CN114019912A - Group robot motion planning control method and system - Google Patents
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Abstract
The invention relates to a swarm robot motion planning control method and a swarm robot motion planning control system. Compared with the prior art, the method has the advantages of low complexity, high accuracy, low cost and the like of motion planning.
Description
Technical Field
The invention relates to the field of robot path planning control, in particular to a group robot motion planning control method and system.
Background
With the progress of science and technology and the development of society, the level of robot intelligence and automation gradually increases, and the robot has gradually penetrated into daily life. The robot can excellently complete tasks in industry and daily families, and labor burden of people is reduced. The robot needs to plan a path from an initial position to a target position in a working scene, and the path should meet a series of requirements of short path, high efficiency, high safety and the like, and must be capable of avoiding static and dynamic obstacles along the path. Meanwhile, the robot has certain computing power to compute the shortest and safest route in real time so as to save time and reserve energy. Good robot path planning techniques may not only save significant time, but may also reduce wear and capital investment in the robot. The path planning of the mobile robot has important application value, and becomes a research hotspot at home and abroad. Particularly in the industry, the motion planning control of group robots is more complex, so that a novel motion planning method is needed to complete various manufacturing tasks in the industry, which plays an important role in the motion planning tasks of group robots.
The existing group robot control generally distributes tasks in a cloud server, and path planning control and obstacle avoidance of a specific robot are realized by a microprocessor of the robot. The group of robots sense environmental information by means of respective sensors and radars of the robots, tasks of each robot are distributed by the cloud server, the robots are in various communication, and path planning control is performed by the microprocessor; in the prior art, a corresponding path planning method is difficult to design aiming at different motion scenes, so that the adaptability of group robots to different scenes is not strong; in addition, the robot can not move when no instruction is given, and the existing group robot path planning algorithm is difficult to meet different conditions.
At present, the existing group robot control has the following defects:
1) because the current swarm robot realizes path planning by means of a microprocessor of the current swarm robot, when the algorithm of the path planning is complex, the path is complex and the obstacles are too many, the computational power of the processor is stressed, the phenomenon of dead halt in the motion process is likely to occur, and if the computational power of the processor is insufficient, the motion of the robot is likely to be discontinuous;
2) the cloud server is only used for task allocation, which is too wasteful;
3) the group robots have various motion scenes, and a corresponding path planning method needs to be adopted according to different conditions;
4) when the robots avoid obstacles, the processor pressure is too high due to the fact that the robots communicate with each other too frequently;
5) the path planning algorithm cannot aim at the running conditions of all group robots, only the group robots move when instructions are issued, and at present, a motion control algorithm when no instructions are issued does not exist.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a group robot motion planning control method and system with low complexity, high accuracy and low cost of motion planning.
The purpose of the invention can be realized by the following technical scheme:
according to the first aspect of the invention, a method for controlling group robot motion planning is provided, the method places path planning and obstacle avoidance processing processes on a cloud server for processing, selects a corresponding path planning algorithm for various motion scenes of the group robot, adopts a cloud platform control module to directly communicate with a target robot, and controls the target robot to move to a target point according to a planned path.
Preferably, the motion scene includes a brownian motion scene, a queue decision scene, an automatic charging scene, and a human-computer interaction scene.
Preferably, the control process of the brownian motion scene is as follows:
the swarm robots are started from a sleep state, and the initial state is that a swarm of robots are gathered at a fixed position; the cloud platform control module determines the motion priority of each robot, plans a path for each robot and sends a motion instruction to the robot body; the robot body is analyzed through the motion instruction, and the robot is driven to move; during the movement process, the robot judges whether the robot touches an obstacle or not at any moment during the movement process, and if the robot touches the obstacle, the cloud platform control module replans a path for the robot to avoid the obstacle;
and when the group robots do not receive the control instruction, carrying out motion control by adopting an obstacle avoidance and random motion algorithm.
Preferably, the obstacle avoidance and random motion algorithm includes simple linear motion, obstacle detection, and obstacle avoidance operations, and specifically includes:
the robot body performs simple linear motion before encountering an obstacle, and performs obstacle avoidance operation when judging that the robot body will encounter the obstacle in the next period in obstacle detection, bounces off the obstacle and then continues to perform linear motion until encountering the next obstacle;
the obstacle detection operation is: the method comprises the steps of defining the motion range of group robots as a two-dimensional plane, establishing a coordinate system to mathematically abstract all obstacles, and discretizing irregular interfaces of all the obstacles into a series of line segment sets, so that collision conditions of a robot body and the obstacles are simplified into inequality judgment.
Preferably, the obstacles include fixed site obstacles and other robot body obstacles, and the corresponding obstacle avoiding operation is that:
fixing a place obstacle, and performing pseudo-elastic collision action, namely, the velocity component of the robot body in the direction vertical to the incident plane is reverse, and the parallel velocity component is unchanged;
and other robot body obstacles are processed according to the motion priority of the robot body.
Preferably, the control process of the queue decision scenario is as follows:
when the robot receives a specific external signal, the signal is transmitted to the cloud platform control module; the cloud platform control module executes a queue decision algorithm in the background, calculates an optimal path for each robot, sends a motion instruction to the robots and drives the robots to be arranged into a preset shape.
Preferably, the control objective of the queue decision algorithm is to allocate a target point according to a given queue, implement the queue in the shortest time, and make the path distance as short as possible, and the algorithm specifically includes the following steps:
step S1, acquiring obstacle information, queue information and real-time coordinates of the target robot, and initializing;
s2, planning an obstacle avoidance path, starting from a starting point, selecting a node to avoid an obstacle until reaching a target point; distributing target points according to weight based on the acquired path node set and the corresponding path length, and obtaining corresponding target point coordinates and corresponding paths by each robot;
wherein the weight is a first order weight based on path length, and is represented as a first neighboring target point of the robot; if the two weights are the same, then the second order weight is considered, and so on.
Preferably, the control process of the automatic charging scenario is as follows:
when the electric quantity of the robot body is lower than a preset electric quantity value, the cloud platform control module sends a charging instruction to the robot when detecting the condition, plans a path for the robot at the background and gives a higher motion priority to the robot, and the robot can smoothly return to the charging pile for charging.
Preferably, the control process of the human-computer interaction scene is as follows:
when a person enters the venue, the cloud platform control module acquires the position of the target person in the venue and assigns one or more robots near the person to interact with the person.
According to a second aspect of the present invention, a system based on the group robot motion planning method is provided, and the system includes group robots, a cloud server module for performing path planning and obstacle avoidance processing, and a cloud platform control module for implementing direct communication between a cloud server and target robots.
Compared with the prior art, the invention has the following advantages:
1) the invention provides a swarm robot motion planning algorithm, which adopts a cloud server to carry out path planning and obstacle avoidance processing at a cloud end, and after the result is obtained, the swarm robots are directly communicated with a target robot, so that the communication among the robots is reduced, a large amount of real-time information is not required to be transmitted and received among the swarm robots, and a better synergistic effect can be achieved among the swarm robots; the operation pressure of a microprocessor is reduced, the hardware cost is reduced, the robot only needs to pay attention to control, and the problems of complex motion planning, difficult control and overhigh hardware cost of the current group of robots are effectively solved;
2) the existing group robot motion planning method does not aim at various motion scenes, and different path planning methods are selected through a designed cloud platform control module aiming at different motion scenes of the group robot, so that the complexity of the group robot motion planning is reduced;
3) the invention designs a queue decision algorithm aiming at a reasonable path of a target to be calculated in robot path planning, and the distance of the robot relative to the target can be calculated through the algorithm, so that the effect of moving the minimum path is achieved, and the accuracy of the path motion of the group robots and the high efficiency of obstacle avoidance are improved;
4) the invention designs a random movement and obstacle avoidance algorithm, so that the group robots can perform random movement through the algorithm under the condition of no instruction issuing, and the high efficiency and the safety of avoiding obstacles are realized;
5) according to the invention, various motion scenes of the group robots are designed in one cloud platform control module, and the cloud platform control system corresponds to various path planning methods, and can be expanded after aiming at the motion scenes, so that the cloud platform control system has advantages for industrial production.
Drawings
FIG. 1 is a flow chart of the group robot control under different motion scenarios according to the present invention;
FIG. 2 is a flow chart of an algorithm implementation of the present invention;
FIG. 3 is a flow chart of a path planning algorithm;
fig. 4 is a schematic diagram of planning an obstacle avoidance path;
fig. 5 is a flow chart of the random motion and obstacle avoidance algorithm.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
Examples
In order to solve the problems of difficult control of group robot motion planning, and the like, the embodiment provides a group robot motion planning control method and system. For the problem that the motion scenes of the group robots are complex, a corresponding path planning algorithm is adopted for various motion scenes based on a cloud platform control module; aiming at the problem of path planning of group robots, a queue decision algorithm is adopted, so that the movement path distance of the robots is as short as possible, the movement of a plurality of robots is coordinated, and the problem of collision between the robots and obstacles is solved.
An embodiment of the system of the present invention is first presented below.
A swarm robot motion planning control system comprises swarm robots, a cloud server module used for path planning and obstacle avoidance processing, and a cloud platform control module used for realizing direct communication between a cloud server and a target robot.
The method of the present invention will be described in detail below with reference to the accompanying drawings.
1. Scene design in cloud platform control module
As shown in fig. 1, there are four main scenarios of the software-controlled group robot, which are:
1.1 Brownian motion scenes
The swarm robots are started from a sleep state, and the initial state is that a swarm of robots are gathered at a fixed position;
the cloud platform control module determines the motion priority of each robot, plans a path for each robot and then sends a motion instruction to the robot body;
the robot body is analyzed through the motion instruction, and the robot is driven to move;
the robot constantly judges whether an obstacle is encountered in the movement process, and if the obstacle is encountered, the cloud platform control module replans a path for the robot to avoid the obstacle;
the whole robot group does disordered movement in the venue, and is similar to Brownian movement;
1.2 queue decision scenarios
When the robot receives a specific external signal, the signal is transmitted to the cloud platform control module;
and the software executes a queue decision algorithm in the background, calculates an optimal path for each robot, sends the motion instruction to the robots and drives the robots to be arranged into a preset shape.
1.3 automatic charging scenario
When the electric quantity of the robot body is lower than 20%, the software sends a charging instruction to the robot when detecting the situation, plans a path for the robot in the background and gives a higher motion priority to the robot; meanwhile, other robots appearing on the path give way to the robot, and the robot can smoothly return to the charging pile for charging;
1.4 human-computer interaction scenarios
When a person enters the venue, the software obtains the position of the target person in the venue and assigns one or more robots in the vicinity of the person to interact with the person through algorithmic analysis.
2. Queue decision algorithm
The implementation flow chart of the algorithm is shown in fig. 2, and the queue decision algorithm aims at: according to a given queue, distributing a target point, realizing the queue in the shortest time and enabling the path distance to be as short as possible; the problem of collision between the robot and the barrier is solved; the motion of a plurality of robots is coordinated, and collision among the robots is ensured not to occur.
The algorithm is premised on that: the group robots do not interfere with each other in movement at the starting point and the ending point; collisions between robots are caused by path intersection, and collisions caused by paths which are parallel and close enough are not considered for the moment; in a static environment, there are no other dynamic obstacles except the target robot.
Defining an algorithm model input set:
the initial set of coordinates for the N robots is: penguin [ N ]]＝{(x1,y1),(x2,y2),...,(xN,yN)}，
The N target point coordinate sets for implementing the queue are:
the set of obstacle boundary line segments Obstalles and the set of vertical line segments Verticals are (X)min,Xmax,slope,intercept)。
As shown in fig. 3, a path node set LineNN [ num1] [ num2]. Nodes [ ] (obstacle avoidance path Nodes from the num1 robot to the num2 target point) and Distancebetween (LineNN [ num1] [ num2]) (corresponding path lengths) are obtained through a queue planning algorithm, the target points are allocated according to weights, and each robot acquires the coordinates of the target points and the corresponding paths.
As shown in fig. 3, the calculation process of the algorithm specifically includes:
firstly, planning an obstacle avoidance path: the obstacles are expanded according to the safety distance and are approximated to be polygons, and the vertexes of the polygons are taken as vertical lines to touch other obstacles or boundaries to be cut off.
Considering the path between the num1 robot and the num2 target point, starting from the starting point, the node is selected to avoid the obstacle until the target point, wherein the node selection is performed according to the following rules.
Determining the ith robot (x)j,yj) And the jth target point (e)xj,eyj) The straight line is an initial route, and coordinates (x, y) of upper points of the straight line satisfy the following conditions:
obtaining an obstacle avoidance path node set of a num1 robot and a num2 target point (each blank dot coordinate in fig. 4, a black dot is a target point): LineN [ num1] [ num2]. Nodes [ ], connecting each point, sequentially calculating the Distance between adjacent points and accumulating to obtain the Distance between the robot and the target point under the path (LineN [ num1] [ num2]), wherein the expression is as follows:
D＝∑di
if d isijFor the distance between the i-th robot (i.e., num1) and the target point j (i.e., num2), all distance information matrices can be represented by the following matrices:
calculating first-order weights of the rows: qi＝dmin(the row minimum, i.e. the first neighbor target point of the robot);
sequentially distributing target points according to the first-order weight value, if the target points are the same, considering the second-order weight (the second smallest value in the row, namely the second neighbor target point of the robot), and so on; once per allocation, the corresponding column is deleted (masked).
Target point assignment is completed, robot presses LineNN num1][num2].Nodes[]Each node proceeds to the target point, considering the case of path intersection. At any time t, c (t) represents the coordinate position of each robot, and after dt times, according to the current speed and direction: if robot i and other robot(s) are going to the same area, they are sequenced through according to priority (waiting on site if priority is low), priority depends on tiSize; if the front area is the target point of other robots and the robot arrives at the static state, the road is bypassed.
And after the queue planning is finished, the robot moves to a target point according to the set path.
3. Obstacle avoidance and random motion algorithm
The random motion refers to the autonomous motion of the machine body in the state of not receiving the instruction of the control platform, and is designed into a non-sequence motion mode similar to Brownian motion. The whole process flow chart of random motion and obstacle avoidance is shown in fig. 5. The body carries out linear motion before meeting the barrier, when judging that the next movement cycle will meet the barrier, carries out obstacle avoidance action, bounces off from the barrier and then continues linear motion until meeting the next barrier.
The triggering condition and the termination condition of the random motion are respectively as follows: receiving a dismissal (free motion) command from the console; and receiving other motion commands of the console, such as queuing, nesting and the like.
The whole movement process is divided into three parts of simple linear movement, obstacle detection and obstacle avoidance.
The linear motion is realized simply, and independent modules are not needed.
And spreading the two parts of obstacle detection and obstacle avoidance. And establishing a two-dimensional coordinate plane, and performing mathematical abstraction on the range of the motion of the machine body, the group robots and the obstacles. Aiming at various irregular curved surfaces related to obstacles, the interfaces of all the obstacles are discretized into a series of line segment sets, and each line segment expression is as follows:
y＝ax+b,x∈[xmin,xmax]。
and simplifying the collision condition of the robot body and the obstacle into inequality judgment. The robot body i approaches a line segment y from a certain direction, wherein the line segment y is ax + b, and x belongs to [ x ∈ [ x ]min,xmax]When a collision occurs, the inequality yi＞axiThe value of + b will flip over, thus locating a crash event.
Next, avoiding obstacles, wherein the robot body can touch two kinds of obstacles in the movement process, namely a fixed site obstacle and other robot bodies;
for fixed field obstacles, a pseudo-elastic collision action is performed, i.e. the velocity component v of the body in the direction perpendicular to the incident plane⊥The reverse, parallel component v//The change is not changed;
for other robot bodies, when collision occurs with other robot bodies, a motion priority variable is additionally introduced to indicate that some robot bodies have higher 'right of way' under special conditions, including for example, battery exhaustion homing, or the robot bodies are about to reach a specified position when queuing.
The motion priority has the following three conditions, respectively:
the movement priority is smaller than the movement priority of the other side, and the other side waits for a movement period in situ;
the motion priority is greater than the motion priority of the other side, and the straight line advances;
the priority of movement being equal to the priority of movement of the other party, performing a pseudo-elastic collision, i.e. a component of velocity v perpendicular to the tangential plane of the collision⊥The reverse, parallel component v//Remain unchanged.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A swarm robot motion planning control method is characterized in that a path planning and obstacle avoidance processing process is put on a cloud server for processing, a corresponding path planning algorithm is selected according to various motion scenes of the swarm robot, a cloud platform control module is adopted to directly communicate with a target robot, and the target robot is controlled to move to a target point according to a planned path.
2. The method of claim 1, wherein the motion scenes comprise brownian motion scenes, queue decision scenes, automatic charging scenes, and human-computer interaction scenes.
3. The swarm robot motion planning control method according to claim 2, wherein the control process of the brownian motion scene is as follows:
the swarm robots are started from a sleep state, and the initial state is that a swarm of robots are gathered at a fixed position; the cloud platform control module determines the motion priority of each robot, plans a path for each robot and sends a motion instruction to the robot body; the robot body is analyzed through the motion instruction, and the robot is driven to move; during the movement process, the robot judges whether the robot touches an obstacle or not at any moment during the movement process, and if the robot touches the obstacle, the cloud platform control module replans a path for the robot to avoid the obstacle;
and when the group robots do not receive the control instruction, carrying out motion control by adopting an obstacle avoidance and random motion algorithm.
4. The swarm robot motion planning control method of claim 3, wherein the obstacle avoidance and stochastic motion algorithm comprises simple linear motion, obstacle detection and obstacle avoidance operations, and specifically comprises:
the robot body performs simple linear motion before encountering an obstacle, and performs obstacle avoidance operation when judging that the robot body will encounter the obstacle in the next period in obstacle detection, bounces off the obstacle and then continues to perform linear motion until encountering the next obstacle;
the obstacle detection operation is: the method comprises the steps of defining the motion range of group robots as a two-dimensional plane, establishing a coordinate system to mathematically abstract all obstacles, and discretizing irregular interfaces of all the obstacles into a series of line segment sets, so that collision conditions of a robot body and the obstacles are simplified into inequality judgment.
5. The swarm robot motion planning control method of claim 4, wherein the obstacles comprise fixed site obstacles and other robot body obstacles, and the corresponding obstacle avoidance operation is:
fixing a place obstacle, and performing pseudo-elastic collision action, namely, the velocity component of the robot body in the direction vertical to the incident plane is reverse, and the parallel velocity component is unchanged;
and other robot body obstacles are processed according to the motion priority of the robot body.
6. The method according to claim 2, wherein the control process of the queue decision scenario is as follows:
when the robot receives a set external signal, the signal is transmitted to the cloud platform control module; the cloud platform control module executes a queue decision algorithm in the background, calculates an optimal path for each robot, sends a motion instruction to the robots and drives the robots to be arranged into a preset shape.
7. The swarm robot motion planning control method of claim 6, wherein the control objective of the queue decision algorithm is to allocate a target point according to a given queue, to realize the queue in the shortest time and to make the path distance as short as possible, and the algorithm specifically comprises the following steps:
step S1, acquiring obstacle information, queue information and real-time coordinates of the target robot, and initializing;
s2, planning an obstacle avoidance path, starting from a starting point, selecting a node to avoid an obstacle until reaching a target point; distributing target points according to weight based on the acquired path node set and the corresponding path length, and obtaining corresponding target point coordinates and corresponding paths by each robot;
wherein the weight is a first order weight based on path length, and is represented as a first neighboring target point of the robot; if the two weights are the same, then the second order weight is considered, and so on.
8. The group robot motion planning control method according to claim 2, wherein the control process of the automatic charging scenario is as follows:
when the electric quantity of the robot body is lower than a preset electric quantity value, the cloud platform control module sends a charging instruction to the robot when detecting the condition, plans a path for the robot at the background and gives a higher motion priority to the robot, and the robot can smoothly return to the charging pile for charging.
9. The group robot motion planning control method of claim 2, wherein the control process of the human-computer interaction scene is as follows:
when a person enters the venue, the cloud platform control module acquires the position of the target person in the venue and assigns one or more robots near the person to interact with the person.
10. The system of the group robot motion planning control method based on claim 1 is characterized by comprising the group robots, a cloud server module for path planning and obstacle avoidance processing, and a cloud platform control module for realizing direct communication between a cloud server and target robots.
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