WO2020222408A1 - Real-time waypoint path improvement method, recording medium in which program for implementing same is stored, and computer program stored in medium in order to implement same - Google Patents

Abstract

The present invention relates to a real-time waypoint path improvement method, a recording medium in which a program for implementing same is stored, and a computer program stored in a medium in order to implement same and, more specifically, to: a real-time waypoint path improvement method, which determines, as a target waypoint, a robot traveling direction side waypoint that is outside a sensor detection region of a robot, so that the robot can autonomously travel by more actively finding a more optimized path; a recording medium in which a program for implementing same is stored; and a computer program stored in a medium in order to implement same.

Description

Real-time waypoint route improvement method, a recording medium in which a program to implement it is stored, and a computer program stored in the medium to implement it

The present invention relates to a real-time waypoint path improvement method, a recording medium in which a program for implementing the same is stored, and a computer program stored in the medium to implement the same, and more particularly, to a waypoint in the direction of the robot outside the sensor sensing area of the robot. Is determined as a target waypoint, and a method for improving a real-time waypoint route that enables autonomous driving by finding a more challenging route for the robot to be more challenging, a recording medium in which a program for implementing this is stored, and a computer program stored in the medium to implement the same. About.

Autonomous robots refer to robots that search their surroundings and detect obstacles and use wheels or legs to find the optimal route to their destination.

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

In general, the safe path is more important for robotic applications, but the most ideal path will be the safest and the shortest possible path.

In general, as a method of securing the safety route, an obstacle detection sensor (a device that can measure the distance to surrounding obstacles such as laser, ultrasonic, etc.) is used to find the direction with the most empty space and to find the direction of the destination. Considering the method of determining the moving direction of the robot has been mainly used. The weight of the direction toward the empty space and the direction toward the destination is determined experimentally. If a lot of weight is given to the empty space, the possibility of collision with an obstacle can be minimized, but in extreme cases, the destination may not be reached. Conversely, if you give a lot of weight to the destination, safety is poor.

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

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

First, global route planning is to search for the optimal route from the starting point to the target point using a given environment map,

Local route planning means creating an actual movement trajectory to avoid a dynamic obstacle recognized by a sensor.

In addition, the location recognition technology is a positioning technology that finds out the current position of a robot on a map while 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)", "Dijkstra algorithm".

More specifically, the Dijkstra algorithm described above has been suggested as the first route plan and has been widely used in various fields until now, but has a disadvantage in that it requires a lot of computation time since 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 is a complementary form of the Dijkstra algorithm, and by adding an appropriate evaluation function, it is possible to achieve a faster search time than the Dijkstra algorithm based on the depth search and the area search.

In the regional path planning, the local path planner follows the path created by the global path plan given to the robot based on the current state of the robot and the local environment information, so that the path can be moved to avoid collisions caused by non-environmental obstacles. It's a planning skill.

Regional route planning is an essential technology for a mobile robot to move to a destination without obstacles and collisions in a daily space where environmental changes inevitably occur, and unlike the global route planning, real-time is required.

As an example of the prior art for the local route planning method as described above, there is an incremental (gradual) planning method in which a route is planned by assigning a waypoint through which a robot must pass.

The above incremental (progressive) planning method can be calculated quickly, but if the distance between waypoints is short or the angle between waypoints is small, the path to the next target waypoint after the robot passes the target waypoint increases. There is a problem with this occurring.

Korean Patent Registration [10-1079197] discloses a route tracking method for an autonomous driving device.

Accordingly, the present invention was conceived to solve the above-described problems, and an object of the present invention is to determine a waypoint on the side of the robot traveling direction outside the sensor sensing area of the robot as a target waypoint, and if an overrun phenomenon does not occur In this regard, it provides a real-time waypoint route improvement method that enables autonomous driving by finding a more challenging and more optimized route, a recording medium storing a program for implementing it, and a computer program stored in the medium to implement the method.

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

A method for improving a real-time waypoint path according to an embodiment of the present invention for achieving the above object, in a method for improving a real-time waypoint path in the form of a program executed by an arithmetic processing means including a computer, A waypoint path determination step (S10) of determining, by the processing means, 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 paths connecting the waypoint and the waypoint; In the waypoint path determination step (S10), among the local visible position space, which is a set of areas in which the arithmetic processing means does not have obstacles and collisions when moving straight from the robot among the movable areas detected by the robot sensors. A local goal determination step (S20) of determining the next waypoint of the last waypoint on the determined waypoint path or the next waypoint of the waypoint path segment as a target waypoint, a local goal; A path planning step (S30) in which the operation processing means plans a path from the robot to the local bone determined in the local bone determination step (S20); 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).

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

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

In addition, in the path planning step (S30), when there is an intersection between 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 way point. It is characterized in that it is determined by.

In addition, in the path planning step (S30), when there is an obstacle on the local bone accessible path segment, a temporary way to the direction side where there is no obstacle on the local visible position space so that there is no collision with the obstacle on the path between the local waypoints. It is characterized by planning a local waypoint, which is a point, and 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. To do.

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

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

In addition, according to an embodiment of the present invention, a computer-readable recording medium in which a program for implementing the real-time waypoint path improvement method is stored 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 for 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 the medium to implement the same, the waypoint on the side of the robot traveling direction outside the sensor sensing area of the robot By determining as the target waypoint, the robot can more challengingly find a more optimized path to enable autonomous driving, thereby minimizing the occurrence of overrun of the robot and at the same time reducing the amount of computation, thus enabling real-time path calculation.

In addition, by planning a bidirectional path using a forward path and a reverse path, there is an effect of minimizing the occurrence of overrun or vibration of the robot and enabling real-time path calculation.

In addition, by applying a cost minimization algorithm based on function decomposition that reduces the amount of computation by decomposing the cost function required for movement, there is an effect of further reducing the amount of computation required for path calculation.

Also, time coordination safety area

By calculating the path based on, there is an effect of further reducing the amount of computation required for calculating the path.

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, but by performing the reverse path calculation step before the forward path calculation step, the path is made to pass the target waypoint more smoothly. There is an effect that can be formed.

In addition, by using a connection path connecting the forward path and the reverse path, there is an effect of being able to connect the forward path and the reverse path so as not to put an excessive force on the robot.

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

In addition, if there is a connection path, the forward time index for the connection path and the reverse path is set, and collision with dynamic obstacles of the connection path and the reverse path that were not considered in the calculation can be checked, thereby enabling faster and more stable path creation. have.

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

In addition, by planning the route by planning the accessible waypoints 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 the accessible waypoint, there is an effect that it is possible to reduce the amount of computation and to quickly plan an optimized route to enter the local goal accessible path segment.

In addition, by planning the route based on the local waypoint and the accessible waypoint when there is an obstacle, it is possible to plan an optimized route that quickly enters the local goal accessible path segment while reducing the amount of computation even if an obstacle appears. It can have an effect.

In addition, when there is an obstacle, there is an effect of minimizing risk factors by setting the local waypoint and the accessible waypoint with the smallest cost of the local waypoint and the accessible waypoint.

In addition, when there is an obstacle, it is possible to minimize overrun by setting a local waypoint at which the angle between the current robot's moving direction and the minimum angle is set.

1 is a flowchart of a method for improving a real-time waypoint route according to an embodiment of the present invention.

2 is an exemplary view for explaining a local workspace applied to a method for improving a real-time waypoint route according to an embodiment of the present invention.

3 is an exemplary diagram for explaining a local position space applied to a method for improving a real-time waypoint route according to an embodiment of the present invention.

4 is an exemplary view illustrating a local visible position space applied to a method for improving a real-time waypoint path 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 method for improving a real-time waypoint path according to an embodiment of the present invention.

6 is an exemplary view for explaining a local goal accessible pass segment applied to a method for improving a real-time waypoint path 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 route according to an embodiment of the present invention.

8 is a flowchart of an online bidirectional route planning method in a time state domain according to an embodiment of the present invention.

9 is a flowchart of an online bidirectional route planning method 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 moves as a result of the path calculation 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 sensing data and a path calculation period.

15 is an exemplary view showing 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 an autonomous driving simulation result according to a route plan to which a real-time waypoint route improvement method according to an embodiment of the present invention is applied.

*Detailed explanation of the main symbols in the drawings*

S10: Waypoint path determination step

S20: Local goal determination step

S30: Route planning stage

S32: Waypoint setting step S33: Reverse route calculation step

S34: connection path check step S35: forward path calculation step

S40: Robot control step

Since the present invention can make various changes and have various embodiments, 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 changes, equivalents, and substitutes included in the spirit and scope of the present invention.

When a component is referred to as being "connected" or "connected" to another component, it is understood that it may be directly connected or connected to the other component, but other components may exist in the middle. Should be.

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

The terms used in the present 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 the present application, terms such as "comprise" or "have" are intended to designate the presence of features, numbers, processes, operations, components, parts, or combinations thereof described in the specification, but one or more other features. It is to be understood that the possibility of addition or presence of elements or numbers, processes, operations, components, parts, or combinations thereof is not preliminarily excluded.

Unless otherwise defined, all terms including technical or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs. Terms such as those defined in a commonly used dictionary should be interpreted as having a meaning consistent with the meaning of the related technology, and should not be interpreted as an ideal or excessively formal meaning unless explicitly defined in this application. Does not.

Hereinafter, the present invention will be described in more detail with reference to the accompanying drawings. Prior to this, terms or words used in the specification and claims should not be construed as being limited to their usual or dictionary meanings, and the inventors appropriately explain the concept of terms in order to explain their own invention in the best way. Based on the principle that it can be defined, it should be interpreted as a meaning and concept consistent with the technical idea of the present invention. In addition, unless there are other definitions in the technical terms and scientific terms used, they have the meanings commonly understood by those of ordinary skill in the art to which this invention belongs, and the gist of the present invention in the following description and accompanying drawings Description of known functions and configurations that may be unnecessarily obscure will be 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 elements. It should be noted that the same elements in the drawings are indicated by the same reference numerals wherever possible.

1 is a flowchart of a method for improving a real-time waypoint route according to an embodiment of the present invention, and FIG. 2 is an exemplary view for explaining a local workspace applied to a method for improving a real-time waypoint route 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 route improvement method according to an embodiment of the present invention, and FIG. 4 is a real-time waypoint route improvement method according to an embodiment of the present invention. An exemplary diagram for explaining a local visible position space applied to, and FIG. 5 is an exemplary diagram for explaining a process of determining a local goal in a local goal determination step of a method for improving a real-time waypoint path according to an embodiment of the present invention 6 is an exemplary view for explaining a local bone accessible pass segment applied to a method for improving a real-time waypoint path according to an embodiment of the present invention, and FIG. 7 is a real-time waypoint according to an embodiment of the present invention. An exemplary diagram for explaining a local waypoint and an accessible waypoint applied to a route improvement method, and FIG. 8 is a flowchart of an online two-way 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, and FIG. 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 the movement of a robot to which the real-time waypoint route improvement method of the present invention is applied, and FIG. 12 is an exemplary view showing the route traveled by the robot as a result of calculating the route of FIG. , FIG. 13 is an exemplary view showing a simulation result in a place where a door is opened and closed, FIG. 14 is an exemplary view showing a simulation result that changes according to an update of sensing data and a path calculation period, and FIG. 15 is a view to which the present invention is not applied. Fig. 16 is an exemplary diagram showing the result of an autonomous driving simulation according to a general route plan, and FIG. 16 is a real-time waypoint view This is an example diagram showing the results of the autonomous driving simulation according to the route plan to which the route improvement method was applied.

As shown in FIG. 1, a method for improving a real-time waypoint path according to an embodiment of the present invention includes a method for improving a real-time waypoint path in the form of a program executed by an operation processing means including a computer. 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.

Waypoints refer to points that the robot can designate as a target for driving, and are indicated as dots in the drawing. Targeting can pass through a waypoint, but you can also pass nearby without passing through.

The waypoint pass segment refers to a path connecting the waypoint and the waypoint. In general, the waypoint pass segment may be a line segment connecting a point (waypoint) and a point (waypoint), but may also be a curved line. In addition, the waypoint path segment is a relatively safe path that does not collide with obstacles on the map. In other words, it is a path that does not collide with a fixed obstacle displayed on the map.

The waypoint path refers to a set of waypoint path 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 line segments.

Local workspace refers to a workspace (area) based on sensor data (see Fig. 2).

The local workspace includes the local free workspace, the local opstarkle workspace, and the local unknow workspace.

Local Free Workspace

Denotes a workspace without obstacles detected by the sensor, and corresponds to the area indicated by the orange side of FIG. 2.

Local Opstarkle Workspace

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

Local Unknow Workspace

Denotes a workspace where collision with an obstacle cannot be predicted because it is an area not detected by a sensor, and corresponds to an area without color in FIG. 2.

The local position space refers to a workspace (area) based on sensor data in consideration of collisions according to the shape of the robot (see Fig. 3).

The local position space includes a local obscuring position space, a local preposition space, a local unknown position space, and a local visible position space.

Local Obstacle Position Space

Is the obstacle area detected by the sensor

It refers to a workspace that collides with and corresponds to the orange area of FIG. 3.

Local preposition space

Denotes a workspace that does not collide with an obstacle detected by the sensor, and corresponds to the light blue area of FIG. 3.

Local Unknown Position Space

Denotes a workspace where collision cannot be predicted because it is not detected by a sensor, and corresponds to an area without color in FIG. 3.

Local Visible Position Space

Denotes a set of areas (spaces) without obstacles and collisions when the robot moves straight from the current position of the robot, and corresponds to the red area of FIG. 4.

Local Goal Accessible Pass Segment

Denotes an area in which the local goal can be accessed directly 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 of FIG. 6.

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

In the waypoint path determination step (S10), the calculation processing means determines a waypoint path, which is a set of waypoint path segments that form an optimal path to a target point among waypoint path segments that connect the waypoint and the waypoint. .

The waypoint path determination step (S10) determines an optimal path to which waypoints are connected based on the previously input map information.

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

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

The local bone determination step (S20) is a local visible position space that is a set of areas without obstacles and collisions (on the map) when the operation processing means moves straight from the robot among the movable areas of the robot detected by the sensor of the robot. Among them, the next waypoint of the last waypoint on the waypoint path determined in the waypoint path determination step S10 or the next waypoint of the waypoint path segment is determined as a target waypoint, a local goal.

The local visible position space is narrower than the local preposition space.

This is because collisions according to the shape of the robot are considered.

To make this easier to understand, in FIG. 4, the local visible position space is displayed in red and the local pre-position space is displayed in light blue.

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

Since it is clear that a collision will occur in the local opstarkle position space, if you consider to find an optimized path in consideration of the local unknown position space,

While included in the local unknown position space, on the waypoint path where the global pass was created (i.e., the intersection of the local unknown position space and the waypoint path), there is no collision with obstacles on the map, but obstacles (people, etc.) that were not on the map ) And there may be a conflict.

Therefore, in the order of low risk among local position spaces, local visible position space, local preposition space, local unknow position space, and local opstarkle position space are in order.

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

When the robot (calculation 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 the path based on the detected information.

In other words, when the robot falls on the waypoint path 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 the basic waypoint path and a path (pass) to go toward the local goal is planned.

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

Local Visible Position Space is meaningful because it is safe to go straight on the Local Visible Position Space when trying to improve a previously planned route (refining the route), so it is good to take the first waypoint.

In the local goal determination step (S20), the waypoint at the rear of the waypoints that can safely go straight may be determined as a local goal, but it is not challenging and is not considered, and a waypoint is placed on an invisible area, that is, a local unknown position space I thought about taking pictures,

In order to plan the route more challengingly, the last waypoint that can be checked on the local visible position space or the next waypoint of the waypoint pass segment is determined as a local goal.

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

Among the local visible position spaces, the next waypoint of the last waypoint on the waypoint path or the next waypoint of the waypoint path segment is determined as a target waypoint, a local goal.

An example in which the local goal determination step S20 determines the next waypoint of the last waypoint corresponding to the intersection of the local visible position space and the waypoint path as a local goal.

In both the left and right diagrams of FIG. 5, since the last waypoint on the local visible position space is a point indicated by a blue dot, the next waypoint, a point indicated by a green dot, may be determined as the 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 checked (visible) may be determined as the local goal.

In the path planning step S30, the operation processing means plans a path from the position of the robot to the local bone determined in the local bone determination step S20.

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

The'time state domain' refers to a set of state values considered for time.

The'status value' refers to a value that includes a coordinate value for a position and azimuth (heading angle, etc.) and includes any one or more information selected from among steering angle, speed (linear velocity, angular velocity), and acceleration. For example, it may be (x-coordinate, y-coordinate, bearing, velocity, acceleration).

'Time coordination region' refers to a set of coordination values considered for time.

'Coordination value' refers to a value including a position and azimuth (heading angle, etc.). For example, it may be (x coordinate, y coordinate, direction).

For explanation, various variables and terms have been designated and described, and the variables and terms are as follows.

The status value of the robot's current position is marked as's _k ',

The state value calculated by f step from the current position of the robot in the forward path is expressed as's _f '.

When the robot arrives at the current target waypoint, the status value of the robot is marked as'g _w '.

The status value calculated by step b from the current target waypoint to the reverse path is expressed as's _-b ',

The time index indicating how many steps are calculated is indicated as'n',

The time period given to calculate one step is marked as'T',

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

The robot's status value is expressed as's' (lowercase s),

The status area, which is a set of robot status values, is marked with'S' (capital S),

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

The time-state domain is denoted as'S _n '(s _n ∈ S _n ),

The coordination value of the robot is written as'q' (lowercase q),

The coordination area, which is a set of coordination values of the robot, is marked with'Q' (capital Q),

The time coordination value, which is the coordination value at time step n, is expressed as q _n (lowercase q),

The temporal coordination domain is denoted as'Q _n '(q _n ∈ Q _n ),

The coordination area that collides with a static obstacle is'

', and

The extra static coordination area that does not collide with a static obstacle

(

= Q-

),

At time index n, the coordination region colliding with the dynamic obstacle is

Written as,

The free dynamic coordination area that does not collide with static and dynamic obstacles is'

'(

=

-

),

The state in which collision cannot be avoided was called'inevitable collision state'.

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

Hereinafter, in the description, it is assumed that the robot is moved from the current state point corresponding to the state value s _k to the 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 two-way route that gradually plans (calculates) the forward route and the reverse route, and is used to plan the forward route and the reverse route. The state value (s _n (n is a time index, a natural number)) in the time state area is selected from the steering angle, speed (linear speed, angular speed), and acceleration including the coordinate values for the position and azimuth (heading angle). It may be characterized by including any one or a plurality of information.

Forward path calculation calculates the forward path in the time state domain considering the state value (s) and the time value 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 reverse path. .

Here, the time value is a value that can check the time from the current time to the future, and can be used only as a time index (n), but the calculation period for calculating one step is multiplied by the time index (n) (calculation period * Of course, various methods can be used if the actual progress time can be checked, such as the time index (n)) available.

The forward path calculation may calculate a forward path in a time state domain from the current position of the initial robot toward the current target waypoint. After that, if the forward path and the reverse path are calculated, the forward path in the time state domain from the last calculation point of the forward path to the last calculation point of the reverse path can be calculated.

In the reverse path calculation, the reverse path in the time state domain from the current target waypoint or the last calculation point of the reverse path to the current position of the robot or the last calculation point of the forward path is calculated.

In the calculation of the reverse path, a reverse path in a time state region from an initial current target waypoint to a current position of the robot may be calculated. After that, if the forward path and the reverse path are calculated, the forward path in the time state domain from the last calculation point of the reverse path to 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 is to make the robot pass more smoothly when it passes the target waypoint, so that if the distance between waypoints is short or the angle between waypoints is small, the robot passes the target waypoint and then moves toward the next target waypoint. This is to reduce the overrun phenomenon in which the turning path becomes larger.

Forward path calculation and reverse 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 to the decomposed cost function (H ^r ) (r (r is a natural number)),

By applying an approximate optimal state time function (N) that calculates a path so that the sum of the decomposed cost functions (H ^r ) is minimized according to a predetermined criterion (which can be set by a value of r* or determined by calculation) ,

Based on the given state value (s _n ) at the step n (n is a natural number), the state value at the target point (s ^to ), and the number of steps to be calculated (m), the state value (s _n ) is at a given point. It may be characterized by obtaining a state value (s _n+m ) at a step that will proceed as much as the number of steps (m) to be calculated toward the target point (s ^to ).

When planning a state value s _{n + m} with a given state value s _n , we try to minimize the cost of moving 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 reverse 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, m is the number of steps to be calculated)

If the cost minimization function H is used, there is a problem that it is difficult to apply it to real-time route calculation due to a large amount of time required for calculation.

The robot's input value u _n can be obtained as follows.

here

Is a cost function that returns a cost value that occurs when the transition to s _{n + m} to the given mobile cost function by using the input u which the status of the robot in a s _n on the input for the least effort.

therefore,

U _n that minimizes is an 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 state limited to the input area of the usable robot, there is always an available input.

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

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

Where H ^r is arranged in the dominant order. In other words, H r with smaller ^r is more dominant. When minimizing one by one from H ¹ to H ^R , the usable safe area becomes smaller, and the decomposed cost function (H ^r ) is minimized while increasing the r value until one state value is finally derived.

Time-state safety zone minimizing the r part from H ¹ to H ^r

Can be defined as:

Time-state safe area calculated as an unresolved movement cost function

Is the time state safety zone

Is the same as

The r value from which one state value is derived

It is called

Continue minimizing the decomposed movement cost function until is found. Here, the usable time-state safety area is reduced to a single state as follows.

here,

Although the temporal state value obtained from 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 calculations are efficient.

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

(Here, s _{n +m} is the state value at n+m steps, N is the approximate optimal time state value calculation function, s _n is the given state value at n steps, s ^to is the state value at the target point, m is Number of steps you want to calculate)

You can calculate the forward path and the reverse path.

Adding description, approximation using the optimal time function (N), the robot state value of s _n is the current state in order to transition to the state of s ^to, n after the optimum status of the m-th time index s _It is calculated as _n+m .

An example of a robot capable of omnidirectional movement,

The approximate optimal state time function (N) is the following equation

(Where H ¹ is the speed-reading independent movement cost function, H ² is the angle-travel direction partial cost function, and H ³ is the velocity magnitude partial movement cost function)

It can be calculated using

In order to induce the robot to move rapidly toward the target and pass the target point effectively, the movement cost function H(s ^from , s ^to ) can be composed of three parts, and D is a predefined threshold distance.

Since each position in the reachable area is matched 1:1 with the allowable velocity area, each time state is determined based on the velocity independent movement cost function H ¹ .

The direction of movement partial movement cost function H ² and the velocity magnitude partial movement 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 current target waypoint state.

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

The direction is determined as the average direction of the two vectors 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.

Also, the size of the speed at the target waypoint is determined according to the inner angle of the path at the target waypoint.

The larger the angle, the faster the speed or vice versa.

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

For example of a robot capable of moving with a wheel, the approximate optimal state time function (N) is

Next expression

(Where H ¹ is the speed-reading independent movement cost function, H ² is the angle-travel direction partial cost function, and H ³ is the velocity magnitude partial movement cost function)

It 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 .

In order to consider both the position close to the target position and the direction toward the target position, H ¹ consists of two parts with a predefined weight α.

The movement direction partial movement cost function H ² and the velocity magnitude partial movement 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 omnidirectional movement.

The forward route calculation of the online bidirectional route planning method in the time state domain according to an embodiment of the present invention is a time coordination safety area, which is a safe area on the coordination area that does not collide with static and dynamic obstacles.

If is present, the coordination value of the robot at the time index (n) (

) And time index (n)

It characterized in that it calculates the forward path based on,

In the calculation of the reverse path, if there is a safety area, the coordinate value of the robot at the time index (n) (

) And time coordination safety zone

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

A set of coordination values colliding with static environmental obstacles such as walls

And'environmental obstacle area' (

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

The difference minus is the'free static coordination area'

It can be called this.

The robot can predict the path of each dynamic obstacle such as humans and other robots based on a tracking algorithm using sensor data.

Therefore, at time index n, a set of coordination values that are expected to collide with a dynamic obstacle such as a person

And'dynamic obstacle area' (

⊂ Q), the marginal static coordination area

Dynamic obstacle area

The difference minus is the'free dynamic coordination area'

It can be called this.

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.

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

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

Since the robot's state area S and the robot's input area U are bounded, the robot motion is constrained and this is called motion constraint.

Environmental detectors (sensors) detect and track obstacles based on a sensor system using sensors such as vision sensors or laser range finders (LRF), and classify them into static obstacles (environmental obstacles) and dynamic obstacles (non-environmental obstacles).

And

Can be obtained.

The global route planner, which plans routes by assigning waypoints,

Plan a series of waypoints and goals inside.

Every moment t = kT, k = 0, 1,... The algorithm plans the path using the reference input k.

Through this input, the robot sequentially passes through the waypoints and

And

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

When the time state value s _n is given, the reachable time state region at the time index n + m can be defined, and at this time, the reachable time coordination region 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 may be difficult to accurately calculate and use a time state region or a time coordination region that can be reached.

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

The reachable time state area accessible by the uniform input value of the robot can be defined as follows.

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

In this case, an area approximating the time coordination area reachable by the uniform input value of the robot may be defined as follows.

and

Is useful for real-time route planning because it does not cover the entire actual reachable area, but has a very small computational load compared to calculating the entire actual reachable area.

Given a state value s _n , a safe time-state safe area at time index n + m

Can be defined as:

In addition, given the state value s _n , the safe time coordination safe area at time index n + m

Can be defined as:

silver

Planned within

It is enough to check the expected collision and check if there is a safe area available.

The time-coordination safety zone of the reverse route from the current target waypoint

When calculating,

silver

Is replaced by

this is,

This is because the data of the dynamic obstacle cannot be confirmed because the actual time for is not known.

As shown in Figure 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 path calculation step (S33), and a forward path calculation 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 the forward trajectory is heading to pass.

The waypoint setting step (S32) is to set the state of the robot when the robot passes the current target waypoint, and considers the position of the robot, the position of the target waypoint, and the position of the next waypoint. It is desirable to set the robot's state 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 headed, and a state value (g _w ) of the current target waypoint that can minimize the moving cost as shown in the following equation is set. can do.

(here,

Is the current target waypoint status value,

Is

During setting, G _w is the state range of the current target waypoint, G _{w + 1} is the state range of the next target waypoint, and H(a, b) is for movement 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.)

The reverse path calculation step (S33) calculates the reverse path.

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

The variable B indicated in the conditional sentence of FIG. 8 is the maximum value of the time step for the reverse route planning, and the variable b means the time step of the reverse route plan (increases by 1 when repetitive calculations are made), but the variable and conditions for it are arbitrarily set Of course, it can be used in various ways. (The same applies to the variables of FIGS. 9 to 10)

In the forward path calculation step S50, the forward path is calculated.

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

The variable F indicated in the conditional sentence 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 plan (increases by 1 when calculating iteratively), and k is the current time step. It goes without saying that the conditions for can be arbitrarily set and used in various ways. (The same applies to the variables of 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 first performing the reverse path calculation step (S33) allows the path to pass more smoothly through the target waypoint. Can be formed, it is preferable to first perform the reverse path calculation step (S33).

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

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

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

If the two-way route is continuously planned, the forward route and the reverse route may meet, but the speed may be different.

Therefore, in order to overcome the difference in speed 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 calculation 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 calculation point of the reverse path. It is desirable to calculate the connection path so that it is possible.

At this time, in order to simplify the calculation, the calculation of the connection path, which is the section between the last calculated state value s _f of the forward path and the last calculated state value s _-b of the reverse path, equalizes the input value of the robot and is calculated as follows: It can be characterized by that.

(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 robot b by c time steps at the point corresponding to the state value a.)

When C is the maximum time interval used in the calculation of the connection path,

(c ₁ + ... + c _N ) ≤ C.

9 to 10, the path planning step (S30) of the real-time waypoint path improvement method according to an embodiment of the present invention includes a waypoint setting step (S32), a reverse path calculation step (S33), and a connection It may include a path check 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 the forward trajectory is heading to pass.

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

Therefore, in the waypoint setting step (S32), the current target waypoint is the waypoint to which the forward path is directed, and the following equation

(here,

Is the current target waypoint status value,

Is

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

Is the current position of the robot

Is the current target waypoint

Represents the waypoint following the current target waypoint.)

As described above, a state value (g _w ) of a current target waypoint that can minimize moving cost may be set.

The reverse path calculation step (S33) calculates the reverse path.

In the reverse route calculation step (S33), a route is planned from the current target waypoint toward the current position of the robot. At this time, if the forward path is calculated, the reverse 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 may be calculated in a predetermined step unit.

In step S34 of checking the connection path, it is checked whether there is a connection path in which the current position of the robot or the last calculation point of the forward path and the current target waypoint or the last calculation point of the reverse path are connected.

The connection path checking step (S34) may be performed each time the reverse path is calculated by one step, or may be performed each time 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 check step (S34), the reverse path calculation step (S33) to the connection path check step (S34) are repeated a predetermined number of times, and in the connection path check step (S34) If there is a connection path, it may be characterized in that the return to the waypoint setting step (S32).

That is, every time the reverse path is calculated one step, it is checked whether a connection path exists, and if the connection path does not exist, the reverse path is first calculated a predetermined number of times.

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

In addition, calculating the reverse path first by a predetermined number of times is also to make it smoother when the robot passes the target waypoint.

At this time, if the reverse path calculation step (S33) to the connection path check step (S34) is repeatedly performed a predetermined number of times, the next step may be performed even if the connection path does not exist.

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

In the forward path calculation step S35, a path is planned from the current position of the robot toward the target waypoint. At this time, if the reverse route is calculated, the forward route toward the last calculation point of the reverse route can be calculated.

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

In the connection path checking step (S34) of the method for online bidirectional path planning in a time state domain according to an embodiment of the present invention, if a connection path exists, a forward time index for the connection path and the reverse path is set, and the connection path And after checking whether an inevitable collision condition occurs in the reverse path, if the inevitable collision condition does not occur, the time variable of the forward route, the time variable of the reverse route, and the time variable of the forward route passing through the next waypoint are calculated and changed. You can do it.

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

However, since the absolute time cannot be known between the reverse time index applied to the reverse path and the time index applied to the connection path, the collision of a dynamic obstacle cannot be predicted.

Therefore, if the forward path and the link path and the reverse path are connected, the time index used for the link path and the reverse path must be changed to the forward time index applied to the forward path in order to check the collision of dynamic obstacles. You can know the absolute time of the route and the reverse route, and check whether there is a predicted collision in the reverse route and the connection route.

If there is no predicted 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 for controlling the robot according to the path planned in the path planning step (S30).

In the robot control step S40, when 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 may be calculated according to the bidirectional path. Reference)

Alternatively, when 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 two-way route is planned only when a new two-way route is calculated 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.

(here,

Is the input value of the robot at k time,

Is the input value of the robot included in the input area of the robot that can proceed one step at the kth step,

Is the minimum cost function for the robot's input,

Is the robot's state value at k time,

Represents the state value of the robot at k+1 time.)

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

The local goal accessible pass segment refers to an area in which the local goal can be accessed directly to the local goal without collision with obstacles among the waypoint pass segments connecting the local goal and the previous waypoint. If there is no obstacle on the waypoint pass segment connecting the previous waypoint, the entire waypoint pass segment connecting the local goal and the previous waypoint becomes the local goal accessible pass segment (the purple line segment indicated by the arrow),

6, if there is an obstacle on the waypoint pass segment connecting the local goal and the previous waypoint, among the waypoint pass segments connecting the local goal and the previous waypoint, there is no collision with the obstacle after the obstacle. The area from the point to the local goal becomes the local goal accessible pass segment (the purple line segment indicated by the arrow).

That is, since the safest place in the local unknow position space is the waypoint pass segment, the area in which the waypoint path can be directly safely accessed to the local goal is determined 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 the route is planned by planning an accessible waypoint, which is a temporary waypoint to enter the local goal accessible path segment. can do.

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

This is to avoid collision and to find an optimal path at the same time.

In other words, it is safe to move the robot along the waypoint path, but there may be path loss, so in order to shorten the path further, the path is planned toward the accessible waypoint determined on the local goal accessible path segment. will be.

In the path planning step (S30) of the method for improving a real-time waypoint path according to an embodiment of the present invention, when there is an intersection of the local visible position space and the local bone accessible path segment, the local visible position space and the local bone accessible path It may be characterized in that the point at which the specific cost function is minimized within the intersection with the segment is determined as an accessible waypoint.

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

In the path planning step (S30) of the method for improving a real-time waypoint path according to an embodiment of the present invention, when there is an intersection of the local visible position space and the local bone accessible path segment, the local visible position space and the local bone accessible path It may be characterized in that the point where the movement distance from the robot to the local goal is shortest within the intersection with the segment is determined as the accessible waypoint.

Referring to FIG. 7, in the left figure, 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 path segment, you can find a safe route by determining only one waypoint. Therefore, in order to take the accessible waypoint so that the distance is as safe and short as possible, the point 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 advisable to decide with a blowway point.

In the path planning step (S30) of the method for improving a real-time waypoint path 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, To avoid collision, plan a local waypoint, which is a temporary waypoint toward the direction of travel without obstacles on the local visible position space, and plan an accessible waypoint at the point after passing the obstacle on the local visible position space. It can be characterized by planning a route through points and accessible waypoints.

Referring to FIG. 7, in the figure 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, a local waypoint is determined in the direction toward the local visible position space, and an accessible waypoint is determined on the local goal accessible path segment. You can plan.

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

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

Assuming that the object to be considered as cost is distance, the distance moving within the local visible position space area and the distance moving within the local goal accessible pass segment area are increased, while the distance passing through the local waypoint and the accessible waypoint is increased. It is desirable to minimize it.

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

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

Therefore, if the sensing data update and path calculation period is shortened, it can be confirmed that the vehicle is close to the obstacle and travels avoiding the obstacle as shown in the green dotted line in FIG. 12.

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

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

As a result of the simulation at the place where the door opens and closes as shown in FIG. 13, it was confirmed that even if a path around the door is planned, when the door is opened, a shorter path proceeding through the door can be performed.

Even if the return route is planned, if the sensing data is not updated and route calculation is additionally performed, it is confirmed that the return will occur even if there is a fast route as shown in the upper left two pictures of FIG. 14,

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

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

When the sensing data update and the path calculation period were set to 0.5 seconds, as shown in the lower right figure of FIG. 14, it was confirmed that the path was not a planned path but proceeded to a fast path that could shorten the path.

The blue dots shown in FIG. 14 represent the movement path of the robot.

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

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 bone accessible path segment, the current angle formed with the moving direction of the robot is minimal. It may be characterized by setting a local waypoint to be.

This is to minimize the change of direction of the robot, and it is because the path can be made as the robot changes direction larger.

In the above, the distance and angle have been described as examples as items that can be considered as cost, but the present invention is not limited thereto, and the local waypoint is set so that the cost is minimized in consideration of the speed and time of the robot. It is possible, of course, that various implementations are possible, such as setting a local waypoint so that the cost is minimized in consideration of various items among them.

In the above, a method for planning an online bidirectional route in a time state domain according to an embodiment of the present invention has been described above, but a computer-readable recording medium storing a program for implementing the online bidirectional route planning method in a time state domain and a time state It goes without saying that a program stored in a computer-readable recording medium for implementing an online bidirectional route planning method in the domain can also be implemented.

That is, those skilled in the art can easily understand that the above-described online bidirectional route planning method in the time state domain may be provided in a recording medium that can be read through a computer by tangibly implementing a program of instructions for implementing it. There will be. In other words, it is implemented in the form of program instructions that can be executed through various computer means, and can be recorded on a computer-readable recording medium. The computer-readable recording medium may include a program command, a data file, a data structure, or the like alone or in combination. The program instructions recorded on the computer-readable recording medium may be specially designed and constructed for the present invention, or may be known and usable 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 recording media such as CD-ROMs and DVDs, magnetic-optical media such as floptic disks, and ROM, RAM, and flash memory. , A hardware device specially configured to store and execute program commands such as USB memory, and the like. Examples of the program instructions include not only machine language codes such as those produced by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. 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 of course, various modifications can be made without departing from the gist of the present invention as claimed in the claims.

Claims (10)

1. A method for improving a real-time waypoint route in the form of a program executed by an arithmetic processing means including a computer,

   A waypoint path determination step (S10) of determining a waypoint path, which is a set of waypoint path segments forming an optimal path to a target point, among waypoint path segments, which are paths connecting the waypoint and the waypoint by the operation processing means;

   In the waypoint path determination step (S10), among the local visible position space, which is a set of areas in which the arithmetic processing means does not have obstacles and collisions when moving straight from the robot among the movable areas detected by the robot sensors. A local goal determination step (S20) of determining the next waypoint of the last waypoint on the determined waypoint path or the next waypoint of the waypoint path segment as a target waypoint, a local goal;

   A path planning step (S30) in which the operation processing means plans a path from the robot to the local bone determined in the local bone determination step (S20); 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 route improvement method comprising a.
2. The method of claim 1,

   The route planning step (S30) is

   Real-time, characterized in that a route from the current position of the moving object to the local goal accessible pass segment, which is an area from the local goal to the nearest obstacle, is planned in the waypoint pass segment connecting the local goal and the previous waypoint. Waypoint route improvement method.
3. The method of claim 2,

   The route planning step (S30) is

   A real-time waypoint route improvement method comprising planning a route by planning an accessible waypoint, which is a temporary waypoint entering the local goal accessible pass segment.
4. The method of claim 3,

   The route planning step (S30) is

   If there is an intersection between the local visible position space and the local goal accessible path segment,

   A real-time waypoint route improvement method, characterized in that a point at which a specific cost function is minimized within an intersection between a local visible position space and a local goal accessible path segment is determined as an accessible waypoint.
5. The method of claim 3,

   The route planning step (S30) is

   If there is an intersection between the local visible position space and the local goal accessible path segment,

   A real-time waypoint path improvement method, characterized in that, within the intersection of the local visible position space and the local goal accessible path segment, the point where the movement distance from the robot to the local goal is shortest is determined as the accessible waypoint.
6. The method of claim 3,

   The route planning step (S30) is

   If there is no intersection between the local visible position space and the local goal accessible path segment,

   To avoid collisions with obstacles on the path between local waypoints, plan a local waypoint, which is a temporary waypoint on the side of the direction where there is no obstacle on the local visible position space, and access the point after passing the obstacle on the local visible position space A real-time waypoint route improvement method, characterized in that by planning a black waypoint, and planning a route passing through the local waypoint and the accessible waypoint.
7. The method of claim 6,

   The route planning step (S30) is

   If there is no intersection between the local visible position space and the local goal accessible path segment,

   A real-time waypoint route improvement method, characterized in that the local waypoint and the accessible waypoint are set with the smallest cost of the local waypoint and the accessible waypoint.
8. The method of claim 6,

   The route planning step (S30) is

   If there is no intersection between the local visible position space and the local goal accessible path segment,

   A method for improving a waypoint path in real time, characterized in that setting a local waypoint at which the angle formed by the current moving direction of the robot is minimum.
9. A computer-readable recording medium storing a program for implementing the real-time waypoint route improvement method according to any one of claims 1 to 8.
10. A program stored in a computer-readable recording medium for implementing the real-time waypoint route improvement method according to any one of claims 1 to 8.

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