CN117452932A - Multi-robot distributed collaborative operation method and system - Google Patents
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Abstract
The invention relates to the technical field of intelligent control of robots, in particular to a multi-robot distributed collaborative operation method and system, comprising the following steps: performing three-dimensional modeling on a working scene; converting the three-dimensional physical model into a map model; setting up a simulation verification environment; obtaining a collision-free global operation planning path of each simulation robot; controlling each simulation robot to perform obstacle avoidance verification of static and dynamic obstacles to obtain simulation operation tracks of each simulation robot under collision-free control instructions; and performing difference comparison on the simulated operation track and the planned path, and performing parameter optimization and adjustment to finally obtain the multi-robot distributed collaborative operation method which can be applied to the real normalized operation scene. The invention aims to solve the problems that the prior art does not describe the robot working environment or describes the robot working environment accurately enough, and the verification of the multi-robot collaborative operation algorithm is independent of the actual operation environment and is difficult to be directly applied to the actual scene.
Description
Technical Field
The invention relates to the technical field of intelligent control of robots, in particular to a multi-robot distributed collaborative operation method and system.
Background
With the rapid development of intelligent control technology, more and more robots are put into use, and can replace human beings to execute detection, search and other work tasks in some special environments such as narrow pipelines and industrial production containers, and the work efficiency is remarkably improved. Compared with the robot single operation, the multi-robot distributed collaborative operation has stronger stability and practicability, so the research on the multi-robot distributed collaborative operation method is also focused.
However, related researches focus on the technical field of distributed cooperative control, mostly neglect modeling of the operation environment of a robot system, do not realize organic unification of the operation environment with distributed cooperative operation algorithm development and cooperative operation simulation verification, and have the problems that the operation environment of the robot is not described or described accurately enough, verification of a multi-robot cooperative operation algorithm is independent of an actual operation environment and is difficult to be directly applied to a real scene and the like. The normalized operation environment has the characteristic of relatively fixed scene, and has important practical application significance by carrying out more accurate mathematical description on the given robot operation environment and developing the research of the operation algorithm and the function verification method of the multi-robot system based on the mathematical description.
Disclosure of Invention
The invention carries out accurate mathematical description aiming at a normalized working environment, forms a multi-robot distributed collaborative working mechanism under the working environment, and verifies the working mechanism through a simulation means. And continuously correcting and iteratively improving an operation mechanism according to the verification conclusion, and finally providing an optimization scheme for actually carrying out multi-robot collaborative operation. Therefore, the problem that the prior art does not describe the robot working environment or describes the robot working environment accurately enough, and verification of the multi-robot collaborative operation algorithm is independent of the actual operation environment and is difficult to be directly applied to the actual scene is solved. The invention comprises the following specific contents:
a multi-robot distributed collaborative work method applied to a normalized work scene, the method comprising:
s1, carrying out three-dimensional modeling on the operation scene to obtain a three-dimensional physical model;
s2, converting the three-dimensional physical model into a map model;
s3, constructing a simulation verification environment by using a robot simulation simulator based on the map model; the simulation verification environment comprises a simulation operation scene and a simulation robot;
s4, path planning is carried out on each simulation robot in the simulation operation scene, and a collision-free global operation planning path is obtained;
S5, controlling each simulation robot to perform static obstacle avoidance and dynamic collision avoidance verification on obstacles in the simulation verification environment according to the collision-free global operation planning path to obtain a simulation operation track of each simulation robot under a collision-free control instruction;
and S6, performing difference comparison on the simulated operation track and the collision-free global operation planning path, and repeating the steps S4 and S5 to perform path planning and optimization and adjustment of motion control parameters to obtain the multi-robot distributed collaborative operation method which can be applied to a real normalized operation scene.
Further, the converting the three-dimensional physical model into a map model includes:
s21, translating or rotating the coordinate system of the three-dimensional physical model to obtain a converted three-dimensional physical model;
s22, generating a point cloud format model based on the converted three-dimensional physical model in a sampling mode;
s23, taking each point in the point cloud format model as the center, setting a side length value, generating a cube, and forming an initial map model;
s24, setting an expansion radius; and inserting new occupied obstacle points at positions with the distances equal to the expansion radius in the front, rear, left and right directions of each occupied obstacle point in the initial map model, so as to realize expansion of the initial map model and obtain the map model.
Further, the performing path planning on each simulation robot in the simulation verification environment to obtain a collision-free global operation planning path includes:
s41, initializing each simulation robot; inputting the map model and the serial numbers I, i=0, 1, …, I-1, I of each simulation robot for each simulation robot, wherein I is the number of the simulation robots; integrating keyPoints from a working track key point set of each simulation robot according to the simulation working scene and the working mode i Selecting the key points of the operation track n i ＝0,1,…,N i -1，N i For the number of said operational track key points of the ith said simulation robot, +.>An nth simulation robot i The key points of the operation track are selected;
s42, sequentially selecting the key points of the operation trackAnd->Obtaining a collision-free operation planning path between two adjacent points>
S43, planning a path for collision-free operation between the two adjacent pointsObtaining all key points of the operation track by head-to-tail connection>Is a complete planned path of (c) connectiedpath i ；
S44, connecting the path to the complete planning path i Performing track smoothness optimization to obtain a global path of the operation planning path of each simulation robot i The globalPath i The method comprises the following steps:
wherein the method comprises the steps ofPosition information of an mth planning path point in the planning path for the job,the method comprises the following steps:
m is more than or equal to 0 and less than or equal to M-1, M is the operationThe total number of planned path points in the planned path,coordinate values of the ith planning path point X, Y and Z of the ith simulation robot are respectively the coordinate values of the ith planning path point X, Y and Z of the ith simulation robot;
s45, after the track is smooth and optimized, starting from a starting point on a smooth curve, uniformly sampling points according to a set sampling step length, and sequentially storing each path point in the operation planning path globalPath according to the sampling sequence i In the method, the collision-free global job planning path Global Path is obtained i 。
Further, the key points of the operation track are selected in sequenceAnd->Obtaining a collision-free operation planning path between two adjacent points>Comprising the following steps:
s421, using the operation track key pointsAs root node, iterative generation of extended branch node by using prior random tree generation algorithm>For the expansion branch node of the K-th iteration, k=0, 1, …, K-1, K is the total number of the expansion branch nodes;
s422, detecting the expansion branch node by using an open source collision detection libraryWhether there is a collision with a static obstacle in the map model; if there is a collision, the current expansion branch node is abandoned +. >If there is no collision, the current expansion branch node is reserved +.>
S423, repeating the steps S421 and S422 until the extended tree reaches the operation track key pointOr the set maximum iteration number is reached;
s424, from the last expansion branch nodeStarting, sequentially backtracking to the root node according to the connection condition of the extension branch node>-passing each of said extended branch nodes +.>Sequentially connecting to obtain the collision-free operation planning path +.>
Further, according to the collision-free global operation planning path, controlling each simulation robot to perform static obstacle avoidance for an obstacle in the simulation verification environment and dynamic collision avoidance verification for the simulation robots, so as to obtain a simulation operation track of each simulation robot under a collision-free control instruction, including:
s51, inputting a control instruction update time interval delta t, delta t for each simulation robot i>0, and the collision-free global job planning path globalPath i ；
S52, setting initialization timet＝t 0 The method comprises the steps of carrying out a first treatment on the surface of the Acquiring position information and speed information of all initial states of the simulation robot i, and storing the position information and the speed information in a state vector robotStateVector t ，
For the state information of the simulation robot i at the time t, x t ,y t ,z t Respectively representing the position coordinate values of the simulation robot i in the X, Y and Z directions in the global coordinate system of the map model at the moment t, and twist_x t ,twist_y t ,twist_z t Respectively representing the speed values of the simulation robot i along the X, Y and Z directions of the global coordinate system at the moment t;
s53, using the state informationAnd the collision-free global job planning path globalPath i Obtaining a path point index number index corresponding to the local target point at the current t moment t ，0≤index t ≤M-1，For the local target point at the current moment, the path point index number index t The model of (2) is:
wherein:threshold is a set threshold value, threshold>0；
S54, according to the state vector robotStateVector t The map modelCalculating new speed twist_x of the simulation robot i capable of avoiding collision with static obstacles and other simulation robots at the next moment by using an optimal mutual obstacle avoidance algorithm and the open source collision detection library new 、twist_y new 、twist_z new ；
S55, calculating the position coordinate value and the speed value of each simulation robot i at the next moment:
twist x t+Δt ＝twist x new
twist_y t+Δt ＝twist_y new
twist_z t+Δt ＝twist_z new
updating the state informationAnd the state vector robotStateVector t ：
S56, updating t=t+Δt; repeating the steps S53, S54 and S55, and driving the simulation robot i to complete planning the path globalPath for the global job without collision i And obtaining the simulation operation track of the simulation robot i.
Further, the obstacle comprises setting a fault of the simulation robot to stay in situ to form the unexpected obstacle.
Further, the difference comparison comprises a deviation value of the simulated operation track and the collision-free global operation planning path, success rates of dynamic and static obstacle avoidance and smoothness of the simulated operation track.
Further, the method further comprises modifying the simulation robot number, the path planning parameters and the control instruction parameters by using a YAML file.
Further, the format of the three-dimensional physical model is stl or obj format.
A multi-robot distributed collaborative operation system, the system comprising:
the three-dimensional model format conversion module is used for converting the three-dimensional physical model of the operation scene into the map model;
the global operation path planning module is used for planning a collision-free global operation planning path from a starting point to a target point for each simulation robot;
the distributed motion control module is used for controlling the next moment speed and position of each simulation robot in the operation process of the map model in real time so that the simulation robots have the functions of avoiding collision and avoiding static obstacles;
The simulation verification module is used for building the simulation verification environment, verifying the feasibility of the multi-robot distributed collaborative operation method in the simulation verification environment by utilizing the three-dimensional model format conversion module, the global operation path planning module and the distributed motion control module, and performing iterative optimization on the path planning and control instruction parameters according to verification results to finally obtain the multi-robot distributed collaborative operation method which can be applied to a real normalized operation scene.
The beneficial effects of the invention are as follows:
(1) The real operation scene can be modeled by converting the normalized operation scene into a map model and constructing a simulation verification environment, so that basic data support is provided for verifying the feasibility of the multi-robot distributed collaborative operation method.
(2) And planning the collision-free global operation path of each simulation robot, so that a simulated planning path can be provided for the operation process of multiple robots.
(3) The collision-free control instruction calculated for each simulation robot realizes real-time obstacle avoidance control on the collaborative operation of multiple robots on the basis of a planned path, and a simulation operation track is obtained.
(4) The multi-robot distributed collaborative operation method applicable to the real normalized operation scene is finally obtained by performing difference comparison on the simulation operation track and the collision-free global operation planning path and performing iterative optimization on the path planning and control instruction parameters.
Through the multi-robot collaborative operation simulation verification aiming at the normalized operation scene, the finally obtained optimized verification method can be directly implemented into the real-scene multi-robot collaborative operation, so that the static and dynamic collision of the multi-robots in the real operation is avoided. Therefore, the problem that the prior art does not describe the robot working environment or describes the robot working environment accurately enough, and verification of the multi-robot collaborative operation algorithm is independent of the actual operation environment and is difficult to be directly applied to the actual scene is solved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other embodiments may be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is an overall workflow diagram of a multi-robot distributed collaborative work method;
FIG. 2 is a flow chart of a three-dimensional physical model construction and format conversion mechanism implementation of a multi-robot distributed collaborative work method and system;
FIG. 3 is a flow chart of a distributed collaborative operation mechanism implementation of a multi-robot distributed collaborative operation method and system;
FIG. 4 is a schematic diagram of the components of the simulation verification module and the relationship with other modules of the multi-robot distributed collaborative process method and system;
FIG. 5 is a diagram of the effect of format conversion of a columnar container three-dimensional physical model of a multi-robot distributed collaborative work method and system;
FIG. 6 is a graph of simulated operation trajectories of multiple robots in different environments and different modes of operation for a multi-robot distributed collaborative operation method and system;
FIG. 7 is a diagram of simulated operation trajectory effects of a multi-robot distributed collaborative process and system for simulating one simulated robot after the failure of another.
Fig. 8 is a construction diagram of a multi-robot distributed collaborative operation system.
In the figure: 1. a map model; 2. a simulation robot; 21. a simulation robot which works normally; 22. a simulation robot with faults; 3. a static obstacle; 4. and simulating the operation track.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, apparatus, article, or device that comprises a list of steps or elements is not limited to the list of steps or elements but may, in the alternative, include other steps or elements not expressly listed or inherent to such process, method, article, or device.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
Referring to fig. 1, fig. 1 is an overall workflow diagram of a multi-robot distributed collaborative operation method, and the multi-robot distributed collaborative operation method provided by the invention is applied to a normalized operation scene, and includes:
S1, carrying out three-dimensional modeling on the operation scene to obtain a three-dimensional physical model;
s2, converting the three-dimensional physical model into a map model;
s3, constructing a simulation verification environment by using a robot simulation simulator based on the map model; the simulation verification environment comprises a simulation operation scene and a simulation robot;
s4, path planning is carried out on each simulation robot in the simulation operation scene, and a collision-free global operation planning path is obtained;
s5, controlling each simulation robot to perform static obstacle avoidance and dynamic collision avoidance verification on obstacles in the simulation verification environment according to the collision-free global operation planning path to obtain a simulation operation track of each simulation robot under a collision-free control instruction;
and S6, performing difference comparison on the simulated operation track and the collision-free global operation planning path, and repeating the steps S4 and S5 to perform path planning and optimization and adjustment of motion control parameters to obtain the multi-robot distributed collaborative operation method which can be applied to a real normalized operation scene.
The normalized operation scene refers to a situation where the robot works in a relatively fixed place and the working environment is unchanged, for example, a container, a pipe, etc. with a fixed shape.
It should be noted that the three-dimensional physical model is a stl or obj-format three-dimensional physical model. The job scene is preferably modeled in three dimensions using software such as CAD, solidWorks.
It should be noted that, the robot simulation simulator preferably adopts Gazebo or UWSim simulator to construct a simulation verification environment based on a three-dimensional physical model, which comprises a simulation operation scene and a plurality of simulation robots.
In the step S5, the motion direction and the position of each simulation robot at the next moment are calculated in real time based on the position of each simulation robot and the map model according to the collision-free global operation planning path, and a control instruction is generated to control each simulation robot to perform static obstacle avoidance for an obstacle in the simulation verification environment and dynamic collision avoidance verification between the simulation robots, so as to obtain a simulation operation track of each simulation robot under the collision-free control instruction;
in the specific implementation, the real operation scene can be modeled by converting the normalized operation scene into a map model and constructing a simulation verification environment, so that basic data support is provided for verifying the feasibility of the multi-robot distributed collaborative operation method; the collision-free global operation path planning is carried out on each simulation robot, so that a simulated planning path can be provided for the operation process of multiple robots; the simulation operation track of each simulation robot under the collision-free control instruction is calculated in real time, so that real-time obstacle avoidance control on the cooperative operation of multiple robots on the basis of a planned path is realized, and the simulation operation track can be obtained; the multi-robot distributed collaborative operation method applicable to the real normalized operation scene is finally obtained by performing difference comparison on the simulation operation track and the collision-free global operation planning path and performing iterative optimization on the path planning and control instruction parameters.
Through the multi-robot collaborative operation simulation verification aiming at the normalized operation scene, the finally obtained optimized verification method can be directly implemented into the real-scene multi-robot collaborative operation, so that the static and dynamic collision of the multi-robots in the real operation is avoided. Therefore, the problem that the prior art does not describe the robot working environment or describes the robot working environment accurately enough, and verification of the multi-robot collaborative operation algorithm is independent of the actual operation environment and is difficult to be directly applied to the actual scene is solved.
In the embodiment provided by the present invention, please refer to fig. 2 and fig. 5, fig. 2 is a flowchart for implementing a three-dimensional physical model construction and format conversion mechanism of a normalized operation environment of a multi-robot distributed collaborative operation method, fig. 5 is a diagram for implementing a three-dimensional physical model format conversion effect of a columnar container of the multi-robot distributed collaborative operation method and system, and the substep of the step S2 includes:
s21, translating or rotating the coordinate system of the three-dimensional physical model to obtain a converted three-dimensional physical model;
it should be noted that, since the origin or coordinate axis of the coordinate system of the three-dimensional physical model may be changed during the drawing and deriving of the three-dimensional physical model, the coordinate system of the stl or obj format three-dimensional physical model needs to be translated or rotated, and the conversion mode may preferably be translated or rotated by the meilab tool.
S22, generating a point cloud format model based on the converted three-dimensional physical model in a sampling mode;
it should be noted that, the PCD point cloud format model is preferably generated by using the PCD command line tool pcl_mesh2PCD of the point cloud library to sample the converted three-dimensional physical model; the sampling density is controlled by specifying a parameter leaf_size.
It should be noted that, the file format corresponding to PCD (english full name: point Cloud Date, chinese translated name: point Cloud data) is PCL official specified format, and has two data storage types of ASCII and binary, and the PCD format has a file header for describing the overall information of the Point Cloud.
S23, taking each point in the point cloud format model as the center, setting a side length value, generating a cube, and forming an initial map model;
it is preferable that the PCD point cloud format model is converted into the map model in octree format by using an Octomap library.
It should be noted that, the Octomap is a three-dimensional map creation tool based on octree, which can display a complete 3D graph including a non-obstructed area and a non-mapped area, and the sensor data based on the occupancy grid can be fused and updated in multiple measurements, and the map can provide multiple resolutions, can be data-compressed, and is compact in storage. The octree format is a compressed map format which can conveniently inquire information of whether any position is an obstacle or not, has adjustable resolution and can be used for navigation and obstacle avoidance.
In the step S21, when converting from the PCD format to the octree format, each point of the point cloud may be translated or rotated and then inserted into the octree map, thereby realizing the transformation of the three-dimensional physical model coordinate system of the working environment.
S24, setting an expansion radius; and inserting new occupied obstacle points at positions with the distances equal to the expansion radius in the front, rear, left and right directions of each occupied obstacle point in the initial map model, so as to realize expansion of the initial map model and obtain the map model.
It should be noted that the octree map is composed of small cubes, the size of the cubes is the resolution of the map, and each cube corresponds to a center point. Occupying an obstacle point is a term of art in an octree map, a point occupied indicating that the cube is an obstacle region, and a point unoccupied indicating that the cube is a viable region. New points are arranged in front of, behind, left of and right of each occupied barrier point according to the expansion radius, which is a process of expanding the barrier, is a processing mode for improving safety, and can effectively reduce the probability of collision; fig. 5 is a view showing a conversion effect of the map model of the work environment by taking a columnar container as an example.
In the specific implementation, the three-dimensional physical model is converted into the expanded octree map model, so that the obstacle in the working environment and the part within a certain distance around the obstacle can be identified, and a safe and collision-free basic guarantee is provided for the cooperative operation of multiple robots.
In a specific embodiment, as shown in fig. 3, fig. 3 is a flowchart of a distributed collaborative operation mechanism implementation of a multi-robot distributed collaborative operation method and system, and the sub-steps of the step S4 include:
s41, initializing each simulation robot; inputting the map model and the serial numbers I, i=0, 1, …, I-1, I of each simulation robot for each simulation robot, wherein I is the number of the simulation robots; integrating keyPoints from a working track key point set of each simulation robot according to the simulation working scene and the working mode i Selecting the key points of the operation track n i ＝0,1,…,N i -1，N i For the number of said operational track key points of the ith said simulation robot, +.>An nth simulation robot i The key points of the operation track are selected;
it should be noted that, the job track key point set keyPoints i Is selected manually according to specific working environment and robot working mode, for example, the working environment is cylindrical pipeline, the robot working mode is circular motion operation, then the selected working track key point set key points are selected i It is possible to select a section of the pipe at regular intervals along the axial direction on the inner wall of the cylindrical pipe, and select a plurality of points along the circumferential direction on the inner wall of the section of the pipe.
It should be noted that the number of the substrates,storedIs the coordinate value of each operation track key point in the map model.
S42, sequentially selecting the key points of the operation trackAnd->Obtaining a collision-free operation planning path between two adjacent points>
It should be noted that the number of the substrates,is->To->A collection of points in between.
S43, planning a path for collision-free operation between the two adjacent pointsObtaining all key points of the operation track by head-to-tail connection>Is a complete planned path of (c) connectiedpath i ；
S44, connecting the path to the complete planning path i Performing track smoothness optimization to obtain a global path of the operation planning path of each simulation robot i The globalPath i The method comprises the following steps:
wherein the method comprises the steps ofPosition information of an mth planning path point in the planning path for the job,the method comprises the following steps:
m is more than or equal to 0 and less than or equal to M-1, M is the total number of planned path points in the operation planned path,coordinate values of the ith planning path point X, Y and Z of the ith simulation robot are respectively the coordinate values of the ith planning path point X, Y and Z of the ith simulation robot;
it should be noted that the complete planned path connectiedpath is preferably performed by using a B-spline curve i The trajectory smoothness is optimized, and the higher the number of times of the B-spline curve is, the better the curve smoothness is, but the calculation amount becomes larger and the approximation degree becomes worse, preferably 2 times or 3 times.
By connecting the path to the complete planning path i Operation planning path globalPath obtained by track smooth optimization i The robot is a smoother curve, so that the actual operation of the robot is more convenient, and the running track of the robot is smoother.
S45, after the track is smooth and optimized, starting from a starting point on a smooth curve, uniformly sampling points according to a set sampling step length, and sequentially storing each path point in the operation planning path globalPath according to the sampling sequence i In the method, the collision-free global job planning path Global Path is obtained i 。
In one embodiment of the present invention, as shown in fig. 3, fig. 3 is a flowchart of a distributed collaborative operation mechanism implementation of a multi-robot distributed collaborative operation method and system, and the sub-steps of the step S42 include:
S421，with the operation track key pointsAs root node, an extended branch node +/is iteratively generated using a priori random tree generation algorithm (description explanation)>For the expansion branch node of the K-th iteration, k=0, 1, …, K-1, K is the total number of the expansion branch nodes;
It should be noted that, the priori random tree generation algorithm, english is: the inform-RRT is an algorithm for rapidly expanding a random tree (RRT, rapidly-exploring random tree) based on a sampling method, and compared with a traditional path planning algorithm (such as a, D, etc.), the method can plan a path more quickly and flexibly, and does not require the regularity of the shape of an obstacle; however, since the iterative process of the RRT is not targeted, the obtained path is unstable and has a large deviation from the optimal path. Therefore, the Informated-RRT algorithm improves the parent node selection mode, inherits RRT probability completeness and has progressive optimality, so that the quality of a planned path is improved, but the consumption time is longer. For this reason, the inform-RRT algorithm is based on a mathematical idea that "the sum of the distances from the points on the ellipse to the two foci of the ellipse is the same, and the sum of the distances from the points outside the ellipse to the two foci is greater than the sum of the distances from the points on the ellipse to the two foci, and the points in the ellipse are opposite" to optimize the sampling space, so that a feasible path with higher quality can be obtained in a shorter time. The main idea of the inform-RRT algorithm is: taking the shortest path which is searched at present as cbest, taking the distance between the starting point and the end point as cmin, constructing an ellipse for sampling, and converging the shortest path into a straight line (when no obstacle exists) when cbest is continuously reduced, so that the searching range is greatly reduced.
S422, detecting the expansion branch node by using an open source collision detection libraryWhether to interact with static obstacles in the map modelThe object has collision; if there is a collision, the current expansion branch node is abandoned +.>If there is no collision, the current expansion branch node is reserved +.>
It should be noted that, open source collision detection library, english is: FCL (Flexible Collision Library), providing collision detection between objects; in the implementation mode, a Informand-RRT algorithm and an open source collision detection library FCL are used for realizing path planning between a current point and a target point, branches are iteratively generated from the current point by utilizing the Informand-RRT algorithm, and FCL is used for detecting whether a map model simulating a robot and a corresponding octree format in the branch growth direction collides or not, if so, the path is not included, so that static obstacle avoidance is realized.
S423, repeating the steps S421 and S422 until the extended tree reaches the operation track key pointOr the set maximum iteration number is reached;
It should be noted that, when the branch node and the operation track key point are grown from the expanded treeWhen the distance between the two is smaller than the set threshold value, the expanded tree is considered to reach the key point of the operation track +.>Is set in the threshold range of (a).
S424, from the last expansion branch nodeStarting, sequentially backtracking to the root node according to the connection condition of the extension branch node>-passing each of said extended branch nodes +.>Sequentially connecting to obtain the collision-free operation planning path +.>
It should be noted that, in the above-mentioned process S424, namely, the collision-free planning points detected and planned by the inform-RRT algorithm and the open source collision detection library FCL are connected into one path, that is, the set of points, so as to obtain the collision-free operation planning path
In one embodiment of the present invention, as shown in fig. 3 and fig. 4, fig. 3 is a flowchart of a distributed collaborative operation mechanism implementation of a multi-robot distributed collaborative operation method and system, fig. 4 is a schematic diagram of a simulation verification module component and other module relationship of the multi-robot distributed collaborative operation method and system, and the sub-steps of the step S5 include:
s51, inputting a control instruction update time interval delta t, delta t for each simulation robot i >0, and the collision-free global job planning path GlobalPath i ；
S52, setting an initialization time t=t 0 The method comprises the steps of carrying out a first treatment on the surface of the Acquiring position information and speed information of all initial states of the simulation robot i, and storing the position information and the speed information in a state vector robotStateVector t ，
For the state information of the simulation robot i at the time t, x t ,y t ,z t Respectively representing the position coordinate values of the simulation robot i in the X, Y and Z directions in the global coordinate system of the map model at the moment t, and twist_x t ,twist_y t ,twist_z t Respectively representing the speed values of the simulation robot i along the X, Y and Z directions of the global coordinate system at the moment t;
s53, using the state informationAnd the collision-free global job planning path GlobalPath i Obtaining a path point index number index corresponding to the local target point at the current t moment t ，0≤index t ≤M-1，For the local target point at the current moment, the path point index number index t The model of (2) is:
wherein:threshold is a set threshold value>0；/>
Note that Δt represents a time interval, index t Indicating the index number of the route point corresponding to the local target point calculated at the time t, wherein the local target point is the job planning route globalPath i The serial number to be reached by the simulation robot at the next moment in the process is index t Points of (i), i.e Wherein:the coordinate values of the local target points X, Y and Z directions obtained by calculation at the moment t are obtained. Local target point->Is an important input, and index is carried out along with the movement of a robot t Is updated continuously, i.e.)>Continuously updating until->Global job planning path with collision-free global i Is>Post-registration index t Then no longer update (at this time index t ＝M-1)。
S54, according to the state vector robotStateVector t The map modelCalculating a new speed 1-degree twist_x of the simulation robot i capable of avoiding collision with static obstacles and other simulation robots at the next moment by using an optimal mutual obstacle avoidance algorithm and the open source collision detection library new 、twist_y new 、twist_z new ；
It should be noted that, the best mutual obstacle avoidance algorithm, english is: ORCA (Optimal Reciprocal Collision Avoidance), which is a classical distributed underlying obstacle avoidance algorithm, dynamically avoiding other robots in the process of moving to a target point by a plurality of robots, and searching for an optimal path to move to the target point. Compared with the global path planning algorithms such as the RRT, the ORCA is local navigation, and the navigation target is around the robot individual, so that the individual can avoid other individual targets and static obstacles close to the individual. ORCA can be complementary to the Informed-RRT routing: on the one hand, the Informand-RRT is a global path-finding algorithm, a global path from the current position of the robot to a target point can be found, and the global information of the Informand-RRT algorithm has obstacle information of the whole environment but does not have the ability of sensing the specific states of all robot individuals and other surrounding individual information, so that the Informand-RRT algorithm cannot avoid collision among individuals, which is just the problem solved by ORCA; on the other hand, the ORCA only senses the situation close to the surroundings of the ORCA, has no global environment information, can only realize navigation without collision with other surrounding individual targets and static obstacle barriers, but cannot find the shortest path between the starting point and the target point of the ORCA, and is just the problem solved by Informated-RRT path finding.
S55, calculating the position coordinate value and the speed value of each simulation robot i at the next moment:
twist x t+Δt ＝twist x new
twist_y t+Δt ＝twist_y new
twist_z t+Δt ＝twist_z new
updating the state informationAnd the state vector robotStateVector t ：
S56, updating t=t+Δt; repeating the steps S53, S54 and S55, and driving the simulation robot i to complete the global job planning path Global Path without collision i And obtaining the simulation operation track of the simulation robot i.
In one embodiment, as shown in fig. 7, fig. 7 is an effect diagram of simulating a sudden failure of a simulation robot in a simulation verification module of a multi-robot distributed collaborative operation method and system, where the obstacle includes setting a fault of the simulation robot to stay in situ to form the obstacle that is unexpected.
It should be noted that, in the process of cooperative operation of multiple robots, accidents will not be avoided, for example, a robot will fail and stay in place, and at this time, the failed robot will be converted from a dynamic obstacle to a static obstacle, so that it is necessary to manually set this situation in the method for detecting the feasibility of the method; as can be seen in fig. 7, the failed simulation robot 22 stays in place and is converted from a dynamic obstacle to a static obstacle, and the normal operation simulation robot 21 can smoothly bypass the failed simulation robot 22 and continue to operate normally.
In one embodiment, in the step S6, the following steps: performing difference comparison on the simulation operation track and the collision-free global operation planning path; the difference comparison comprises an offset value of the simulated operation track and the collision-free global operation planning path, success rates of dynamic and static obstacle avoidance and smoothness of the simulated operation track.
It should be noted that, the above-mentioned deviation value, success rate of dynamic and static obstacle avoidance and smoothness of the simulated operation track may be set as thresholds, the simulated operation track is compared with each point in the collision-free global operation planning path, and if the deviation value exceeds the set thresholds, the above-mentioned steps S4 and S5 are repeated to perform path planning and optimization and adjustment of motion control parameters.
It should be noted that, as shown in fig. 6, fig. 6 is a graph of the effects of the simulation operation track of multiple robots under different environments and different operation modes of the multi-robot distributed collaborative operation method and system; the left side of the figure is a map model 1 of a columnar container, the inside of the map model is not provided with a protruding static obstacle 3, and the right side of the map model is a columnar container with the static obstacle 3; the operation modes of the simulation robot 2 in the left and right drawings are different. From the figure, it can be seen that the form of the simulated work trajectory 4 of the three simulation robots 2 on the left side and the form of the simulated work trajectory 4 of the simulation robot on the right side over the static obstacle 3.
In one embodiment, the method further comprises modifying the simulated robot number, path planning parameters, and control instruction parameters using a YAML file.
It should be noted that the syntax of YAML language is similar to other high-level languages, and data forms such as manifest, hash table, scalar, etc. can be simply expressed. The method uses blank symbol indentation and a large amount of appearance-dependent characteristics, and is particularly suitable for expressing or editing data structures, various configuration files, debugging contents and file schemas. The YAML language can thus be utilized as an editable file for modification, setting and adjustment of various parameters in the present invention.
In one embodiment, the three-dimensional physical model is in stl or obj format.
It should be noted that the stl file format (english: stereolithtography) is originally used for the file format of the STereoLithography computer aided design software. Many packages of software support this format, which is widely used for rapid prototyping, 3D printing and Computer Aided Manufacturing (CAM). The stl file describes only the surface geometry of a three-dimensional object, without color, texture maps, or other attributes of a common three-dimensional model. The stl file format is simple, only describes the geometric information of the three-dimensional object, does not support information such as color materials and the like, and is the most common file format supported by computer graphics processing CG, digital geometric processing such as CAD and digital geometric industrial application such as a three-dimensional printer; and thus are relatively suitable for the technical requirements of the present solution.
Triangulation of the surface then causes the 3D model to appear polyhedral. The selection of parameters for outputting stl files can affect the molding quality. So if the stl archive is rough or polyhedral you will see a real response on the model.
It should be noted that, the obj file format is a 3D model file format, and does not include information such as animation, material characteristics, map paths, dynamics, particles, and the like, and mainly supports a polygon (Polygons) model, supports more than three points of surfaces, and many other model file formats only support three points of surfaces, so that the imported model is often triangulated, which is very unfavorable for reprocessing the model, and therefore, an obj file format may be used for model modeling of the scheme.
As shown in fig. 8, fig. 8 is a component structure diagram of a multi-robot distributed collaborative operation system, the system includes:
the three-dimensional model format conversion module is used for converting the three-dimensional physical model of the operation scene into the map model;
it should be noted that, the module can obtain corresponding octree maps according to different working environments, and realize a three-dimensional model construction and conversion mechanism of the multi-robot normalized working environment.
The global operation path planning module is used for planning a collision-free global operation planning path from a starting point to a target point for each simulation robot;
it should be noted that, the module may obtain a collision-free global working path for each robot according to different working modes.
The distributed motion control module is used for controlling the next moment speed and position of each simulation robot in the operation process of the map model in real time so that the simulation robots have the functions of avoiding collision and avoiding static obstacles;
the module can realize the cooperative motion control of the distributed robots, drive the robots to spread according to a planned path, and automatically avoid obstacles and avoid mutual influence in the motion process; the global operation path planning module and the distributed motion control module form a multi-robot distributed collaborative operation mechanism together.
The simulation verification module is used for building the simulation verification environment, verifying the feasibility of the multi-robot distributed collaborative operation method in the simulation verification environment by utilizing the three-dimensional model format conversion module, the global operation path planning module and the distributed motion control module, and performing iterative optimization on the path planning and control instruction parameters according to verification results to finally obtain the multi-robot distributed collaborative operation method which can be applied to a real normalized operation scene.
It should be noted that, the module can construct a simulation verification environment comprising a working environment and a plurality of robot platforms, and verify the validity of the multi-robot distributed working method facing the normalized working environment.
From the above detailed description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product that may be stored in a computer-readable storage medium including Read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), one-time programmable Read-Only Memory (OTPROM), electrically erasable programmable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disc Memory, magnetic disc Memory, tape Memory, or any other medium that can be used for computer-readable carrying or storing data.
Finally, it should be noted that: the embodiment of the invention discloses a multi-robot distributed collaborative operation method, which is only disclosed as a preferred embodiment of the invention, and is only used for illustrating the technical scheme of the invention, but not limiting the technical scheme; although the invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme recorded in the various embodiments can be modified or part of technical features in the technical scheme can be replaced equivalently; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (10)
1. A multi-robot distributed collaborative operation method, applied to a normalized operation scene, the method comprising:
s1, carrying out three-dimensional modeling on the operation scene to obtain a three-dimensional physical model;
s2, converting the three-dimensional physical model into a map model;
s3, constructing a simulation verification environment by using a robot simulation simulator based on the map model; the simulation verification environment comprises a simulation operation scene and a simulation robot;
s4, path planning is carried out on each simulation robot in the simulation operation scene, and a collision-free global operation planning path is obtained;
S5, controlling each simulation robot to perform static obstacle avoidance and dynamic collision avoidance verification on obstacles in the simulation verification environment according to the collision-free global operation planning path to obtain a simulation operation track of each simulation robot under a collision-free control instruction;
and S6, performing difference comparison on the simulated operation track and the collision-free global operation planning path, and repeating the steps S4 and S5 to perform path planning and optimization and adjustment of motion control parameters to obtain the multi-robot distributed collaborative operation method which can be applied to a real normalized operation scene.
2. The multi-robot distributed collaborative process according to claim 1, wherein the converting the three-dimensional physical model into a map model comprises:
s21, translating or rotating the coordinate system of the three-dimensional physical model to obtain a converted three-dimensional physical model;
s22, generating a point cloud format model based on the converted three-dimensional physical model in a sampling mode;
s23, taking each point in the point cloud format model as the center, setting a side length value, generating a cube, and forming an initial map model;
S24, setting an expansion radius; and inserting new occupied obstacle points at positions with the distances equal to the expansion radius in the front, rear, left and right directions of each occupied obstacle point in the initial map model, so as to realize expansion of the initial map model and obtain the map model.
3. The multi-robot distributed collaborative work method according to claim 1, wherein the performing path planning for each of the simulation robots in the simulation verification environment to obtain a collision-free global work planned path includes:
s41, initializing each simulation robot; inputting the map model and the serial numbers I, i=0, 1, …, I-1, I of each simulation robot for each simulation robot, wherein I is the number of the simulation robots; integrating keyPoints from a working track key point set of each simulation robot according to the simulation working scene and the working mode i Selecting the key points of the operation track N i For the number of said operational track key points of the ith said simulation robot, +.>An nth simulation robot i The key points of the operation track are selected;
s42, sequentially selecting the key points of the operation trackAnd->Obtaining a collision-free operation planning path between two adjacent points
S43, planning a path for collision-free operation between the two adjacent pointsObtaining all key points of the operation track by head-to-tail connection>Is a complete planned path of (c) connectiedpath i ；
S44, connecting the path to the complete planning path i Performing track smoothness optimization to obtain a global path of the operation planning path of each simulation robot i The globalPath i The method comprises the following steps:
wherein the method comprises the steps ofPosition information of an mth planning path point in the planning path for the job,the method comprises the following steps:
m is more than or equal to 0 and less than or equal to M-1, M is the total number of planned path points in the operation planned path,coordinate values of the ith planning path point X, Y and Z of the ith simulation robot are respectively the coordinate values of the ith planning path point X, Y and Z of the ith simulation robot;
s45, after the track is smooth and optimized, starting from a starting point on a smooth curve, uniformly sampling points according to a set sampling step length, and sequentially storing each path point in the operation planning path globalPath according to the sampling sequence i In the method, the collision-free global job planning path Global Path is obtained i 。
4. A multi-robot distributed collaborative process according to claim 3, wherein the sequence selects the operational track keypointsAnd->Obtaining a collision-free operation planning path between two adjacent points >Comprising the following steps:
s421, using the operation track key pointsAs root node, iterative generation of extended branch node by using prior random tree generation algorithm> For the expansion branch node of the K-th iteration, k=0, 1, …, K-1, K is the total number of the expansion branch nodes;
s422, detecting the expansion branch node by using an open source collision detection libraryWhether there is a collision with a static obstacle in the map model; if there is a collision, the current expansion branch node is abandoned +.>If there is no collision, the current expansion branch node is reserved +.>
S423, repeating the steps S421 and S422 until the extended tree reaches the operation track key pointOr the set maximum iteration number is reached;
s424, from the last expansion branch nodeStarting, sequentially backtracking to the root node according to the connection condition of the extension branch node>-passing each of said extended branch nodes +.>Sequentially connecting to obtain the collision-free operation planning path +.>
5. The multi-robot distributed collaborative operation method according to claim 3, wherein controlling each simulation robot to perform static obstacle avoidance for an obstacle in the simulation verification environment and dynamic collision avoidance verification for each simulation robot according to the collision-free global operation planning path, obtaining a simulation operation track of each simulation robot under a collision-free control instruction, comprises:
S51, inputting a control instruction update time interval delta t, delta t for each simulation robot i>0, and the collision-free global job planning path globalPath i ；
S52, setting an initialization time t=t 0 The method comprises the steps of carrying out a first treatment on the surface of the Acquiring position information and speed information of all initial states of the simulation robot i, and storing the position information and the speed information in a state vector robotStateVector t ，
For the state information of the simulation robot i at the time t, x t ,y t ,z t Respectively representing the position coordinate values of the simulation robot i in the X, Y and Z directions in the global coordinate system of the map model at the moment t, and twist_x t ,twist_y t ,twist_z t Respectively representing the speed values of the simulation robot i along the X, Y and Z directions of the global coordinate system at the moment t;
s53, using the state informationAnd the collision-free global job planning path globalPath i Obtaining a path point index number index corresponding to the local target point at the current t moment t ，0≤index t ≤M-1，/>For the local target point at the current moment, the path point index number index t The model of (2) is:
wherein:threshold is a set threshold value>0；
S54, according to the state vector robotStateVector t The map modelCalculating new speed twist_x of the simulation robot i capable of avoiding collision with static obstacles and other simulation robots at the next moment by using an optimal mutual obstacle avoidance algorithm and the open source collision detection library new 、twist_y new 、twist_z new ；
S55, calculating the position coordinate value and the speed value of each simulation robot i at the next moment:
twist x t+Δt ＝twist x new
twist_y t+Δt ＝twist_y new
twist_z t+Δt ＝twist_z new
updating the state informationAnd the state vector robotStateVector t ：
S56, updating t=t+Δt; repeating the steps S53, S54 and S55, and driving the simulation robot i to complete planning the path globalPath for the global job without collision i And obtaining the simulation operation track of the simulation robot i.
6. The multi-robot distributed collaborative work method according to claim 5, wherein the obstacle includes setting a fault of one of the simulation robots to remain in place to form the obstacle that is unexpected.
7. The multi-robot distributed collaborative work method according to claim 1, wherein the difference comparisons include a deviation value of the simulated work trajectory from the collision-free global work planning path, success rates of dynamic and static obstacle avoidance, and smoothness of the simulated work trajectory.
8. The multi-robot distributed collaborative work method according to claim 1, further comprising modifying the simulated robot quantity, path planning parameters, and control instruction parameters using a YAML file.
9. The multi-robot distributed collaborative process according to claim 1, wherein the three-dimensional physical model is in stl or obj format.
10. A multi-robot distributed collaborative work system, characterized in that it utilizes the multi-robot distributed collaborative work method of any of claims 1-8, the system comprising:
the three-dimensional model format conversion module is used for converting the three-dimensional physical model of the operation scene into the map model;
the global operation path planning module is used for planning a collision-free global operation planning path from a starting point to a target point for each simulation robot;
the distributed motion control module is used for controlling the next moment speed and position of each simulation robot in the operation process of the map model in real time so that the simulation robots have the functions of avoiding collision and avoiding static obstacles;
the simulation verification module is used for building the simulation verification environment, verifying the feasibility of the multi-robot distributed collaborative operation method in the simulation verification environment by utilizing the three-dimensional model format conversion module, the global operation path planning module and the distributed motion control module, and performing iterative optimization on the path planning and control instruction parameters according to verification results to finally obtain the multi-robot distributed collaborative operation method which can be applied to a real normalized operation scene.
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