and the local goal accessible pass segment,
To avoid collision with obstacles on the path between local waypoints, plan a local waypoint, which is a temporary waypoint, toward the direction in which there is no obstacle on the local visible position space, and access to a point after passing the obstacle on the local visible position space A method of improving a real-time waypoint path, comprising planning a waypoint and planning a route through the local waypoint and the accessible waypoint.
The method of claim 6,
The route planning step (S30)
If there is no intersection between the local visible position space and the local goal accessible pass segment,
A method for improving a real-time waypoint path, characterized in that a local waypoint and an accessible waypoint are set to have the smallest cost.
The method of claim 6,
The route planning step (S30)
If there is no intersection between the local visible position space and the local goal accessible pass segment,
A method for improving a real-time waypoint path, characterized by setting a local waypoint that minimizes an angle formed with a current robot's traveling direction.
A computer-readable recording medium storing a program for implementing the real-time waypoint path improvement method according to any one of claims 1 and 3 to 8.
A program stored in a computer-readable recording medium for implementing the real-time waypoint path improvement method according to any one of claims 1 and 3 to 8.

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