CN115220461A - Robot single system and multi-robot interaction and cooperation method in indoor complex environment - Google Patents
Robot single system and multi-robot interaction and cooperation method in indoor complex environment Download PDFInfo
- Publication number
- CN115220461A CN115220461A CN202211147755.5A CN202211147755A CN115220461A CN 115220461 A CN115220461 A CN 115220461A CN 202211147755 A CN202211147755 A CN 202211147755A CN 115220461 A CN115220461 A CN 115220461A
- Authority
- CN
- China
- Prior art keywords
- robot
- robots
- conflict
- intersection
- task
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 230000003993 interaction Effects 0.000 title description 5
- 230000002452 interceptive effect Effects 0.000 claims abstract description 10
- 230000007246 mechanism Effects 0.000 claims description 24
- 238000004891 communication Methods 0.000 claims description 21
- 238000007726 management method Methods 0.000 claims description 19
- 238000000547 structure data Methods 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 239000000178 monomer Substances 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 5
- 230000007547 defect Effects 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
- 206010033799 Paralysis Diseases 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 239000004744 fabric Substances 0.000 description 1
- 230000004044 response Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- 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
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- 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
- G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
- G05D1/0289—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling with means for avoiding collisions between vehicles
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Remote Sensing (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Radar, Positioning & Navigation (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Databases & Information Systems (AREA)
- Automation & Control Theory (AREA)
- Aviation & Aerospace Engineering (AREA)
- Economics (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Tourism & Hospitality (AREA)
- Development Economics (AREA)
- Quality & Reliability (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention belongs to the field of logistics robots, and relates to a robot single system and a multi-robot interactive cooperation method in an indoor complex environment. The invention solves the problem of multi-robot path conflict, thereby efficiently completing tasks.
Description
Technical Field
The invention belongs to the field of logistics robots, and particularly relates to a robot single system and a multi-robot autonomous interactive cooperation method in an indoor complex environment.
Background
Multi-vehicle path planning has been one of the main research subjects in the field of cluster robots. Most of the current popular multi-vehicle path planning algorithms have strict limitations on the rules of the running paths of the robots, for example, only one-way synchronization is allowed, the moving areas of the robots are limited, and the like, so that the computation amount is reduced, and the problem of deadlock of the robots is avoided.
When a plurality of robots execute tasks in the same scene, the task routes may conflict with each other, so that the system may be paralyzed, wherein the conflict problems include pursuing conflicts, opposite conflicts, node conflicts and the like. In order to solve the problem of path planning of multiple robots, multi-robot traffic management can be performed.
The existing multi-robot traffic management solution has the defects of a one-way graph method, a time window method and a dynamic route planning method.
1. The drawback of the one-way graph method:
(1) Intersection conflicts still exist and cannot be solved;
(2) The route is single, and the phenomenon of detour is easy to occur, so that the efficiency of the robot for completing tasks is influenced;
(3) The method can only meet the condition of a single route, and the road network with complex scenes cannot realize the one-way graph representation, such as the route without a loop.
2. The time window method has the following disadvantages:
(1) The time prediction type estimation can not be completely accurate, and after the robot can not arrive at a corresponding position according to preset time on time, the time prediction type estimation and the speed control can not effectively carry out task management on the robot, so that route conflicts of multiple robots are caused, and even the whole system is paralyzed;
(2) The time window method is applied to a master control multi-robot task management system, real-time running data of all robots are monitored, a global optimum is output by analyzing running states of all robots at present to distribute tasks, the number of the robots and the complexity of scenes directly influence the calculated amount and the complexity of the management system, and the risk of system crash exists;
(3) The time window method is complex in algorithm structure, has more reference factors, and has the condition that the optimal solution cannot be output, so that the tasks cannot be reasonably distributed, and the route conflict occurs to multiple robots;
(4) The method is only suitable for wide scenes with unobstructed routes and no external intervention, and is not suitable for scenes with large people flow change, narrow channels and routes needing obstacle avoidance, similar to restaurants, and the time window method is not suitable.
3. The dynamic path planning method has the following defects:
(1) The method is applied to a master control multi-robot task management system, and has the problem of real-time performance along with the increase of the task quantity and the increase of the task path length;
(2) In order to avoid collision of task paths of multiple robots, the situation that the task paths of the robots go around far ways easily exists, so that the cost for executing the task paths is increased, and the efficiency is influenced;
(3) When the alternate paths from a certain site to other sites are few or even no alternate paths, the problem of multi-robot task path conflict cannot be avoided.
Chinese patent application CN108759851A provides a time window-based multi-vehicle path planning method, system, device and storage medium, the method includes: traversing each edge between adjacent nodes in the road network node map to obtain the passing time of each edge and the shortest estimated distance between any two points in the map; obtaining a current access list and a historical access list of a first vehicle passing through each node from a starting node in sequence; updating the locking time windows of the nodes and the locking time windows of the edges according to all vehicles which have finished the path planning; obtaining a current access list and a historical access list of the next required path planning passing through each node from the initial node in sequence; if the time interval of the legal time window is contained in the time interval of the locking time window of the node, discarding the node; and sequentially outputting each front node in the historical access list as a path plan. The disadvantages of which are described in the disadvantages of the above time windowing method.
Disclosure of Invention
According to the defects in the prior art, the invention provides a single robot system and a multi-robot interactive cooperation method in an indoor complex environment, and solves the problems that in the prior cooperation technology, under the indoor general scene, the current people flow change is large, the channel is narrow, the route needs to be avoided, the multi-robot cooperation efficiency is low, and the task path conflict rate is high.
The robot single system in the indoor complex environment comprises a robot cluster consisting of a plurality of robots, each robot is provided with the robot single system, and the robot single system comprises a road network map, a task management system, a path planning and collaborative autonomous decision unit and a communication module which are realized through computer software and hardware;
the task management system forms a path plan on the road network map according to the road network structure data and the task points; the cooperative autonomous decision unit acquires a path planning point sequence and the running state of a nearby robot, acquires the position of the robot through the positioning unit, outputs the running state of the robot and feeds information back to the task management system to replan instructions; the communication module is connected with the communication module of the nearby robot through the wireless radio frequency transceiver, so that the state information communication between the robots is realized.
The invention relates to a multi-robot interaction cooperation method in an indoor complex environment by adopting the robot single system, which comprises the following steps:
s1, collecting key conflict interest point data in road network structure data of a road network map, generating a node topological connection relation, eliminating redundant common point data, creating an interest point topological structure map according to the key conflict interest point data, and setting a scene route and intersection according to requirements;
s2, after the robot obtains the tasks, performing combined planning through a task management system according to the interest point topological structure map and the task points in the road network structure data to obtain a path optimal sequence point combination to form path planning, then acquiring a path planning point sequence through a collaborative autonomous decision-making unit, and executing task paths;
s3, in the process of executing tasks by the robot, calculating the running state of the robot in cooperation with the autonomous decision unit, and broadcasting and issuing the running state of the robot to nearby robots through the communication module;
and S4, in the process of executing the task by the robot, the cooperative autonomous decision unit receives the running state information of the nearby robot through the communication module, judges the road conflict relationship to be generated between the cooperative autonomous decision unit and the nearby robot according to the current speed prediction through a traveling prediction mechanism, calculates a robot conflict avoidance strategy according to the traffic rule and the priority weight, and executes the strategy.
By adopting all tasks, the robot self plans paths, communicates and collects information to carry out self decision, detection conflict and conflict resolution, the response efficiency is improved, the complexity of centralized judgment is reduced, and the problems of overlarge data volume, communication delay or interruption in centralized distributed network data communication are avoided.
And performing conflict judgment by adopting a road network structure and a route planning prediction mode, and performing path re-planning route by using the speed decision of the body robot and the conflict decision in the task execution process to solve the conflict problem.
According to the invention, a traffic rule is adopted, and all elements such as a road network structure, a task level, the re-planning times, a conflict node distance, a real-time task state and the like are combined to form a conflict state conversion model for judgment, so that the robot needs to decide whether to re-plan a task path for detour and turning around by itself under the condition of major conflict, and the centralized deadlock waiting is avoided.
The preferred scheme is as follows:
the self-running state in the step S3 includes the robot number, the current position, the executing path interest point and the point attribute.
And the advancing prediction mechanism in the step S4 comprises the steps of judging whether an intersection exists between two robot line points, calculating the distance of the intersection point and a turning node, and finally analyzing the conflict relationship and decision between the robots.
The weight mechanism model of the weight calculation in step S4 is P = k1 × L + k2 × N + k3 × H +Wherein P is a conflict state conversion model index, L is a task importance level, N is a re-planning time, H is a conflict node distance, and the remaining path point complexity D is a reference index, k1-k3 are weight coefficients, and M isThe total number of points planned, i, is the order id of the remaining points of the planned sequence.
The robot conflict avoidance strategy in the step S4 comprises a non-intersection node conflict decision mechanism and an intersection node conflict decision mechanism.
Further, the non-intersection node conflict decision mechanism comprises judging the opposite or opposite relation with the body robot, judging whether the safety distance between the two robots is less than or not, and judging the weight of the body robot;
the non-intersection nodes are common points in the interest point topological structure map, when the self-body robot and the adjacent robot have intersection at the non-intersection nodes, the opposite relation between the adjacent robot and the self-body robot is judged, if the self-body robot and the adjacent robot run in the same direction, the safety distance between the adjacent robot and the self-body robot is judged, if the safety distance is smaller than the safety distance, the rear robot decelerates and keeps a certain distance to follow (the rear robot can also stop to give way and then run), and if the safety distance is larger than the safety distance, the rear robot keeps a certain distance to follow according to the speed of the front robot; and if the two robots are running oppositely, judging the weights of the two robots, waiting for avoidance by the robot with high weight, then running normally, turning around the robot with low weight, and replanning the path from the current position.
The intersection node conflict decision mechanism comprises the steps of judging the opposite relation with the self-body robot, judging whether the intersection node only has one side to move straightly, judging whether the self-body robot is close to the intersection node, and judging the weight of the self-body robot;
the intersection nodes are key conflict interest points in an interest point topological structure map, when intersection exists between the body robot and the adjacent robots at the intersection nodes, the opposite relation between the adjacent robots and the body robot is judged, if the routes of the adjacent robots and the body robot are in a non-opposite relation, whether only one of the robots is in a straight line or not is judged, if yes, turning is carried out to allow the straight line, if both the robots are in the straight line or both the robots are in the turning line, the other robot close to the intersection nodes normally runs, and the other robot stops and allows the straight line and then runs; if the two routes are in an opposite relationship, the weights of the two routes are judged, the robot with the high weight runs normally, the robot with the low weight turns around, and the route is re-planned from the current position.
In the above-described determination of the facing relationship between the neighboring robot and the main robot, the facing relationship means that the predetermined routes of the two robots travel toward the position where the other robot is located after passing through the intersection node, and therefore there is a facing collision.
The invention has the beneficial effects that:
the robot single system cooperation multi-robot interaction cooperation method can be used for a general robot system, is applied to an indoor general scene, solves the problem of multi-robot path conflict, and can efficiently complete tasks. In service scenes such as stores and warehouses, the arrangement of the table and the chair of the container affects the arrangement of the road network structure. Specifically, the present invention:
1. the method comprises the steps of improving a road network structure, thinning dense road network structure maps, eliminating redundant common points in a mode of conflicting key interest points, creating an interest point topological structure map according to key conflicting interest point data, improving calculation efficiency and reducing map topological structure complexity;
2. under the assistance of a sparse interest point topological structure map, centralized cooperative scheduling of a master control robot management system in the prior art is converted into a cooperative autonomous decision unit capable of autonomously deciding for each robot monomer, so that the cooperative calculation amount of multiple robots and the limitation of a road network are reduced;
3. the global detection mode that the multiple robots have path conflict is changed into the mode that the robots receive the running states of the nearby robots through the communication modules of the robots, and the self-body robot carries out prediction and judgment by itself, so that the detection frequency is reduced, and the operation efficiency is improved;
4. the self-body robot autonomously decides to compete out a priority passing mode through a certain priority, a traffic rule and a nearby running state, so that the problem of robot road conflict is solved, the flexibility of a road network structure is improved, the conflict rate of a multi-machine cooperative task path is reduced, and the running efficiency of the robot is improved.
Drawings
FIG. 1 is a schematic block diagram of a robotic cell system of the present invention;
FIG. 2 is a point of interest topology map of the present invention;
FIG. 3 is a task path planning operation diagram of the present invention;
FIG. 4 is a logic diagram of the travel prediction mechanism of the present invention;
FIG. 5 is a logic decision diagram of a non-intersecting node collision decision mechanism of the present invention;
fig. 6 is a logic decision diagram of a rendezvous node collision decision mechanism of the present invention.
Detailed Description
Embodiments of the invention are further described below with reference to the accompanying drawings:
as shown in fig. 1, the robot single system in the indoor complex environment includes a robot cluster composed of a plurality of robots, each robot is provided with a robot single system, and the robot single system includes a road network map, a task management system, a path planning and collaborative autonomous decision unit and a communication module, which are realized by computer software and hardware;
the task management system forms a path plan on the road network map according to the road network structure data and the task points; the cooperative autonomous decision unit acquires a path planning point sequence and the running state of a nearby robot, acquires the position of the robot through the positioning unit, outputs the running state of the robot and feeds information back to the task management system to replan instructions; the communication module is connected with the communication module of the nearby robot through the wireless radio frequency transceiver, and state information communication between the robots is realized.
The multi-robot interaction cooperation method in the indoor complex environment by adopting the robot single system comprises the following steps:
s1, collecting key conflict interest point data in road network structure data of a road network map, generating a node topological connection relation, eliminating redundant common point data, creating an interest point topological structure map according to the key conflict interest point data, and setting a scene route and intersection according to requirements;
s2, after the robot obtains the tasks, performing combined planning through a task management system according to the interest point topological structure map and the task points in the road network structure data to obtain an optimal sequence point combination of the path to form path planning, then acquiring a path planning point sequence through a collaborative autonomous decision unit, and executing a task path;
s3, in the process of executing tasks by the robot, calculating the running state of the robot in cooperation with the autonomous decision unit, and broadcasting and issuing the running state of the robot to nearby robots through the communication module;
and S4, in the process of executing the task by the robot, the cooperative autonomous decision unit receives the running state information of the nearby robot through the communication module, judges the road conflict relationship to be generated between the cooperative autonomous decision unit and the nearby robot according to the current speed prediction through a traveling prediction mechanism, calculates a robot conflict avoidance strategy according to the traffic rule and the priority weight, and executes the strategy.
The self-running state in step S3 includes the robot number, the current position, the executing path interest point, and the point attributes (common point, intersection node).
In step S1, a topological structure map of interest points is created, as shown in fig. 2. The interest point topological structure map comprises data structures such as two-dimensional coordinate points, point attributes and point-to-point topological relations: coordinate points (x, y); point attribute: common points, junction nodes (key collision points of interest); the black round points are intersection nodes, the black square points are common points, and the black polygonal frame is a robot.
The robot receives a task and obtains task path points through path planning, as shown in fig. 3, a black polygon frame is the robot, and a black solid line is a path planning sequence; in the process of autonomous operation, in order to facilitate other nearby robots to accurately predict behaviors of the self-body robot, the positions of the robots are obtained through the positioning units in the cooperative autonomous decision-making unit, the interest points to be approached next and interest point steering information and the like are analyzed, and other nearby robots are announced. Ontology robot cloth basic information: robot number, current location, point of interest of the path being executed, point attributes, state of travel (turn or straight).
As shown in fig. 4, the advance prediction mechanism in step S4 includes determining whether there is an intersection between two robot route points, calculating the intersection point distance and the turning node, and finally analyzing the conflict relationship and decision between the robots.
The weight mechanism model of the weight calculation in step S4 is P = k1 × L + k2 × N + k3 × H +Wherein P is a conflict state conversion model index, L is a task importance level, N is a re-planning number, H is a conflict node distance, and the complexity D of the remaining path points is a reference index, k1-k3 are weight coefficients, M is the total number of points for planning, and i is the order id of the remaining points of the planning sequence.
The weight difference is mainly influenced by the following three reasons: 1. the attention degrees of the indexes are different, and the subjective difference of an evaluator is reflected; 2. the functions of the indexes in the evaluation are different, and the objective difference among the indexes is reflected; 3. the reliability degree of each index is different, and the reliability degree of the information provided by each index is different.
The robot collision avoidance strategy in step S4 includes a non-intersection node collision decision mechanism and an intersection node collision decision mechanism.
As shown in fig. 5, the non-intersection node conflict decision mechanism includes determining an opposite or opposite relationship with the ontology robot, determining whether the distance is less than a safety distance between the two robots, and determining a weight of the ontology robot;
the non-intersection nodes are common points in the interest point topological structure map, when the non-intersection nodes of the body robot and the adjacent robots are intersected, the opposite relation between the adjacent robots and the body robot is judged, if the adjacent robots and the adjacent robots are driven in the same direction, the safety distance between the adjacent robots and the body robot is judged, if the safety distance is smaller than the safety distance, the rear robot is decelerated and keeps a certain distance to follow, and if the safety distance is larger than the safety distance, the rear robot keeps a certain distance to follow according to the speed of the front robot; and if the two robots are running oppositely, judging the weights of the two robots, waiting for avoidance by the robot with high weight, then running normally, turning around the robot with low weight, and replanning the path from the current position.
As shown in fig. 6, the intersection node conflict decision mechanism includes determining an opposite relation with the ontology robot, determining whether the intersection node has only one side going straight, determining that the ontology robot is close to the intersection node, and determining a weight of the ontology robot;
the intersection nodes are key conflict interest points in an interest point topological structure map, when intersection exists between the self-body robot and the adjacent robots at the intersection nodes, the opposite relation between the adjacent robots and the self-body robot is judged, if the routes of the two robots are in a non-opposite relation, whether only one of the robots is in a straight direction or not is judged, if yes, turning is carried out to allow the robot to run straight, if both the robots are in the straight direction or both the robots are in the turning direction, the other robot is stopped to allow the robot to run, and then the robot is stopped to allow the robot to run; if the two routes are in an opposite relationship, the weights of the two routes are judged, the robot with the high weight runs normally, the robot with the low weight turns around, and the route is re-planned from the current position.
Claims (8)
1. Robot monomer system under indoor complex environment, including the robot cluster that comprises a plurality of robots, its characterized in that: each robot is provided with a robot single system, and the robot single system comprises a road network map, a task management system, a path planning and collaborative autonomous decision unit and a communication module which are realized through computer software and hardware;
the task management system forms a path plan on the road network map according to the road network structure data and the task points; the collaborative autonomous decision unit acquires a path planning point sequence and the running state of a nearby robot, acquires the position of the robot through the positioning unit, outputs the running state of the robot and feeds information back to the task management system to re-plan instructions; the communication module is connected with the communication module of the nearby robot through the wireless radio frequency transceiver, so that the state information communication between the robots is realized.
2. The multi-robot interactive collaboration method in indoor complex environment using the robot cell system as claimed in claim 1, comprising the steps of:
s1, collecting key conflict interest point data in road network structure data of a road network map, generating a node topological connection relation, eliminating redundant common point data, creating an interest point topological structure map according to the key conflict interest point data, and setting a scene route and intersection intersections according to requirements;
s2, after the robot obtains the tasks, performing combined planning through a task management system according to the interest point topological structure map and the task points in the road network structure data to obtain a path optimal sequence point combination to form path planning, then acquiring a path planning point sequence through a collaborative autonomous decision-making unit, and executing task paths;
s3, in the process of executing tasks by the robot, calculating the running state of the robot in cooperation with the autonomous decision unit, and broadcasting and issuing the running state of the robot to nearby robots through the communication module;
and S4, in the process of executing the task by the robot, the cooperative autonomous decision unit receives the running state information of the nearby robot through the communication module, judges the road conflict relationship to be generated between the cooperative autonomous decision unit and the nearby robot according to the current speed prediction through a traveling prediction mechanism, calculates a robot conflict avoidance strategy according to the traffic rule and the priority weight, and executes the strategy.
3. The multi-robot interactive collaboration method in indoor complex environment as claimed in claim 2, wherein: the self-running state in the step S3 includes the robot number, the current position, the executing path interest point and the point attribute.
4. The multi-robot interactive collaboration method in indoor complex environment as claimed in claim 2, wherein: and the advancing prediction mechanism in the step S4 comprises the steps of judging whether an intersection exists between the two robot line points, calculating the distance of the intersection point and a turning node, and finally analyzing the conflict relationship and decision between the robots.
5. The multi-robot interactive collaboration method in indoor complex environment as claimed in claim 2, wherein: the weight mechanism model of the weight calculation in step S4 is P = k1 × L + k2 × N + k3 × H +Wherein P is a conflict state conversion model index, L is a task importance level, N is a re-planning time, H is a conflict node distance, and the complexity D of the remaining path points is a reference index, k1-k3 are weight coefficients, M is the total number of points for planning, and i is the order id of the remaining points of the planning sequence.
6. The multi-robot interactive collaboration method in indoor complex environment as claimed in claim 2, wherein: the robot collision avoidance strategy in step S4 includes a non-intersection node collision decision mechanism and an intersection node collision decision mechanism.
7. The multi-robot interactive collaboration method in indoor complex environment as claimed in claim 6, wherein: the non-intersection node conflict decision mechanism comprises the steps of judging the opposite or opposite relation with the body robot, judging whether the safety distance between the two robots is smaller than or not, and judging the weight of the body robot;
the non-intersection nodes are common points in the interest point topological structure map, when the non-intersection nodes of the body robot and the adjacent robots are intersected, the opposite relation between the adjacent robots and the body robot is judged, if the adjacent robots and the adjacent robots are driven in the same direction, the safety distance between the adjacent robots and the body robot is judged, if the safety distance is smaller than the safety distance, the rear robot is decelerated and keeps a certain distance to follow, and if the safety distance is larger than the safety distance, the rear robot keeps a certain distance to follow according to the speed of the front robot; and if the two robots run oppositely, judging the weights of the two robots, waiting for avoidance by the robot with the high weight, then running normally, turning around the robot with the low weight, and replanning the path from the current position.
8. The multi-robot interactive collaboration method in indoor complex environment as claimed in claim 6, wherein: the intersection node conflict decision mechanism comprises the steps of judging the opposite relation with the self-body robot, judging whether the intersection node only has one side to move straightly, judging whether the self-body robot is close to the intersection node, and judging the weight of the self-body robot;
the intersection nodes are key conflict interest points in an interest point topological structure map, when intersection exists between the self-body robot and the adjacent robots at the intersection nodes, the opposite relation between the adjacent robots and the self-body robot is judged, if the routes of the two robots are in a non-opposite relation, whether only one of the robots is in a straight direction or not is judged, if yes, turning is carried out to allow the robot to run straight, if both the robots are in the straight direction or both the robots are in the turning direction, the other robot is stopped to allow the robot to run, and then the robot is stopped to allow the robot to run; and if the two routes are in an opposite relation, judging the weights of the two routes, enabling the robot with the high weight to normally run, turning around the robot with the low weight, and replanning the route from the current position.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202211147755.5A CN115220461B (en) | 2022-09-21 | 2022-09-21 | Robot single system and multi-robot interaction cooperation method in indoor complex environment |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202211147755.5A CN115220461B (en) | 2022-09-21 | 2022-09-21 | Robot single system and multi-robot interaction cooperation method in indoor complex environment |
Publications (2)
Publication Number | Publication Date |
---|---|
CN115220461A true CN115220461A (en) | 2022-10-21 |
CN115220461B CN115220461B (en) | 2023-02-17 |
Family
ID=83617753
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202211147755.5A Active CN115220461B (en) | 2022-09-21 | 2022-09-21 | Robot single system and multi-robot interaction cooperation method in indoor complex environment |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN115220461B (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN117032231A (en) * | 2023-08-10 | 2023-11-10 | 海南大学 | Multi-agent path planning method based on improved RRT |
Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110727272A (en) * | 2019-11-11 | 2020-01-24 | 广州赛特智能科技有限公司 | Path planning and scheduling system and method for multiple robots |
CN111638717A (en) * | 2020-06-06 | 2020-09-08 | 浙江科钛机器人股份有限公司 | Design method of distributed autonomous robot traffic coordination mechanism |
US20200338733A1 (en) * | 2019-04-24 | 2020-10-29 | X Development Llc | Robot motion planning |
CN112835364A (en) * | 2020-12-30 | 2021-05-25 | 浙江大学 | Multi-robot path planning method based on conflict detection |
CN113780633A (en) * | 2021-08-20 | 2021-12-10 | 西安电子科技大学广州研究院 | Complex environment-oriented multi-AGV intelligent cooperative scheduling method with real-time conflict resolution function |
CN114705194A (en) * | 2022-04-15 | 2022-07-05 | 中国农业大学 | Multi-agricultural-machinery cooperative global path conflict detection method based on topological map and time window |
CN114815832A (en) * | 2022-04-28 | 2022-07-29 | 西安交通大学 | Multi-agent over-the-horizon networking cooperative perception dynamic decision method based on point cloud |
-
2022
- 2022-09-21 CN CN202211147755.5A patent/CN115220461B/en active Active
Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20200338733A1 (en) * | 2019-04-24 | 2020-10-29 | X Development Llc | Robot motion planning |
CN110727272A (en) * | 2019-11-11 | 2020-01-24 | 广州赛特智能科技有限公司 | Path planning and scheduling system and method for multiple robots |
CN111638717A (en) * | 2020-06-06 | 2020-09-08 | 浙江科钛机器人股份有限公司 | Design method of distributed autonomous robot traffic coordination mechanism |
CN112835364A (en) * | 2020-12-30 | 2021-05-25 | 浙江大学 | Multi-robot path planning method based on conflict detection |
CN113780633A (en) * | 2021-08-20 | 2021-12-10 | 西安电子科技大学广州研究院 | Complex environment-oriented multi-AGV intelligent cooperative scheduling method with real-time conflict resolution function |
CN114705194A (en) * | 2022-04-15 | 2022-07-05 | 中国农业大学 | Multi-agricultural-machinery cooperative global path conflict detection method based on topological map and time window |
CN114815832A (en) * | 2022-04-28 | 2022-07-29 | 西安交通大学 | Multi-agent over-the-horizon networking cooperative perception dynamic decision method based on point cloud |
Non-Patent Citations (1)
Title |
---|
于赫年等: "仓储式多AGV系统的路径规划研究及仿真", 《计算机工程与应用》 * |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN117032231A (en) * | 2023-08-10 | 2023-11-10 | 海南大学 | Multi-agent path planning method based on improved RRT |
Also Published As
Publication number | Publication date |
---|---|
CN115220461B (en) | 2023-02-17 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Jin et al. | Platoon-based multi-agent intersection management for connected vehicle | |
Bevly et al. | Lane change and merge maneuvers for connected and automated vehicles: A survey | |
Lin et al. | Anti-jerk on-ramp merging using deep reinforcement learning | |
Wuthishuwong et al. | Vehicle to infrastructure based safe trajectory planning for Autonomous Intersection Management | |
Zhang et al. | A multiple mobile robots path planning algorithm based on A-star and Dijkstra algorithm | |
US20200294394A1 (en) | Joint Control of Vehicles Traveling on Different Intersecting Roads | |
Kala et al. | Multi-level planning for semi-autonomous vehicles in traffic scenarios based on separation maximization | |
US20100256852A1 (en) | Platoon vehicle management | |
CN112284393B (en) | Global path planning method and system for intelligent mobile robot | |
CN112525196B (en) | AGV route planning and scheduling method and system based on multidimensional data | |
CN114283607A (en) | Multi-vehicle collaborative planning method based on distributed crowd-sourcing learning | |
CN109115220B (en) | Method for parking lot system path planning | |
CN112562409A (en) | Autonomous parking system and method based on multi-access edge calculation | |
Guney et al. | Scheduling‐based optimization for motion coordination of autonomous vehicles at multilane intersections | |
CN115220461B (en) | Robot single system and multi-robot interaction cooperation method in indoor complex environment | |
Baskar et al. | Hierarchical traffic control and management with intelligent vehicles | |
Liu | A progressive motion-planning algorithm and traffic flow analysis for high-density 2D traffic | |
EP4291457A1 (en) | System, method, and computer program product for topological planning in autonomous driving using bounds representations | |
WO2022211932A1 (en) | Route planner and decision-making for exploration of new roads to improve map | |
US20220340177A1 (en) | Systems and methods for cooperative driving of connected autonomous vehicles in smart cities using responsibility-sensitive safety rules | |
Zhang et al. | Cavsim: A microscopic traffic simulator for evaluation of connected and automated vehicles | |
Esposto et al. | Hybrid path planning for non-holonomic autonomous vehicles: An experimental evaluation | |
Kala et al. | Multi-vehicle planning using RRT-connect | |
CN115638804B (en) | Deadlock-free unmanned vehicle online path planning method | |
CN115344049B (en) | Automatic path planning and vehicle control method and device for passenger boarding vehicle |
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 |