CN113137969B - Local path optimization method of mobile robot - Google Patents
Local path optimization method of mobile robot Download PDFInfo
- Publication number
- CN113137969B CN113137969B CN202110521350.2A CN202110521350A CN113137969B CN 113137969 B CN113137969 B CN 113137969B CN 202110521350 A CN202110521350 A CN 202110521350A CN 113137969 B CN113137969 B CN 113137969B
- Authority
- CN
- China
- Prior art keywords
- robot
- obstacle
- point
- path
- search
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Aviation & Aerospace Engineering (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention relates to the technical field of mobile robots, in particular to a bidirectional search local path optimization algorithm of a mobile robot, which fully utilizes the information of a known obstacle region to optimize a path to be traveled, searches a local path in a bidirectional way in the traveling process of the robot, guides the robot to walk out of a local trap by setting a temporary target point if the obstacle is found to block the robot from going toward the target, selects an optimized path to finally reach an expected target point, and can automatically bypass the obstacle in an optimized way to go toward the target point.
Description
Technical Field
The invention relates to the technical field of mobile robots, in particular to a bidirectional search local path optimization algorithm of a mobile robot.
Background
At present, the application range of robots is wider and wider, and mobile robots become one of the main flows of current development, and real-time path planning research of mobile robots is generally divided into two main categories: one type is planning research based on a static global environment, the other type is local planning research based on sensor data, the robot planning party based on the static global information has huge using data and long planning time, and a local path planning algorithm based on the sensor local information plays an important role in real-time path planning of the robot.
The existing local path planning researches mainly comprise: 1. in order to solve the problem, some methods based on new potential field functions, such as a super-quadratic equation potential field method, a coordination function method, an artificial moment algorithm and the like, are proposed, but the inherent defects of the artificial potential field method are not fundamentally eliminated; 2. according to the wall walking algorithm, the robot is controlled to move along one direction of the edge of the obstacle all the time so as to break away from the local trap, but the optimization consideration of the passable path is less by the method, the local path optimization refers to that in the running process of the robot, if the obstacle is found to block the robot to the target, the robot can automatically bypass the obstacle to the target point in an optimized mode, and the decision basis can be provided for autonomous navigation of the robot by the local path optimization method of the robot, so that the running safety and economy of the robot are improved.
Disclosure of Invention
In order to solve the problems in the background technology, the invention discloses a local path optimization method of a mobile robot, which fully utilizes the information of a known obstacle region to optimize a path to be traveled, so that the robot is kept in an optimal state at all times in the traveling process.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the local path optimization method of the mobile robot is characterized by comprising the following steps of:
step 1: path initialization
Firstly, generating a line segment between a robot R and a destination point Tl(R,T) Then calculate the line segmentl(R,T) And an obstacleGL[n]Whether or not to intersect, ifl(R,T) And obstaclesGL[n]If they do not intersect, then end ifl(R,T) And obstacle(s)GL[n]If the intersection is found, recording the intersection as PC, and starting from the intersection PC, along the obstacleGL[n]Generating a "forward search line" in a clockwise direction from the edge of the search line "ClockMinRL[n]And from the intersection point PC along the obstacleGL[n]Generation of "reverse search route" in counterclockwise direction at edge of the search "InvertMinRL[n]；
Step 2: forward search
Moving an intersection point PC along a forward search line ' ClockMinRL [ n ], obtaining a new intersection point PC ' each time, judging whether a line segment l (PC ', T) between the new intersection point PC ' and a target T is intersected with an obstacle GL [ n ] or not, if so, continuing searching, otherwise, stopping moving and storing the PC ' point, marking the point as Tmin, generating a line segment l (Tmin, T), and when l (Tmin, T) is not intersected with the obstacle GL [ n ] and can be directly communicated with the target point T, marking the path as ClockMinRL [ n ] and entering the next step, otherwise, repeating the forward searching step to continue searching;
step 3: reverse search
Moving an intersection point PC along a reverse search line InvertMinRL n, obtaining a new intersection point PC 'after each movement, judging whether a line segment l (PC' T) between the new intersection point PC 'and a target T and an obstacle GL n intersect, if so, continuing searching, otherwise stopping moving and storing the PC' point and marking the point as tmin, generating a line segment l (tmin, T) at the same time, and when l (tmin, T) and GL n do not intersect and can be directly communicated with the target point T, marking the path as InvertMinRL n as the path to enter the next step at the same time, otherwise, continuing repeating the search of the reverse searching step;
step 4: forward and reverse search path optimization
Firstly, starting from a Tmin point in ClockMinRL [ n ], searching the robot R along the edge of the obstacle GL [ n ] in the direction opposite to the reverse search path in the step 3, setting an intersection point as PCj if l (Tmin, R) is intersected with GL [ n ], simultaneously moving PCj points along the direction opposite to the reverse search, marking the PCj points as Rmin if l (Tmin, R) is not intersected with the obstacle GL [ n ], overlapping with Tmin at the moment, setting a connecting line l (Rmin, R) of the point and the robot R, continuing searching the robot R in the direction opposite to the reverse search path until l (Rmin, R) is not intersected with GL [ n ] and can be directly communicated with the robot R; then, starting from the tmin point in InvertMinRL [ n ], searching the robot R along the edge of the obstacle GL [ n ] in the direction opposite to the forward search in step 2, during the search, recording the position of the robot R which passes over the obstacle and is seen for the first time as Rmin, and generating a line segment l (Rmin, R), and finally comparing the lengths of the paths of ClockMinRL [ n ] and InvertMinRL [ n ], wherein one of the short distances is the shortest path for the robot to travel to the destination point.
Compared with the prior art, the invention has the beneficial effects that:
the invention discloses a local path optimization method of a mobile robot, which fully utilizes the information of a known obstacle region to optimize a path to be traveled, searches a local path in a bidirectional manner in the traveling process of the robot, guides the robot to walk out of a local trap by setting a temporary target point if an obstacle is found to block the robot from traveling to the target, selects an optimized path to finally reach an expected target point, and can automatically bypass the obstacle in an optimized manner to travel to the target point.
Drawings
FIG. 1 is a flow chart of a method of local path optimization for a mobile robot of the present invention;
FIG. 2 is a schematic view of a forward search of the mobile robot of the present invention;
FIG. 3 is a schematic view of a reverse search of the mobile robot of the present invention;
FIG. 4 is a schematic diagram of a mobile robot search path of the present invention;
FIG. 5 is a schematic view of an obstacle between a mobile robot and a destination in an embodiment of the invention;
FIG. 6 is a schematic diagram of a forward search path of a mobile robot in an embodiment of the present invention;
fig. 7 is a schematic diagram of a reverse search path of a mobile robot in an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The flow of the local path optimization method of the mobile robot is shown in fig. 1, and as shown in fig. 5, the mobile robot R encounters an obstacle in the process of traveling to a target T, and travels by the path optimization method of the invention, and the specific steps are as follows:
path initialization: as shown in fig. 2, the robot R encounters an obstacle in front of the forward direction of the destination point T, at this time, the robot R needs to autonomously travel over the obstacle to reach the destination point T, the position of the robot R itself and the position of the destination point T are known, then the robot R sets a path l (R, T) from R to T according to the position of the robot R itself and the position of the destination point T, then the obstacle is set to GL [ n ] and calculates whether the path l (R, T) intersects with the obstacle GL [ n ], if the path l (R, T) does not intersect with the obstacle GL [ n ], it is indicated that there is no obstacle between the robot R and the destination point T, otherwise the intersection is recorded as PC and the path search task is started;
forward search: as shown in fig. 2, the robot R starts to search clockwise along the edge of the obstacle GL [ n ], an obstacle exists between the current robot R and the destination point T, the intersection point is PC, the PC point is moved clockwise along the edge of the obstacle GL [ n ] to obtain a new intersection point PC ', and then it is determined whether the connection line l (PC ', T) between the obtained new intersection point PC ' and the destination point T intersects the obstacle GL [ n ]; if the line l (PC ', T) intersects with the obstacle GL [ n ], continuing to move the PC point clockwise and recalculate whether the current line l (PC', T) of the PC 'point and the destination point T intersects with the obstacle GL [ n ], if so, continuing, otherwise stopping moving the PC point and storing the PC' point and marking the point as Tmin, simultaneously generating the line l (Tmin, T) between Tmin and the destination point T, then calculating the intersection of the line l (Tmin, T) with the obstacle GL [ n ], and repeating the above method until l (Tmin, T) and GL [ n ] do not intersect, so far the robot R can bypass the obstacle along the route and mark the path as ClockMinRL [ n ];
reverse search: as shown in fig. 3, the robot R searches only in the counterclockwise direction similarly to the forward search method starting from the intersection PC of the original path l (R, T) and the obstacle GL [ n ], and finally can obtain a path and is denoted as InvertMinRL [ n ];
and (3) forward and reverse search path optimization: as shown in FIG. 4, the path ClockMinRL [ n ] is first searched from the forward direction]Starting optimization from ClockMinRL [ n ]]Starting at the Tmin point in (a), unlike the forward search, the optimization is performed from the opposite direction to the reverse search path, along the obstacle GL [ n ]]The edge searching robot R; at this time, a connection line l (T1, R) between the point T1 and the robot R is provided, and if the connection line l (T1, R) and the obstacle GL [ n ]]The intersection point is set as PC j And set the connection of the point and the robotLine l (PC) j R) if the connection line l (T1, R) is connected with the obstacle GL [ n ]]Disjoint points indicate that T1 point and robot R are clear, followed by PC j Along the "reverse search path", one step of the movement is determined as l (PC j R) and obstacle GL [ n ]]If it is intersected, if so, the motion along the reverse search line is continued until PC j The point can be directly communicated with the robot R, and the PC is connected with the robot R at the moment j The point is located at a position R1 and is set to the "start nearest point", and similarly the reverse search path InvertMinRL [ n ]]The optimization is from InvertMinRL [ n ]]The tmin point in (1) is started, and is performed along the direction opposite to the direction of forward search, which is different from the forward search path optimization step, and finally the PC is obtained j The position of the dot is r1, and the final comparison is ClockMinRL [ n ]]And InvertMinRL [ n ]]One of the short distances is the shortest path for the robot to travel to the destination point.
When the robot encounters a new obstacle, the robot bypasses the obstacle until reaching the target, as shown in fig. 6, which shows a forward searched path of the last obstacle to reach the target, and fig. 7, which shows a reverse searched path of the last obstacle to reach the target, it can be found from fig. 6 and 7 that the forward searched path is shorter than the reverse searched path of fig. 7, so that the robot automatically selects the nearest path to reach the target, and the final path is the path shown in fig. 6.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art.
Claims (1)
1. The local path optimization method of the mobile robot is characterized by comprising the following steps of:
step 1: path initialization
Firstly, generating a line segment between a robot R and a destination point Tl(R,T) Then calculate the line segmentl(R,T) And an obstacleGL[n]Whether or not to intersect, ifl(R,T) And obstaclesGL[n]If they do not intersect, then end ifl(R,T) And obstacle(s)GL[n]If the intersection is found, recording the intersection as PC, and starting from the intersection PC, along the obstacleGL[n]Generating a "forward search line" in a clockwise direction from the edge of the search line "ClockMinRL[n]And from the intersection point PC along the obstacleGL[n]Generation of "reverse search route" in counterclockwise direction at edge of the search "InvertMinRL[n]；
Step 2: forward search
Along a "forward search line"ClockMinRL[n]Moving the intersection point PC, obtaining a new intersection point PC 'each time, and judging a line segment between the new intersection point PC' and the target Tl(PC´,T) And disordersGL[n]If the PC points are intersected, searching is continued, otherwise, moving and storing the PC points are stopped and marked as the pointsT min Simultaneous generation of line segmentsl(T min ,T) When (when)l(T min ,T) And an obstacleGL[n]When the paths are disjoint and can be directly communicated with the destination point T, the paths are recorded asClockMinRL[n]Meanwhile, entering the next step, otherwise repeating the forward searching step to continue searching;
step 3: reverse search
Along a "reverse search line"InvertMinRL[n]Moving the intersection point PC, obtaining a new intersection point PC 'each time, and judging a line segment between the new intersection point PC' and the target Tl(PC´´,T) And disordersGL[n]If the PC's are intersected, searching is continued, otherwise, moving and storing the PC's and marking the PC's as the PC' st min Simultaneous generation of line segmentsl(t min ,T) When (when)l(t min ,T) AndGL[n]when the paths are disjoint and can be directly communicated with the destination point T, the paths are recorded asInvertMinRL[n]Simultaneously entering the next step, otherwise, continuing to repeat the reverse search step for searching;
step 4: forward and reverse search path optimization
First fromClockMinRL[n]In (a) and (b)T min Starting at the point, along the obstacle in the opposite direction to the "reverse search path" in step 3GL[n]Edge search robot R, connection pointT min Connection line with robot Rl(T min ,R) If (if)l(T min ,R) And (3) withGL[n]The intersection point is set as PC j While moving the PC in the opposite direction to the reverse search j If the dot isl(T min ,R) And an obstacleGL[n]Disjoint, the PC is j Point is marked asr min At this timer min And (3) witht min Overlap and set the connecting line of the point and the robot Rl(r min ,R) The robot R continues to be searched in the opposite direction to the "reverse search path" untill(R min ,R) AndGL[n]the robot is disjoint and can be directly communicated with the robot R; then fromInvertMinRL[n]In (a) and (b)t min Starting at a point, along the obstacle in a direction opposite to the forward search in step 2GL[n]Is to go over an obstacle and firstThe R position of the robot is recorded as the next visionR min And generate line segmentsl(R min ,R) Finally compareClockMinRL[n]AndInvertMinRL[n]the length of the path distance, wherein one short distance is the shortest path for the robot to travel to the destination point.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110521350.2A CN113137969B (en) | 2021-05-13 | 2021-05-13 | Local path optimization method of mobile robot |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110521350.2A CN113137969B (en) | 2021-05-13 | 2021-05-13 | Local path optimization method of mobile robot |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113137969A CN113137969A (en) | 2021-07-20 |
| CN113137969B true CN113137969B (en) | 2023-08-04 |
Family
ID=76817714
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110521350.2A Active CN113137969B (en) | 2021-05-13 | 2021-05-13 | Local path optimization method of mobile robot |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113137969B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113741455B (en) * | 2021-09-02 | 2023-03-28 | 重庆大学 | Mobile robot visual range coverage path planning method |
| CN115437368B (en) * | 2022-06-02 | 2023-08-29 | 珠海云洲智能科技股份有限公司 | Rescue path determining method and device, rescue equipment and readable storage medium |
| CN115877852B (en) * | 2023-02-22 | 2023-06-13 | 深圳市欧拉智造科技有限公司 | Robot motion control method, robot, and computer-readable storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101604166A (en) * | 2009-07-10 | 2009-12-16 | 杭州电子科技大学 | A kind of method for planning path for mobile robot based on particle swarm optimization algorithm |
| CN104457775A (en) * | 2014-12-12 | 2015-03-25 | 北京航天宏图信息技术有限责任公司 | Path determination method and device, and navigation instrument |
| CN111174798A (en) * | 2020-01-17 | 2020-05-19 | 长安大学 | Foot type robot path planning method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7603229B2 (en) * | 2004-08-25 | 2009-10-13 | Microsoft Corporation | Efficiently finding shortest paths using landmarks for computing lower-bound distance estimates |
-
2021
- 2021-05-13 CN CN202110521350.2A patent/CN113137969B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101604166A (en) * | 2009-07-10 | 2009-12-16 | 杭州电子科技大学 | A kind of method for planning path for mobile robot based on particle swarm optimization algorithm |
| CN104457775A (en) * | 2014-12-12 | 2015-03-25 | 北京航天宏图信息技术有限责任公司 | Path determination method and device, and navigation instrument |
| CN111174798A (en) * | 2020-01-17 | 2020-05-19 | 长安大学 | Foot type robot path planning method |
Non-Patent Citations (1)
| Title |
|---|
| 基于立体视觉的环境构建及机器人路径规划研究;周自维;中国博士学位论文全文数据库信息科技辑;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113137969A (en) | 2021-07-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113137969B (en) | Local path optimization method of mobile robot | |
| CN112964271B (en) | Multi-scene-oriented automatic driving planning method and system | |
| CN106598055B (en) | A kind of intelligent vehicle local paths planning method and its device, vehicle | |
| CN111426330B (en) | Path generation method and device, unmanned transportation system and storage medium | |
| CN106647769B (en) | Based on A*Extract AGV path trace and the avoidance coordination approach of pilot point | |
| CN108121339A (en) | Wheeled zone setting device and wheeled area setting method | |
| CN110361013B (en) | Path planning system and method for vehicle model | |
| CN111060108B (en) | Path planning method and device and engineering vehicle | |
| KR20230056802A (en) | Methods and systems for map matching | |
| US11334083B2 (en) | Route tracking method for mobile vehicle | |
| CN113721637B (en) | Intelligent vehicle dynamic obstacle avoidance path continuous planning method and system and storage medium | |
| US11747826B2 (en) | Method for route optimization based on dynamic window and redundant node filtering | |
| CN111397622B (en) | Intelligent automobile local path planning method based on improved A-algorithm and Morphin algorithm | |
| JP6090226B2 (en) | Route generation apparatus and route generation method | |
| CN111006667A (en) | Automatic driving track generation system under high-speed scene | |
| CN104616520A (en) | Method and device for dynamically recording navigation trail | |
| CN109579854A (en) | Unmanned vehicle barrier-avoiding method based on Quick Extended random tree | |
| CN111896004A (en) | Narrow passage vehicle track planning method and system | |
| KR20210121595A (en) | Optimal route finding device and operating method thereof | |
| CN115079702B (en) | Intelligent vehicle planning method and system under mixed road scene | |
| CN115140096A (en) | Spline curve and polynomial curve-based automatic driving track planning method | |
| CN113593238B (en) | Intersection virtual lane modeling method for automatic driving navigation | |
| CN114715192A (en) | Decoupled real-time trajectory planning method, device and system for automatic driving vehicle | |
| JP5074153B2 (en) | Route generation device and method, and mobile device including route generation device | |
| CN116149314A (en) | Robot full-coverage operation method and device and robot |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |