CN115220461B - Robot single system and multi-robot interaction cooperation method in indoor complex environment - Google Patents
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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 multiple robots execute tasks in the same scene, the system paralysis caused by the conflict of task routes may occur, and the conflict problems include pursuit conflict, opposite conflict, node conflict 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 very 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 prediction cannot be completely accurate, and after the robot cannot arrive at a corresponding position at a preset time on time, the time prediction type prediction and the speed control cannot effectively perform task management on the robot, so that route conflicts of multiple robots occur, 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 the current running states of all robots 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 routes of multiple robots conflict;
(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 amount 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 is easy to happen, 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 comprising: 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, the current man-flow change is large, the passage is narrow, the route needs to be avoided, the multi-robot cooperation efficiency is low, and the task path conflict rate is high in the indoor general scene.
The robot single system under the indoor complex environment comprises a robot cluster consisting of a plurality of robots, wherein 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 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.
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 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 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.
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 preferable scheme is as follows:
the self-running state in the step S3 includes a robot number, a current position, a path interest point being executed, and a 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 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 robot collision avoidance strategy in step S4 includes a non-intersection node collision decision mechanism and an intersection node collision 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 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.
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 and 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 a dense road network structure map, 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 the calculation efficiency and reducing the 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 path 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 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.
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 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 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 interest point of the path, and the point attribute (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, intersection nodes (key conflict 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.
As shown in fig. 3, a black polygon frame is a 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 publishing basic information: robot number, current location, point of interest of the path being performed, 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 indexes have different functions in evaluation, and objective differences among the indexes are reflected; 3. the reliability degrees of the indexes are different, and the reliability degrees of the information provided by the indexes are 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 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 drive 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, 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 relationship with the self-robot, determining whether the intersection node has only one side moving straight, determining that the self-robot is close to the intersection node, and determining a weight of the self-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.
Claims (7)
1. The multi-robot interactive cooperation method under the indoor complex environment is characterized by comprising a robot cluster consisting of a plurality of robots, wherein 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 unit, a cooperation 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 to realize the state information communication between the robots;
further 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 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 path 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.
2. The multi-robot interactive collaboration method in indoor complex environment as claimed in claim 1, 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.
3. The multi-robot interactive collaboration method in indoor complex environment as claimed in claim 1, wherein: 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.
4. The multi-robot interactive collaboration method in indoor complex environment as claimed in claim 1, 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.
5. The multi-robot interactive collaboration method in indoor complex environment as claimed in claim 1, wherein: the robot collision avoidance strategy in step S4 includes a non-intersection node collision decision mechanism and an intersection node collision decision mechanism.
6. The multi-robot interactive collaboration method in indoor complex environment as claimed in claim 5, wherein: 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 drive 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, 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.
7. The multi-robot interactive collaboration method in indoor complex environment as claimed in claim 5, 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; 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.
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