CN116627127A - Multi-mobile robot collaborative exploration method under unknown environment - Google Patents

Multi-mobile robot collaborative exploration method under unknown environment Download PDF

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CN116627127A
CN116627127A CN202310507646.8A CN202310507646A CN116627127A CN 116627127 A CN116627127 A CN 116627127A CN 202310507646 A CN202310507646 A CN 202310507646A CN 116627127 A CN116627127 A CN 116627127A
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exploration
mobile robot
algorithm
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unknown environment
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陈超
黄明熙
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Jiangsu University of Science and Technology
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G05D1/0238—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors
    • G05D1/024—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors in combination with a laser
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

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  • Automation & Control Theory (AREA)
  • Optics & Photonics (AREA)
  • Electromagnetism (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

The invention discloses a multi-mobile robot collaborative exploration method under an unknown environment, which comprises the following steps: n mobile robots are dispersed in different areas of an unknown environment to cooperatively work, data sharing is carried out among the N mobile robots, and a segmentation strategy is adopted to quickly traverse an unknown environment map so as to finish exploration; after each mobile robot determines an exploration target point, taking the current position of the mobile robot as a starting point, and adopting an A-type algorithm to complete global shortest path planning from each mobile robot to the exploration target point by taking the exploration target point as an end point; according to the invention, each mobile robot is distributed to different areas for exploration by adopting a segmentation strategy, so that the robots are prevented from being gathered in the same area, after a certain area is explored, the area robots are distributed to areas with fewer exploration, and the area exploration is further divided until the exploration of the unknown environment area is completed, so that the efficiency is improved; in addition, data sharing is carried out among different robots, and unknown environment exploration can be more effectively completed.

Description

Multi-mobile robot collaborative exploration method under unknown environment
Technical Field
The invention relates to the robot technology, in particular to a multi-mobile robot collaborative exploration method under an unknown environment.
Background
Along with the continuous updating of the robot technology and the continuous expansion of the requirements of the application field, higher requirements are put forward on the working capacity, stability, robustness and efficiency of the robot. A single robot is a comprehensive individual of sensing, processing, controlling, and executing, but is not adequate to handle some tasks that are complex, collaborative, and efficient. The multi-robot system has a plurality of advantages compared with a single robot system, improves the capability of executing complex tasks in a cooperative mode, and can provide a more effective solution; the multi-robot system can generally complete tasks in a shorter time, so that the system performance is improved; the multi-robot system has higher fault tolerance and robustness in the face of some single-point faults; the multi-robot system has better scalability in facing new functional needs.
Autonomous robotic exploration is an important research topic in the robot field. For the difference of the autonomous exploration purposes of the robot, exploration strategies in different directions are also sequentially proposed. In order to complete the specified task in the unknown environment, the mobile robot is required to have the capability of autonomously searching the target object, so that the robot not only needs the functions of path planning, obstacle avoidance, self positioning and the like, but also has the capability of searching and traversing the whole area and detecting and identifying the target object on line.
Disclosure of Invention
The invention aims to: the invention aims to provide a multi-mobile robot collaborative exploration method in an unknown environment so as to solve the problems in the background technology.
The technical scheme is as follows: the invention discloses a multi-mobile robot collaborative exploration method under an unknown environment, which comprises the following steps:
n mobile robots are dispersed in different areas of an unknown environment to cooperatively work, data sharing is carried out among the N mobile robots, and a segmentation strategy is adopted to quickly traverse an unknown environment map so as to finish exploration; after each mobile robot determines an exploration target point, taking the current position of the mobile robot as a starting point, and adopting an A-type algorithm to complete global shortest path planning from each mobile robot to the exploration target point by taking the exploration target point as an end point;
the method for quickly traversing the unknown environment map by adopting the segmentation strategy and completing exploration comprises the following steps: each mobile robot scans the surrounding environment, builds a local grid map of the current environment, transmits the local grid map to a controller database, and synchronously transmits the local grid map of each mobile robot to other mobile robots by the controller; meanwhile, in the process of constructing the local grid map by the mobile robots, the controller realizes the positioning of each mobile robot through a positioning algorithm; the controller maps the environment according to N local grid maps constructed by N mobile robots to obtain an unknown environment map; the controller divides the unknown environment area into N areas, and each mobile robot corresponds to one area; each mobile robot adopts an RRT exploration algorithm to explore the area where the mobile robot is located, and sends an exploration result to the controller in real time, and data sharing is carried out among the mobile robots; if the exploration of a certain region is completed, the controller distributes the mobile robots completing the region to unexplored regions according to the exploration conditions of each region, and further divides the unexplored regions into sub-regions according to the region division method, and the mobile robots continue to explore the sub-regions where the mobile robots are located by adopting an RRT exploration algorithm until all the region exploration is completed, namely the exploration of the unknown environment is realized, and an unknown environment map is obtained.
Further, a Gapping algorithm is adopted to construct a local grid map of the current environment.
Furthermore, the controller adopts an adaptive Monte Carlo positioning AMCL algorithm for adaptively adjusting the number of the particle samples based on the KL distance algorithm to realize the positioning of the robot;
the AMCL algorithm is improved on the basis of the MCL algorithm, and comprises the following steps: the AMCL algorithm estimates the weight w from the long term slow And short-term estimation weights w fast Judging whether the robot is kidnapped or not, if w fast Inferior to w slow The robot is recovered from the kidnapping by adding random particles in the resampling, the expression is:
wherein w is avg Representing the average weight of all particles, parameter α slow And alpha fast The attenuation rate of the exponential filter is equal to or less than 0 and equal to or less than alpha, and the attenuation rate is equal to or less than alpha when long-term estimation and short-term estimation are respectively averaged slow ≤α fast ；
The AMCl resampling stage adopts a KLD algorithm to adaptively adjust the particle number, and the particle number is expressed as:
where ε represents the error between the true and estimated distributions, z 1-δ Represents the 1-delta quantile on the standard normal distribution, k represents the non-vacancy of the histogram, and the upper limit M of the particle number top Linear relation with non-vacancy k; in the initial phase of global localization, a very large number of samples are needed, M being the greater the k top The higher; when global positioning is completed, the problem is converted into a position tracking problem, and particles are concentrated near the real position, k becomes less, and M top Lowering; in this way, the number of particles is dynamically adjusted.
Further, the RRT exploration algorithm specifically includes:
(1) Setting an initial point xinit and a target point xgold to set a state sampling space M by itself;
(2) Randomly sampling to obtain a sampling point xrands, and if the sampling point xrands is in the obstacle, re-randomly sampling;
(3) If the node is not in the obstacle, calculating the distance between the sampling point xrnd and all nodes in the generated node set tau to obtain the nearest node xnear, moving from the node xnear to the node xrnd in a step size StepSize, generating a new node xnew, and if the line Ei between the xnear and the xnew passes through the obstacle, randomly sampling again; wherein 0< stepsize <1;
(4) And (3) generating a random expansion tree through repeated iteration, and completing exploration of an unknown region in the random expansion tree when child nodes in the random expansion tree enter a specified target region.
Further, the method for completing global shortest path planning from the mobile robot to the target point by adopting an A-algorithm comprises the following steps:
the algorithm A combines the advantages of the rapidity and the breadth-first search BFS algorithm of the Dijkstra algorithm; the cost function of the a algorithm is shown as follows:
f(n)＝g(n)+h(n)
where f (n) represents the total cost from the start point a to the target point through the node n (x, y), g (n) represents the movement cost from the start point a to the node n (x, y), and the calculation formula is as follows:
wherein parent node of parent (n) is n, dist (n) is distance between starting point A and n node; h (n) represents a cost estimate from the starting point a to the target point, the presence of h (n) causes the algorithm to be directional when searching, taking the euclidean distance as shown in the following equation:
h(n)＝dist(n,B)
wherein, the point B is a target point, and the implementation of the A-algorithm requires maintaining two sets, namely a set OPEN and a set
All points which are generated and not accessed are stored in a CLOSE set OPEN, and the points which are accessed are stored in the CLOSE set; in the grid map, the algorithm A adopts eight neighborhood directions to search the surrounding, and the node with the minimum cost function in the feasible adjacent area is found, so that the node searches the surrounding feasible area until the target node is in the set CLOSE or the OPEN set is empty; the robot moves along the generated path to the target location.
Further, the mobile robot is a two-wheeled differential mobile robot, a laser radar and a PC (personal computer) supporting an ROS (reactive oxygen species) operating system are mounted on the mobile robot, the laser radar is used for scanning the surrounding environment, and the PC is used for realizing information interaction between the corresponding mobile robot and a controller; the controller is connected with the PC computers of the mobile robots through a wireless network based on the communication mode among the ROS nodes.
In another embodiment of the present invention, a multi-mobile robot collaborative exploration system in an unknown environment of the present invention includes:
the exploration module is used for realizing that N mobile robots are dispersed in different areas of an unknown environment to cooperatively work, carrying out data sharing among the N mobile robots, and rapidly traversing the unknown environment map by adopting a segmentation strategy to finish exploration; comprising the following steps: each mobile robot scans the surrounding environment, builds a local grid map of the current environment, transmits the local grid map to a controller database, and synchronously transmits the local grid map to other mobile robots; meanwhile, in the process of constructing the local grid map by the mobile robots, the controller realizes the positioning of each mobile robot through a positioning algorithm; the controller maps the environment according to N local grid maps constructed by N mobile robots to obtain an unknown environment map; dividing an unknown environment area into N areas by the positioning of each mobile robot, dividing grids of the unknown area into N parts according to the number of the grids, and enabling each mobile robot to correspond to one area; each mobile robot adopts an RRT exploration algorithm to explore the area where the mobile robot is located, and sends an exploration result to the controller in real time, and data sharing is carried out among the mobile robots; if the exploration of a certain region is completed, the controller distributes the mobile robot of the region to the exploration less region according to the exploration conditions of each region, and further divides the exploration less region into subareas according to the region dividing method, and the mobile robot continues to explore the subareas where the mobile robot is located by adopting an RRT exploration algorithm until all the region exploration is completed, namely the exploration of an unknown environment is realized, and an unknown environment map is obtained;
and the path planning module is used for completing global shortest path planning from the mobile robot to the target point by adopting an A-algorithm.
Preferably, the plurality of mobile robots are two-wheeled differential mobile robots, on which a laser radar and a PC computer are mounted, the PC computer supporting an ROS operating system.
In yet another embodiment of the present invention, an apparatus device comprises a memory and a processor, wherein:
a memory for storing a computer program capable of running on the processor;
and the processor is used for executing the steps of the multi-mobile robot collaborative exploration method in the unknown environment when the computer program is run.
In yet another embodiment of the present invention, a storage medium has stored thereon a computer program which, when executed by at least one processor, implements the steps of a multi-mobile robot collaborative exploration method in an unknown environment as described above.
The beneficial effects are that: compared with the prior art, the invention has the remarkable technical effects that: 1. the multiple robots are distributed in different areas by using the segmentation strategy, so that the robots are prevented from being gathered in the same area, the time for exploring all areas by the robots can be effectively reduced, and the exploring efficiency is improved; 2. the improved exploration efficiency further optimizes the energy consumption, since when the time is reduced it means that the robots need less time to turn on, which results in them using less energy; 3. the multi-mobile robots are used, data sharing is carried out among the multi-mobile robots, the working efficiency is improved, the node type distribution mode is adopted, more complex tasks are completed by a plurality of robots with lower performance, and the task cost is saved.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of a mobile robot of the present invention;
FIG. 3 is a schematic diagram of the system architecture of the present invention;
FIG. 4 is a mobile robot positioning flow chart of the present invention;
FIG. 5 is a flowchart of a segmentation strategy algorithm of the present invention;
fig. 6 is a flowchart of a mobile robot search RRT algorithm of the present invention.
Detailed Description
The present invention will be described in detail below by way of specific embodiments with reference to the accompanying drawings. It should be noted that the description of these examples is for aiding in understanding the present invention, but is not intended to limit the present invention.
According to the multi-mobile robot collaborative exploration method under the unknown environment, a cyclic segmentation strategy is adopted to quickly traverse the unknown environment map, so that exploration is completed; the method comprises the following steps: each mobile robot scans the surrounding environment, builds a local grid map of the current environment, transmits the local grid map to a controller database, and synchronously transmits the local grid map to other mobile robots; meanwhile, in the process of constructing the local grid map by the mobile robots, the controller realizes the positioning of each mobile robot through a positioning algorithm; the controller maps the environment according to N local grid maps constructed by N mobile robots to obtain an unknown environment map; the controller divides the unknown environment area into N areas, grids of the unknown area are divided into N parts according to the number of the grids, and each mobile robot corresponds to one area; each mobile robot adopts an RRT exploration algorithm to explore the area where the mobile robot is located, and sends an exploration result to the controller in real time, and data sharing is carried out among the mobile robots; if the exploration of a certain region is completed, the controller distributes the mobile robots of the completed region to unexplored regions according to the exploration conditions of each region, and further divides the unexplored regions into sub-regions according to the region division method, and the mobile robots continue to explore the sub-regions where the mobile robots are located by adopting an RRT exploration algorithm until all the region exploration is completed, namely the exploration of the unknown environment is realized, and an unknown environment map is obtained. And completing global shortest path planning from the mobile robot to the target point by adopting an A-scale algorithm. As shown in fig. 1, the method specifically comprises the following steps:
step 1, constructing a map;
as shown in fig. 2 and 3, the system includes a plurality of mobile robots and a controller, the mobile robots are two-wheeled differential mobile robots, on which a laser radar and a PC computer supporting an ROS operation system are mounted, and the controller in the whole system frame is connected with the PC computers of the mobile robots through a wireless network based on the communication mode between ROS nodes.
N mobile robots scattered in different areas of an unknown environment finish parameter configuration at a controller end and run Gmopping function packages, each mobile robot scans surrounding environments by using laser radars carried by the mobile robots, builds a local grid map of the current environment in a pc computer of the mobile robots, transmits the local grid map to the controller, and synchronously transmits the local grid map to other mobile robots. Gmaging is a SLAM algorithm for completing two-dimensional grid map construction by using RBPF algorithm based on 2D laser radar. The Gapping function package subscribes to the depth information, the IMU information and the odometer information of the robot, and simultaneously completes the configuration of some necessary parameters, namely, a probability-based two-dimensional grid map, namely, a local grid map, can be created and output.
Step 2, robot positioning, namely, in the moving process of the mobile robot, a controller adopts an adaptive Monte Carlo positioning (AMCL) algorithm for adaptively adjusting the number of the particle samples based on a KL distance (KLD) algorithm to realize the pose of the mobile robot relative to a world coordinate system, namely, realize the positioning of the mobile robot;
the positioning on the local grid map includes the robot position (x, y) and the orientation angle θ in the state space. The MCL algorithm estimates posterior information of the robot pose according to existing data, and the available data are of two types, namely measurement data and mileage data. Representation of robot motion p (x) using odometer motion model i |x t-1, u t ) Using a likelihood domain model of the laser to represent the robot perception p (z i |x t ) Confidence η bel (x i |z 1:t, u 1:t ) Indicating that all past measured data z are integrated at time t 1:t And control data u 1:t Related pose x of (2) t The posterior probability of (a) is expressed as:
η bel (x i |z 1:t, u 1:t )∝p(z i |x t )·∫[p(x i |x t-1, u t )·p(x t-1 |z 1:t-1 ,u 1:t-1 )]dx t-1 ,
wherein, the liquid crystal display device comprises a liquid crystal display device,representing the estimated pose of the robot at the moment t, and updating x through motion model sampling when mileage data is received t ；/>The weight of the particles is represented, and when new measurement data is observed, the particles are weighted according to the measurement model. MCL represents the posterior through a set of weighted particles, however, the MCL algorithm does not solve the robot kidnapping problem and positioning fails once pose changes are discontinuous. In order to improve positioning accuracy, a large amount of particles needs to be added, which slows down the positioning convergence speed.
The AMCL algorithm is improved on the basis of MCL based on long-term estimated weights w slow And short-term estimation weights w fast Judging whether the robot is kidnapped or not, if w fast Inferior to w slow The robot is recovered from the kidnapping by adding random particles in the resampling, the expression is:
wherein w is avg Representing the average weight of all particles, parameter α slow Inferior to alpha fast The decay rate (0. Ltoreq.alpha.) of the exponential filter averaged over the long-term and short-term estimates, respectively slow ≤α fast )。
The AMCl resampling stage adopts a KLD algorithm to adaptively adjust the particle number, and the particle number can be expressed as:
where ε represents the error between the true and estimated distributions, z 1-δ Represents the 1-delta quantile on the standard normal distribution, k represents the non-vacancy of the histogram, and the upper limit M of the particle number top In linear relation to non-vacancy k. In the initial phase of global localization, a very large number of samples are needed, M being the greater the k top The higher. When global positioning is completed, the problem is converted into a position tracking problem, and particles are concentrated near the real position, k becomes less, and M top And (3) lowering. In this way, dynamic tuningThe number of whole particles is increased, thereby improving the calculation efficiency of the algorithm.
The steps of robot positioning are shown in fig. 4:
2.1 initializing particles: taking the initial position of the mobile robot as the center of a particle set, randomly generating a certain number of particles, wherein the position and the direction of each particle are random;
2.2 motion model: and updating the position and the direction of the particles according to the motion data of the mobile robot. The motion model may be a simple model, such as a linear motion or a rotational motion, or a complex model, such as a kinematic model or a kinetic model.
2.3 calculating particle weights: calculating the weight of each particle according to the measurement data of the robot sensor;
2.4KLD resampling: designed to control the above-mentioned particle redundancy. Such as in a grid map, to see how many grids the particles occupy. Much more, the particles are shown to be very dispersed, with a higher upper limit on the number of particles allowed at each iteration of resampling. Less, meaning that particles have been concentrated, the upper limit is set low and this number is sampled. Resampling the particle set, the resampled particle set being closer.
2.5 updating robot position: and calculating the position and the direction of the robot on the map according to the particle set after the KLD resampling.
And 3, searching unknown areas by using a searching algorithm of a segmentation strategy by the robot.
For the efficient robot exploration area, after loading a local environment map in a pc (personal computer) of the robot, uploading the local environment map to a controller, wherein the controller tries to map the environment according to the local map of each mobile robot to construct a global environment map, the controller divides the global environment map into N blocks, grids of an unknown area are evenly divided into N parts according to the number of the grids, each block of the grids goes to one robot to be explored, and the position robots of each area share each other; when a regional robot finishes searching, the controller segments unexplored regions again, the assigned robot goes to the nearest unknown region searching, and the going robot finishes searching the unknown region by using a boundary searching algorithm; information sharing among all robots is convenient for the robots to know the detected areas; when the robot finishes the exploration of the unknown region, the controller performs the segmentation of the unknown region again, dispatches the nearest mobile robot to explore, and always completes the exploration of the unknown region in a circulating way.
As shown in fig. 5, the system first attempts to map the environment based on the local grid map generated by the mobile robot in initial step 1; the controller maps the environment according to N local grid maps constructed by N mobile robots to obtain an unknown environment map; in the exploration process, any one mobile robot receives information of the detection area completed by other mobile robots sent by the controller. All mobile robots can be assisted in knowing the detected area by a data transmission process between the controller and the mobile robot.
The robot uses RRT algorithm to complete the exploration of unknown regions, the idea of RRT is to quickly expand a group of tree-like paths to explore most regions of space, finding a viable path. The RRT algorithm is an algorithm for randomly sampling the state space, and by collision detection of sampling points, the large calculation amount brought by accurate modeling of the space is avoided, and the path planning problem of high-dimensional space and complex constraint can be effectively solved.
The flow of RRT algorithm is shown in fig. 6:
(1) Setting an initial point x init And target point x goal A state sampling space M is set by oneself;
(2) Random sampling is carried out to obtain sampling points x rand If the sampling point x rand Within the obstacle, resampling is performed;
(3) If not in the obstacle, calculate the sampling point x rand Distance from all nodes in the set τ (nodes that have been generated), resulting in nearest node x near Then from node x near StepSize of StepSize (0)<StepSize<1) Trend node x rand Generating a new node x new If (if)x near And x new Line E of (2) i Through the obstacle, re-randomly sampling;
(4) After repeated iteration, a random expansion tree is generated, and when child nodes in the random expansion tree enter a target area specified by the user, the unknown area can be explored in the random expansion tree;
the controller circularly performs unknown map segmentation and distributes the mobile robots to perform unknown region exploration. This process may be continued until the width of the unknown region is less than the sensing range of the robot.
And 4, planning an optimal path, after the map exploration and construction are completed, solving a global shortest path problem of the robot in a static environment by adopting an A-scale algorithm after each robot obtains a target point task, and solving an optimal control problem to enable the optimal control problem to be in line with a kinematic model of the robot, and enabling the robot to move to a target position along the generated path.
After the robot determines the exploration target point, the current position of the robot is taken as a starting point, the exploration target point is taken as an end point to plan the motion path of the robot. The algorithm has good effect in solving the shortest path in static environment, combines the advantages of the Dijkstra algorithm such as rapidity and the breadth first search (Breadth First Search, BFS) algorithm, and is particularly remarkable in the environment of grid structure. The cost function of the a algorithm is shown as follows:
f(n)＝g(n)+h(n)
f (n) represents the total cost from the start point a to the target point through the node n (x, y), g (n) represents the moving cost from the start point to the node, and its calculation formula is as follows:
wherein parent node of parent (n) is n, dist (A, n) is distance between A node and n node. h (n) represents a cost estimate from the starting point a to the target point, the presence of h (n) causes the algorithm to be directional when searching, taking the euclidean distance as shown in the following equation:
h(n)＝dist(n,B)
wherein point B is the target point. Implementation of the a algorithm requires maintaining two sets, set OPEN and set respectively
All points that have been generated but have not been accessed are stored in the CLOSE set OPEN, and points that have been accessed are stored in the CLOSE set. In the grid map, the algorithm A adopts eight neighborhood directions to search the surrounding, and the node with the minimum cost function in the feasible adjacent area is found, so that the node searches the surrounding feasible area until the target node is in the set CLOSE or the OPEN set is empty. The robot moves along the generated path to the target location.
In another embodiment of the present invention, a multi-mobile robot collaborative exploration system in an unknown environment of the present invention includes:
the exploration module is used for realizing that N mobile robots are dispersed in different areas of an unknown environment to cooperatively work, carrying out data sharing among the N mobile robots, and rapidly traversing the unknown environment map by adopting a segmentation strategy to finish exploration; comprising the following steps: each mobile robot scans the surrounding environment, builds a local grid map of the current environment, transmits the local grid map to a controller database, and synchronously transmits the local grid map to other mobile robots; meanwhile, in the process of constructing the local grid map by the mobile robots, the controller realizes the positioning of each mobile robot through a positioning algorithm; the controller maps the environment according to N local grid maps constructed by N mobile robots to obtain an unknown environment map; dividing an unknown environment area into N areas by the positioning of each mobile robot, dividing grids of the unknown area into N parts according to the number of the grids, and enabling each mobile robot to correspond to one area; each mobile robot adopts an RRT exploration algorithm to explore the area where the mobile robot is located, and sends an exploration result to the controller in real time, and data sharing is carried out among the mobile robots; if the exploration of a certain region is completed, the controller distributes the mobile robot of the region to the exploration less region according to the exploration conditions of each region, and further divides the exploration less region into subareas according to the region dividing method, and the mobile robot continues to explore the subareas where the mobile robot is located by adopting an RRT exploration algorithm until all the region exploration is completed, namely the exploration of an unknown environment is realized, and an unknown environment map is obtained;
and the path planning module is used for completing global shortest path planning from the mobile robot to the target point by adopting an A-algorithm.
Preferably, the plurality of mobile robots are two-wheeled differential mobile robots, on which a laser radar and a PC computer are mounted, the PC computer supporting an ROS operating system.
In yet another embodiment of the present invention, an apparatus device comprises a memory and a processor, wherein:
a memory for storing a computer program capable of running on the processor;
and the processor is used for executing the steps of the multi-mobile robot collaborative exploration method under the unknown environment when the computer program is run, and can achieve the technical effect that the methods are consistent.
In yet another embodiment of the present invention, a storage medium has a computer program stored thereon, where the computer program when executed by at least one processor implements the steps of the multi-mobile robot collaborative exploration method in an unknown environment as described above, and achieves technical effects consistent with the method described above.

Claims (10)

1. The multi-mobile robot collaborative exploration method under the unknown environment is characterized by comprising the following steps of:
n mobile robots are dispersed in different areas of an unknown environment to cooperatively work, data sharing is carried out among the N mobile robots, and a segmentation strategy is adopted to quickly traverse an unknown environment map so as to finish exploration; after each mobile robot determines an exploration target point, taking the current position of the mobile robot as a starting point, and adopting an A-type algorithm to complete global shortest path planning from each mobile robot to the exploration target point by taking the exploration target point as an end point;
the method for quickly traversing the unknown environment map by adopting the segmentation strategy and completing exploration comprises the following steps: each mobile robot scans the surrounding environment, builds a local grid map of the current environment, transmits the local grid map to a controller database, and synchronously transmits the local grid map of each mobile robot to other mobile robots by the controller; meanwhile, in the process of constructing the local grid map by the mobile robots, the controller realizes the positioning of each mobile robot through a positioning algorithm; the controller maps the environment according to N local grid maps constructed by N mobile robots to obtain an unknown environment map; the controller divides the unknown environment area into N areas, and each mobile robot corresponds to one area; each mobile robot adopts an RRT exploration algorithm to explore the area where the mobile robot is located, and sends an exploration result to the controller in real time, and data sharing is carried out among the mobile robots; if the exploration of a certain region is completed, the controller distributes the mobile robots completing the region to unexplored regions according to the exploration conditions of each region, and further divides the unexplored regions into sub-regions according to the region division method, and the mobile robots continue to explore the sub-regions where the mobile robots are located by adopting an RRT exploration algorithm until all the region exploration is completed, namely the exploration of the unknown environment is realized, and an unknown environment map is obtained.
2. The collaborative exploration method for multiple mobile robots in an unknown environment according to claim 1, wherein a Gmbping algorithm is adopted to construct a local grid map of the current environment.
3. The collaborative exploration method of multiple mobile robots in an unknown environment according to claim 1, wherein the controller uses an adaptive monte carlo positioning AMCL algorithm for adaptively adjusting the number of particle samples based on a KL distance algorithm to realize the positioning of the robots;
the AMCL algorithm is improved on the basis of the MCL algorithm, and comprises the following steps: the AMCL algorithm estimates the weight w from the long term slow And short-term estimation weights w fast Judging whether the robot is kidnapped or not, if w fast Inferior to w slow Robot slave binding by adding random particles in resamplingIn-rack recovery, expressed as:
wherein w is avg Representing the average weight of all particles, parameter α slow And alpha fast The attenuation rate of the exponential filter is equal to or less than 0 and equal to or less than alpha, and the attenuation rate is equal to or less than alpha when long-term estimation and short-term estimation are respectively averaged slow ≤α fast ；
The AMCl resampling stage adopts a KLD algorithm to adaptively adjust the particle number, and the particle number is expressed as:
where ε represents the error between the true and estimated distributions, z 1-δ Represents the 1-delta quantile on the standard normal distribution, k represents the non-vacancy of the histogram, and the upper limit M of the particle number top Linear relation with non-vacancy k; in the initial phase of global localization, a very large number of samples are needed, M being the greater the k top The higher; when global positioning is completed, the problem is converted into a position tracking problem, and particles are concentrated near the real position, k becomes less, and M top Lowering; in this way, the number of particles is dynamically adjusted.
4. The collaborative exploration method for multiple mobile robots in an unknown environment according to claim 1, wherein the RRT exploration algorithm specifically comprises:
(1) Setting an initial point xinit and a target point xgold to set a state sampling space M by itself;
(2) Randomly sampling to obtain a sampling point xrands, and if the sampling point xrands is in the obstacle, re-randomly sampling;
(3) If the node is not in the obstacle, calculating the distance between the sampling point xrnd and all nodes in the generated node set tau to obtain the nearest node xnear, moving from the node xnear to the node xrnd in a step size StepSize, generating a new node xnew, and if the line Ei between the xnear and the xnew passes through the obstacle, randomly sampling again; wherein 0< stepsize <1;
(4) And (3) generating a random expansion tree through repeated iteration, and completing exploration of an unknown region in the random expansion tree when child nodes in the random expansion tree enter a specified target region.
5. The collaborative exploration method for multiple mobile robots in an unknown environment according to claim 1, wherein the method for completing global shortest path planning from the mobile robot to the target point by using an a-algorithm is as follows:
the algorithm A combines the advantages of the rapidity and the breadth-first search BFS algorithm of the Dijkstra algorithm; the cost function of the a algorithm is shown as follows:
f(n)＝g(n)+h(n)
where f (n) represents the total cost from the start point a to the target point through the node n (x, y), g (n) represents the movement cost from the start point a to the node n (x, y), and the calculation formula is as follows:
wherein parent node of parent (n) is n, dist (A, n) is distance between starting point A and n node; h (n) represents a cost estimate from the starting point a to the target point, the presence of h (n) causes the algorithm to be directional when searching, taking the euclidean distance as shown in the following equation:
h(n)＝dist(n,B)
the method comprises the steps that a point B is a target point, two sets are required to be maintained in the implementation of an algorithm A, all generated points which are not accessed are stored in a set OPEN and a set CLOSE set OPEN respectively, and the accessed points are stored in the set CLOSE; in the grid map, the algorithm A adopts eight neighborhood directions to search the surrounding, and the node with the minimum cost function in the feasible adjacent area is found, so that the node searches the surrounding feasible area until the target node is in the set CLOSE or the OPEN set is empty; the robot moves along the generated path to the target location.
6. The collaborative exploration method of multiple mobile robots in an unknown environment according to claim 1, wherein the mobile robot is a two-wheeled differential mobile robot, a laser radar and a PC computer supporting an ROS operation system are mounted on the mobile robot, the laser radar is used for scanning the surrounding environment, and the PC computer is used for realizing information interaction between the corresponding mobile robot and a controller; the controller is connected with the PC computers of the mobile robots through a wireless network based on the communication mode among the ROS nodes.
7. A multi-mobile robot collaborative exploration system in an unknown environment, comprising:
the exploration module is used for realizing that N mobile robots are dispersed in different areas of an unknown environment to cooperatively work, carrying out data sharing among the N mobile robots, and rapidly traversing the unknown environment map by adopting a segmentation strategy to finish exploration; comprising the following steps: each mobile robot scans the surrounding environment, builds a local grid map of the current environment, transmits the local grid map to a controller database, and synchronously transmits the local grid map to other mobile robots; meanwhile, in the process of constructing the local grid map by the mobile robots, the controller realizes the positioning of each mobile robot through a positioning algorithm; the controller maps the environment according to N local grid maps constructed by N mobile robots to obtain an unknown environment map; dividing an unknown environment area into N areas by the positioning of each mobile robot, dividing grids of the unknown area into N parts according to the number of the grids, and enabling each mobile robot to correspond to one area; each mobile robot adopts an RRT exploration algorithm to explore the area where the mobile robot is located, and sends an exploration result to the controller in real time, and data sharing is carried out among the mobile robots; if the exploration of a certain region is completed, the controller distributes the mobile robot of the region to the exploration less region according to the exploration conditions of each region, and further divides the exploration less region into subareas according to the region dividing method, and the mobile robot continues to explore the subareas where the mobile robot is located by adopting an RRT exploration algorithm until all the region exploration is completed, namely the exploration of an unknown environment is realized, and an unknown environment map is obtained;
and the path planning module is used for completing global shortest path planning from the mobile robot to the target point by adopting an A-algorithm.
8. The collaborative exploration system of multiple mobile robots in an unknown environment of claim 7, wherein the plurality of mobile robots are two-wheeled differential mobile robots having lidar and PC computers mounted thereon, the PC computers supporting ROS operating systems.
9. An apparatus device comprising a memory and a processor, wherein:
a memory for storing a computer program capable of running on the processor;
a processor for performing the steps of the multi-mobile robot collaborative exploration method in an unknown environment of any of claims 1-6 when running the computer program.
10. A storage medium having stored thereon a computer program which, when executed by at least one processor, implements the steps of a method of collaborative exploration of a multiple mobile robot in an unknown environment according to any of claims 1-6.
CN202310507646.8A 2023-05-08 2023-05-08 Multi-mobile robot collaborative exploration method under unknown environment Pending CN116627127A (en)

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