WO2022252221A1 - 一种移动机器人队列***及路径规划、跟随方法 - Google Patents

一种移动机器人队列***及路径规划、跟随方法 Download PDF

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WO2022252221A1
WO2022252221A1 PCT/CN2021/098376 CN2021098376W WO2022252221A1 WO 2022252221 A1 WO2022252221 A1 WO 2022252221A1 CN 2021098376 W CN2021098376 W CN 2021098376W WO 2022252221 A1 WO2022252221 A1 WO 2022252221A1
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robot
following
sensor
mobile
mobile robot
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PCT/CN2021/098376
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English (en)
French (fr)
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鲁守银
张强
高诺
张涛
高焕兵
王涛
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山东建筑大学
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Publication of WO2022252221A1 publication Critical patent/WO2022252221A1/zh

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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
    • G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
    • 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
    • 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
    • G05D1/0278—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using satellite positioning signals, e.g. GPS
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
    • G05D1/0291—Fleet control

Definitions

  • the invention belongs to the field of multi-robot path planning and obstacle avoidance, and mainly relates to a method for moving path planning and obstacle avoidance of a robot queue, which is mainly used in the medical field, military operations, industrial production and post-disaster reconstruction.
  • CN201310402941.3 discloses a multi-mobile robot control system based on following the leader formation, including an experimental environment image acquisition module, a host computer positioning module, a vehicle-type mobile robot group, a communication module, and a control algorithm module.
  • the experimental environment image acquisition module will be used to collect video images of a plurality of mobile robot formation experimental environments;
  • the upper computer positioning module uses image processing algorithms to calculate the absolute position of each robot in real time through the coordinate system calibrated by the camera Information;
  • the car-type mobile robot group is composed of multiple single mobile cars, and independently decides to complete the formation task;
  • the communication module performs data interaction and information sharing through wireless communication;
  • the control algorithm module is based on the pilot follow The formation algorithm coordinates and controls the entire system to complete the formation task.
  • the system uses industrial cameras to obtain robot image information to obtain the pose of the robot.
  • the application conditions are harsh, and the accuracy of the obtained pose information is not enough.
  • the system can only be used under special indoor conditions, and cannot be used outdoors and long-distance operations, which has great limitations.
  • the traditional path planning of the mobile robot regards the mobile robot as a particle for analysis, while the mobile robot queue is the march of multiple targets, which requires the entire The robot queue moves synchronously; the problems in this way are:
  • the robot queue must maintain the formation during the movement, and the path planning of a single robot cannot maintain the formation during the movement. For obstacles whose volume is smaller than the queue spacing, it is easy for the robot to avoid obstacles, and the robot queue should also be able to pass smoothly.
  • the mobile robot queue faces obstacles and emergencies during operation, it needs to maintain the robot queue while avoiding obstacles.
  • the leading robot detects whether there is an obstacle in the forward area; if there is no obstacle, it will continue to run in the original formation; if there is, the multi-mobile robot system enters the obstacle avoidance state, and then informs all following robots in the form of broadcast ;Finally, the follower robot switches formations according to the stored information of different formations and the information broadcast by the leading robot, and determines its position in the formation; repeat the above process.
  • the invention adopts the fuzzy formation and obstacle avoidance control method to realize the multi-mobile robot formation function in an unknown environment, and can effectively avoid obstacles to run.
  • this method adopts a method based on a pilot robot, which mainly relies on a fully functional robot as the pilot robot, and the host computer cannot effectively monitor and control the robot queue.
  • the leader robot fails, it will cause errors in the queue, and when the leader robot cannot work, the entire queue will also be unable to work. Therefore, the reliability and stability of this method are poor, and it cannot cope with complex environments and unexpected situations.
  • the technical problem mainly solved by the present invention is to propose a method for path planning and obstacle avoidance of the mobile robot queue, which can keep the mobile robot queue moving synchronously, and the movement of each individual is not affected, and can also realize the robot queue At the same time, obstacles on the path can be effectively avoided during the travel process, reducing the loss of the robot.
  • the present invention proposes a mobile robot queue system based on multi-sensors; including a host computer and a plurality of robots, each robot communicates with the host computer through the first communication module; The robots communicate with each other through the second communication module; the host computer can designate any one or more robots as the leader robot, and the rest of the mobile robots as follower robots; The environmental information obtained by the second communication module performs path planning; the following robot obtains the positional relationship between itself and the pilot robot through the positioning module carried by itself, and realizes following; The distance between the leader robot and the follower robot can realize the formation transformation.
  • a positioning module and a wireless communication module are installed on each mobile robot, and one mobile robot is designated as a pilot robot, and the rest of the mobile robots are used as follower robots.
  • the positional relationship with the pilot robot is to follow the pilot robot by maintaining the set following distance position with the pilot robot.
  • different formations can be realized by setting parameters such as the position distance of the follower robot relative to the leader robot, so as to meet actual needs.
  • the setting of the pilot robot can not be unique, but generally only one is enough. In complex environments, multiple pilot robots can be set to improve the stability of the system.
  • the present invention also proposes a path planning method based on a multi-sensor mobile robot queue system.
  • the upper computer first sets the formation formation and determines the leader robot, and sends it to the mobile robot through the wireless communication module.
  • Mobile robot after receiving instructions, the mobile robot follows the robot to the designated location.
  • the surrounding obstacle information is detected by the panoramic camera and the ultrasonic sensor, and the following robot transmits the obstacle information to the pilot robot through the Wi-Fi communication module, and the pilot robot performs path planning and starts to move.
  • Step 1 Any robot obtains the command from the host computer and acts as a leader robot; the other robots acquire commands from the host computer as follower robots, and follow the robots to move to the specified position to form a robot queue;
  • Step 2 the pilot robot collects information on obstacles in the environment
  • Step 3 the pilot robot obtains the relative position information of the obstacle collected by the follower robot;
  • Step 4 the pilot robot applies all obstacle position information to the grid map
  • Step 5 take the current position of the pilot robot as the initial position, and take the final position as the target position;
  • Step 6 randomly generating a random point X rand in the grid map
  • Step 7 find the nearest X near to X rand among the points of the known tree
  • Step 8 intercept point X new with step size m from X near to X rand direction;
  • Step 9 if there is no obstacle between X near and X new , find an alternative parent node of X near with a fixed radius L around X near ;
  • Step 10 calculate the path from the starting point to each candidate parent node and then to X new , and find out the candidate parent node with the shortest path as the reselected parent node of X new ;
  • Step 11 calculate the nodes with X new as the center and L as the radius, if the distance from the starting point to X new and then to the surrounding nodes is shorter than the distance from the starting point to the surrounding nodes of X new , then take X new as the parent of the surrounding nodes node;
  • Step 12 add X new to the collection of trees, and return to step 6;
  • Step 13 when growing to the target point, select the path with the shortest distance as the optimal path for planning.
  • the present invention also provides a robot following method.
  • the pilot robot first starts to work, and the follower robot follows.
  • Each mobile robot is equipped with a Wi-Fi communication module that can communicate with each other.
  • the pilot robot detects its own position information through the GPS module, and then transmits the position information to the follower robot through the Wi-Fi communication module.
  • the follower robot uses the pilot robot The location information to follow, the specific steps are as follows:
  • the sensor on the following mobile robot collects the current displacement linear velocity and rotational angular velocity in real time and feeds them back to the control module of the following mobile robot.
  • the control module of the following mobile robot performs calculations to obtain the trajectory tracking error, and then uses the trajectory tracking control method to control the following mobile robot.
  • the trajectory of the robot is adjusted in real time to realize the follow-up of the pilot robot.
  • obstacle avoidance when obstacles are encountered during the operation of the robot queue, obstacle avoidance must be performed.
  • the sensor of the lead robot detects that the distance from the obstacle to the lead robot is d1
  • it will decelerate immediately
  • the follower robot After leaving the obstacle, the lead robot and the follower robot will accelerate to the original speed.
  • the sensor of the leading robot detects that the distance from the obstacle to the leading robot is d2
  • it immediately decelerates until it stops, and at the same time the following robot stops to the following position.
  • the mobile robot detects obstacle information and re-plans the path.
  • Step 3.4 when the sensor of the following robot detects that the distance from the obstacle to the leading robot is d1, decelerate immediately, and accelerate to the following position after leaving the obstacle; when the sensor of the leading robot detects that the distance from the obstacle to the leading robot is d2, immediately Slow down until it stops, send a message to the leader robot, the leader robot stops moving, and the follower robot plans a path to the following position.
  • the follower robot arrives, the robot queue starts to move.
  • step 3.5 after the robot queue stops moving, use the GPS positioning device to detect the current position information, and return to step 3.3 if the target point is not reached.
  • the ultrasonic sensors installed on the car body of each mobile robot can detect the surrounding obstacle information in real time.
  • a stop area d2 and a deceleration area d1 are set at a certain distance around it.
  • the settings of the deceleration area d1 and the stop area d2 are related to the current speed, pose and body size of the mobile robot.
  • the range of d1 and d2 is relatively large.
  • the host computer transmits to the lead robot and the follower robot through the wireless communication module, and the follower robot moves to a new follow position after receiving the command.
  • the present invention uses a plurality of sensors as the environment perception system of the robot, which can effectively detect its own running state and environment information, and improve the adaptability of the system in complex environments.
  • the present invention adopts the rapid expansion random tree method for path planning, and superimposes the environmental information of each robot on the pilot robot for planning, which not only improves the efficiency of path planning, but also ensures the normal movement of a single robot.
  • the upper computer of the present invention can monitor the operating status of the system in real time and issue instructions to the system.
  • Each robot can be used as a pilot robot, and the pilot robot and formation can be set at any time through the host computer to cope with different environments and needs.
  • Fig. 1 is the schematic diagram of mobile robot formation
  • Fig. 2 is a block diagram of the robot system
  • Fig. 3 is the flow chart of mobile robot formation transformation
  • Fig. 4 is the flow chart of mobile robot queue path planning
  • Figure 5 is a flow chart of mobile robot queue obstacle avoidance.
  • the schematic diagram of the mobile robot queue shown in Figure 1 is an application of this patent, including a pilot robot and a follower robot.
  • Using the pilot robot method to control the mobile robot queue has the advantages of low cost and high stability.
  • the number of following robots and the formation of the robot queue can be adjusted by setting the robot queue matrix through the host computer.
  • Figure 2 is a schematic diagram of the robot system, including a control module, a drive module, a perception module, a positioning module and a communication module; the control module is used as the core controller of the robot, and an embedded system is used.
  • the core controller processes the perception module, the positioning module,
  • the data of the communication module is used to control the operation of the drive module.
  • the drive module drives the motor or reducer to run, so as to realize the acceleration, deceleration and steering actions of the mobile robot.
  • the above perception system is a module for the mobile robot to collect environmental information and its own information, and is composed of multiple sensors, including ultrasonic sensors, pose sensors, temperature sensors, power detection sensors and visual sensors; the visual sensors are installed on the mobile robot On the top of the car body, the information of environmental obstacles is scanned and sent to the control module.
  • Ultrasonic sensors are installed in the four directions of the mobile robot body to assist in the detection of environmental information, and real-time detection of obstacle information is sent to the control module.
  • the temperature sensor is installed on the motor and the outside of the robot to detect the motor temperature and ambient temperature in real time, and send the temperature information to the controller.
  • the control module sends a stop command to the drive module, and sends it to the host computer through the communication module.
  • the power detection sensor is installed in the power supply part, and the remaining power of the mobile robot can be checked in real time through the host computer.
  • the pose sensor is composed of an angle sensor, a gyroscope and an acceleration sensor.
  • the angle sensor is installed in the steering part of the front wheel to detect the steering angle of the front wheel.
  • the gyroscope and the acceleration sensor are installed inside the mobile robot to detect the gap between the robot's current longitudinal axis and the x-axis. angle and acceleration.
  • the positioning module uses a GPS positioning device to obtain the position coordinates of the center point of the mobile robot, so as to determine the position of the mobile robot.
  • the positioning module sends the collected position information to the control module for processing, and controls the communication module to transmit information.
  • the communication module includes the wireless communication module between the mobile robot and the upper computer, and the Wi-Fi communication module between the mobile robots.
  • the control module controls the communication module to send information, and the communication module sends the received information to the control module for processing.
  • the wireless communication module Through the wireless communication module, the user can know the running status of all robots in real time. When a mobile robot fails and cannot continue to work, or when the communication fails, the communication module sends it to the host computer for processing.
  • the above-mentioned multiple sensors send the collected information to the control module, and the control module controls the drive module to respond, and at the same time sends it to the host computer in real time through the communication module.
  • the formation matrix P1 can be obtained as:
  • the host computer transmits instructions to the mobile robot through the wireless communication module, and the pilot robot collects its own location information through the GPS positioning module, and transmits the collected location information to the follower robot through the Wi-Fi communication module.
  • the following robot After the following robot receives the position information of the leading robot through the Wi-Fi communication module, it determines its own position through the GPS positioning module, and then determines the walking path through path planning.
  • the following robot sends instructions to the driving module through the control module, and the driving module drives the motor to run, so that the following robot reaches the designated following position to form a robot queue.
  • Fig. 4 is a flow chart of the route planning of the mobile robot queue in the present invention.
  • the follower robot detects the surrounding environment through its own panoramic camera and ultrasonic sensor, grids the environment, and subdivides the environment into a unit , and binarize it, set the position with obstacles to 1, and the position without obstacles to 0.
  • the detected relative position obstacle information is transmitted to the controller of the pilot robot through the Wi-Fi communication module.
  • the pilot robot receives the obstacle information of the relative position of the following robot through the communication module, and the control module superimposes the obstacle information of the relative position of the following robot on the obstacle information of the pilot robot to establish the obstacle environment information.
  • Step 1.1 take the current position of the pilot robot as the initial point, and take the final position as the target point.
  • step 1.2 a random point X rand is randomly generated in the grid map.
  • Step 1.3 find the nearest X near to X rand among the points of the known tree in the grid map.
  • Step 1.4 intercept point X new with step m in the direction from X near to X rand .
  • Step 1.5 if there is no obstacle between X near and X new , find an alternative parent node of X near with a fixed radius L around X near .
  • Step 1.6 calculate the path from the starting point to each candidate parent node and then to X new , and find out the candidate parent node with the shortest path as the reselected parent node of X new .
  • Step 1.7 calculate the nodes with X new as the center and L as the radius, if the distance from the starting point to X new and then to the surrounding nodes is shorter than the distance from the starting point to the surrounding nodes of X new , then use X new as the parent of the surrounding nodes node.
  • Step 1.8 add X new to the collection of trees. Meanwhile return to step 1.2.
  • Step 1.9 when growing to the target point, select the path with the shortest distance as the optimal path for planning.
  • the mobile robot sends its own running status to the host computer in real time, and the user can monitor the running status of the mobile robot in real time through the host computer.
  • the position information (x, y) is collected in real time through the GPS positioning module, and sent to the following robot through the Wi-Fi module. After receiving the information, the Wi-Fi module of the following robot sends it to the control module for trajectory tracking processing. Make the follower robot follow the lead robot in real time.
  • the follower robot obtains the location information of the lead robot in real time, that is, the lead robot starts to work and transmits the location information to the follower robot in real time
  • the sensor on the following mobile robot collects the current displacement linear velocity and rotational angular velocity in real time and feeds them back to the control module of the following mobile robot.
  • the control module of the following mobile robot performs calculations to obtain the trajectory tracking error, and then uses the trajectory tracking control method to control the following mobile robot.
  • the trajectory of the robot is adjusted in real time to realize the follow-up of the pilot robot.
  • x(t), y(t) are the position coordinates of the following robot at time t
  • ⁇ (t) is the deflection angle of the following robot's motion direction relative to the X axis at time t. and are the first derivatives of x(t), y(t), ⁇ (t) and v(t), respectively.
  • v(t) represents the linear velocity fed back by the robot sensor, Indicates the front wheel angle, L indicates the length of the car body, and a indicates the acceleration of the mobile robot.
  • the position information of the pilot robot consists of x o (t), y o (t) and ⁇ o (t), which are the coordinates of the pilot robot in Cartesian coordinates and the deflection angle relative to the X axis.
  • the position information of the follower robot relative to the leader robot can be obtained as x r (t), y r (t) and ⁇ r (t), so the target position of the follower robot is x r (t), y r (t) and ⁇ r (t).
  • x o (t), y o (t) are measured by GPS sensor, ⁇ o (t) is measured by gyroscope, Measured by the angle sensor.
  • x e (t), y e (t) is the error between the actual position of the following robot and the expected distance in the X-axis direction and the error in the Y-axis direction at time t
  • ⁇ e (t) is the relative movement direction of the following robot at time t. The deviation between the declination of the X axis and the desired position relative to the declination of the X axis.
  • obstacle avoidance is performed when the robot queue encounters an obstacle.
  • Fig. 5 is a flow chart of robot queue moving and avoiding obstacles.
  • the robot queue sets the necessary safety distance d1 n and d2 n during the operation process, which is controlled by the ultrasonic sensor on the mobile robot body in real time, and an ultrasonic sensor is installed on the front, rear, left, and right sides of each mobile robot body.
  • the numbers of the ultrasonic sensors are h 1 , h 2 , h 3 and h 4 , which represent four ultrasonic sensors, front, rear, left, and right respectively. Moving the ultrasonic sensors during operation detects the surrounding environment in real time.
  • the h1 sensor detects the obstacles ahead, and sets the deceleration distance d1 and the stop distance d2, and the distance between d1 and d2 increases with the increase of speed.
  • h 2 detects obstacles behind the robot, and does not need to be set when the mobile robot is going straight.
  • h 3 and h 4 detect obstacles in the left and right directions of the robot, and only need to set the stop area d2 when the mobile robot is going straight to ensure the safe passage of the robot.
  • Step 3.1 when the ultrasonic sensor of the pilot robot detects that the distance from the obstacle to the pilot robot is d1, it will decelerate immediately, and at the same time send information to the following robot through the Wi-Fi communication module, and the following robot will also decelerate. After leaving the obstacle, the pilot robot and follow the robot to accelerate to the original speed.
  • Step 3.2 when the sensor of the leading robot detects that the distance from the obstacle to the leading robot is d2, it immediately decelerates until it stops, and at the same time the following robot stops to the following position.
  • step 3.3 the mobile robot detects obstacle information and re-plans the path.
  • Step 3.4 when the sensor of the following robot detects that the distance from the obstacle to the leading robot is d1, decelerate immediately, and accelerate to the following position after leaving the obstacle; when the sensor of the leading robot detects that the distance from the obstacle to the leading robot is d2, immediately Slow down until it stops, send a message to the leader robot, the leader robot stops moving, and the follower robot plans a path to the following position.
  • the follower robot arrives, the robot queue starts to move.
  • step 3.5 after the robot queue stops moving, use the GPS positioning device to detect the current position information, and return to step 3.3 if the target point is not reached.
  • the method adopted by the invention has the advantages of high applicability, low cost, strong stability, etc., and can cope with various emergencies at the same time.

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Abstract

本发明公开了一种移动机器人队列***及路径规划、跟随方法，包括多个机器人和一个上位机，每个机器人通过第一通讯模块与上位机进行通讯；各个机器人之间通过第二通讯模块进行相互通讯；所述的上位机可指定任意一个或者多个机器人作为领航机器人，其余的移动机器人作为跟随机器人；所述的领航机器人通过自身携带的感知模块和第二通讯模块得到的环境信息进行路径规划；跟随机器人通过自身携带的定位模块获得自己与领航机器人的位置关系，实现跟随；所述的上位机通过设定领航机器人与跟随机械人之间的距离实现领航机器人和跟随机器人之间的队形变换。

Description

一种移动机器人队列***及路径规划、跟随方法 技术领域
本发明属于多机器人路径规划和避障领域，主要涉及一种机器人队列的移动路径规划和避障方法，主要应用于医疗领域，军事行动，工业生产及灾后重建。
背景技术
近些年在工业生产和灾后重建等一些应用中，机器人队列的高效工作引起了人们重视，机器人队列的前景变得光明起来，但是在应用过程中仍存在许多的问题。
首先，移动机器人队列在运行过程中的同步问题是研究的重点问题，同步是实现机器人队列的基础，比较常见的方法有基于行为法，领航跟随法和虚拟结构法等，例如：在CN201310402941.3中公开了一种基于跟随领航者编队的多移动机器人控制***，包括实验环境图像采集模块、上位机定位模块、车式移动机器人组、通信模块、控制算法模块。其中所述实验环境图像采集模块，将用于采集多个移动机器人编队实验环境的视频图像；所述上位机定位模块，通过摄像头标定的坐标系，运用图像处理算法实时计算每个机器人的绝对位置信息；所述车式移动机器人组，由多个单体移动小车组成，自主决策完成编队任务；所述通信模块，通过无线通信方式进行数据交互以及信息共享；所述控制算法模块，基于领航跟随的编队算法协调和控制整个***完成编队任务。但是该***存在以下问题：
该***采用工业摄像头获得机器人图像信息，以此得到机器人的位姿，应用条件苛刻，且获取的位姿信息准确度不够。该***只能在室内特殊条件下使用，无法在室外和长距离作业情况下使用，具有很大的局限性。
其次，移动机器人在到达目标点过程中要找到最优的路径，传统的移动机器人路径规划是将移动机器人看作是一个质点来进行分析，而移动机器人队列则是多个目标的行进，需要整个机器人队列同步运动；这种方式存在的问题是：
机器人队列在运动过程中要保持队形，单个机器人的路径规划无法在运动过程中保持队形。对于体积小于队列间距的障碍物，容易引起机器人避障，机器人队列也应可以顺利通过。
最后，移动机器人队列在运行过程中面对障碍物和突发状况时，需要在避障的同时保持机器人队列，例如在专利CN201310402941.3中公开了一种面向多移动机器人***的模糊编队及避障方法，首先领航机器人检测运行的前向区域是否有障碍物；如果无，则按原来队形继续运行；如果有，则多移动机器人***进入避障状态，然后以广播的形式告知所有跟随机器人；最后跟随机器人根据存储的不同队形信息以及领航机器人广播的信息切换队形，并确定在编队队形中的位置；重复上述过程。本发明采用模糊编队及避障控制方法实现未知环境下的多移动机器人编队功能，并能有效的避开障碍物运行。但是该方法采用的是一种基于领航机器人方法，主要依靠一个功能齐全的机器人作为领航机器人，上位机无法对机器人队列实现有效的监测和控制。当领航机器人出现故障时就会导致队列出现误差，而当领航机器人无法工作时，整个队列也将无法工作。因此，该方法的可靠性和稳定性都较差，无法应对复杂环境和意外状况。
发明内容
本发明主要解决的技术问题是提出一种用于移动机器人队列的路径规划和避障的方法，可以使移动机器人队列保持同步运动，并且每个个体的运动都不受到影响，还可以实现机器人队列的变化，同时在行进过程中可以对路径上的障碍物进行有效的避障，较少机器人的损耗。
为解决上述问题，本发明采用的技术方案是：
第一方面，针对移动机器人的队列问题，本发明提出了一种基于多传感器的移动机器人队列***；包括一个上位机和多个机器人，每个机器人通过第一通讯模块与上位机进行通讯；各个机器人之间通过第二通讯模块进行相互通讯；所述的上位机可指定任意一个或者多个机器人作为领航机器人，其余的移动机器人作为跟随机器人；所述的领航机器人通过自身携带的感知模块和第二通讯模块获得的环境信息进行路径规划；所述的跟随机器人通过自身携带的定位模块获得自己与领航机器人的位置关系，实现跟随；所述的上位机通过设定领航机器人与跟随机械人之间的距离实现领航机器人和跟随机器人之间的队形变换。
本发明中在每个移动机器人上都安装有定位模块和无线通信模块，指定一个移动机器人作为领航机器人，其余的移动机器人作为跟随机器人，跟随机器人通过自身携带的定位模块和环境障碍物信息获得自己与领航机器人的位置关系，通过与领航机器人保持设定的跟随距离位置等对领航机器人进行跟随。同时也可以通过设定跟随机器人相对领航机器人的位置距离等参数可以实现不同的编队队形，以达到实际的需求。并且领航机器人的设定也可以不唯一，但一般情况下只需一个就可以，在复杂环境下可以通过设定多个领航机器人来提高 ***的稳定性。
第二方面，本发明还提出了一种基于多传感器的移动机器人队列***的路径规划方法，移动机器人队列工作时，首先由上位机设定编队队形并确定领航机器人，通过无线通信模块发送给移动机器人，移动机器人接收到指令后，跟随机器人到达指定位置。通过全景摄像头和超声波传感器检测环境障碍物信息，跟随机器人通过Wi-Fi通信模块将障碍物信息传送到领航机器人，领航机器人进行路径规划，并开始运动。包括以下步骤：
步骤1，任一机器人获取上位机指令，作为领航机器人；其余机器人获取上位机指令作为跟随机器人，跟随机器人移动到指定位置组成机器人队列；
步骤2，领航机器人采集环境障碍物信息；
步骤3，领航机器人获取跟随机器人采集的障碍物相对位置信息；
步骤4，领航机器人将所有障碍物位置信息应用到栅格地图中；
步骤5，以领航机器人的当前位置为初始位置，以最终位置为目标位置；
步骤6，在栅格地图中随机生成随机点X rand；
步骤7，在已知树的点中找到距离X rand最近的X near；
步骤8，从X near到X rand方向上以步长m截取点X new；
步骤9，如果X near到X new之间没有障碍物，则在X near周围以固定半径L寻找X near的备选父节点；
步骤10，计算从起点到各个备选父节点再到X new的路径，找出路径最短的备选父节点作为X new的重选父节点；
步骤11，计算以X new为圆心以L为半径内的节点，如果从起点到X new再到周围节点的距离比从起点到X new周围节点的距离短，则将X new作为周围节点的父节 点；
步骤12，将X new加入到树的集合中，同时返回步骤6；
步骤13，当生长到目标点时，选择最短距离的路径作为规划的最优路径。
第三方面，本发明还提供了一种机器人跟随方法，机器人队列在运行过程中首先是由领航机器人开始工作，跟随机器人进行跟随。在每个移动机器人上都安装有可以相互通信的Wi-Fi通信模块，领航机器人通过GPS模块检测自己的位置信息，然后将位置信息通过Wi-Fi通信模块传送给跟随机器人，跟随机器人利用领航机器人的位置信息进行跟随，具体步骤如下：
跟随机器人实时获取领航机器人的位置信息；
在跟随机器人内建立跟随机器人运动学模型；
在跟随机器人运动学模型内引入位姿误差，得到跟随机器人跟随运动模型；
跟随移动机器人身上的传感器实时采集当前的位移线速度、旋转角速度并反馈给跟随移动机器人的控制模块，跟随移动机器人的控制模块进行计算，得到轨迹跟踪误差，然后利用轨迹跟踪控制方法对随移动机器人的轨迹进行实时调整，实现对领航机器人的跟随。
进一步的，在机器人队列运行过程中遇到障碍物时要进行避障。当领航机器人的传感器检测到障碍物距离领航机器人为d1时，立即减速，同时跟随机器人也进行减速，当离开障碍物后，领航机器人和跟随机器人加速到原速度。当领航机器人的传感器检测到障碍物距离领航机器人为d2时，立即减速直至停止，同时跟随机器人停止到跟随位置。
移动机器人检测障碍物信息，并重新进行路径规划。
步骤3.4，当跟随机器人的传感器检测到障碍物距离领航机器人为d1时， 立即减速，当离开障碍物后，加速到达跟随位置；当领航机器人的传感器检测到障碍物距离领航机器人为d2时，立即减速直至停止，发送信息给领航机器人，领航机器人停止运动，跟随机器人规划路径到达跟随位置，当跟随机器人到达后机器人队列开始运动。
步骤3.5，机器人队列停止运动后，利用GPS定位装置检测当前位置信息，若没有达到目标点则返回步骤3.3。
通过在每个移动机器人的车体上安装的的超声波传感器可以实时检测周围的障碍物信息。在机器人运动时，在其周围一定距离设置停止区d2和减速区d1，减速区d1和停止区d2的设置跟移动机器人当前速度、位姿和车身尺寸有关。当车速比较高时，d1和d2的范围比较大。
当机器人队列到达目的地需要停靠或者需要变换队形时，上位机通过无线通信模块传输给领航机器人及跟随机器人，跟随机器人接收到指令后移动到新的跟随位置。
本发明的有益效果如下：
(1)本发明采用多个传感器作为机器人的环境感知***，可以对自身运行状态和环境信息进行有效检测，提高***在复杂环境下的适应能力。
(2)本发明采用快速扩展随机树方法进行路径规划，将每个机器人的环境信息叠加到领航机器人上进行规划，既提高了路径规划的效率，也保证了单个机器人的正常运动。
(3)本发明的上位机可以实时监测***运行状况及对***发出指令。每个机器人都可以作为领航机器人使用，通过上位机可以随时设置领航机器人和队形，可以应对不同的环境和需求。
附图说明
图1为移动机器人队列的示意图；
图2为机器人***框图；
图3为移动机器人队列队形变换流程图；
图4为移动机器人队列路径规划流程图；
图5为移动机器人队列避障流程图。
具体实施方式
应该指出，以下详细说明都是例示性的，旨在对本发明提供进一步的说明。除非另有指明，本发明使用的所有技术和科学术语具有与本发明所属技术领域的普通技术人员通常理解的相同含义。
需要注意的是，这里所使用的术语仅是为了描述具体实施方式，而非意图限制根据本发明的示例性实施方式。如在这里所使用的，除非本发明另外明确指出，否则单数形式也意图包括复数形式，此外，还应当理解的是，当在本说明书中使用术语“包含”和/或“包括”时，其指明存在特征、步骤、操作、器件、组件和/或它们的组合；
下面结合附图对本专利进行说明：
图1所示的移动机器人队列示意图是本专利的一种应用情况，包括领航机器人及跟随机器人，采用领航机器人法对移动机器人队列进行控制具有成本低，稳定性高等优点。通过上位机对机器人队列矩阵设置可以调整跟随机器人数量及机器人队列的队形。
图2所示为机器人***示意图，包括控制模块、驱动模块、感知模块、定位模块和通信模块；控制模块作为机器人的核心控制器，采用嵌入式***，核 心控制器通过处理感知模块，定位模块，通信模块的数据来控制驱动模块的运行。驱动模块在接收到控制模块的指令后驱动电机或减速器运行，从而实现移动机器人的加速减速以及转向动作。
进一步的，上述的感知***是移动机器人采集环境信息和自身信息的模块，由多个传感器构成，包括超声波传感器，位姿传感器，温度传感器，电源检测传感器和视觉传感器构成；视觉传感器安装在移动机器人车体上方，扫描环境障碍物信息，发送给控制模块。超声波传感器安装在移动机器人车体的四个方向上，辅助检测环境信息，并实时检测障碍物信息发送给控制模块。温度传感器安装在机器人的电机和外部，实时检测电机温度和环境温度，并将温度信息发送给控制器。当电机或者环境温度过高时，控制模块向驱动模块发送停止指令，并通过通信模块发送给上位机。电量检测传感器安装在电源部分，可以通过上位机实时查看移动机器人电量剩余情况。位姿传感器由角度传感器，陀螺仪和加速度传感器构成，角度传感器安装在前轮转向部分，检测前轮转向角度，陀螺仪和加速度传感器安装在移动机器人内部，检测机器人当前纵向轴与x轴的夹角以及加速度。
定位模块采用GPS定位装置进行，获得移动机器人中心点的位置坐标，可以确定移动机器人的位置。定位模块将采集到的位置信息发送给控制模块处理，并控制通信模块传输信息。
通信模块包括移动机器人与上位机之间的无线通信模块，以及移动机器人之间通信的Wi-Fi通信模块。控制模块控制通信模块发送信息，同时通信模块将接受到的信息发送给控制模块处理。通过无线通信模块，用户可以实时了解到所有机器人的运行状况，当有移动机器人发生故障不能继续工作时，或者通 信出现故障时通信模块发送给上位机处理。
上述的多个传感器将采集到的信息发送给控制模块，控制模块控制驱动模块做出反应，同时通过通信模块实时地发送给上位机。
在本实施例中，设置跟随机器人的数量为2，他们的跟随位置分别为(-1,-1)和(1,-1)。根据图3的机器人队列流程示意图可得队形矩阵P 1为：
Figure PCTCN2021098376-appb-000001
首先确定跟随机器人的数量和移动机器人编队的队形，通过上位机输入队形矩阵P 1。
上位机通过无线通信模块将指令传输给移动机器人，领航机器人通过GPS定位模块采集自己的位置信息，将自己采集到的位置信息通过Wi-Fi通信模块传输给跟随机器人。
跟随机器人通过Wi-Fi通信模块接收到领航机器人的位置信息后，通过GPS定位模块确定自己的位置，然后通过路径规划确定行走的路径。
跟随机器人通过控制模块给驱动模块发送指令，驱动模块驱动电机运行，使跟随机器人到达指定跟随位置，组成机器人队列。
图4为本发明中移动机器人队列路径规划的流程图，在本实施例中，跟随机器人通过自己的全景摄像头和超声波传感器对周围环境进行检测，将环境进行栅格化，把环境细分为一个单位的矩形，并将其进行二值化处理，将有障碍物的位置设为1，没有障碍物的位置设为0。将检测到的相对位置障碍物信息通过Wi-Fi通信模块传递到领航机器人的控制器中。
领航机器人通过通信模块接收跟随机器人的相对位置的障碍物信息，控制 模块将跟随机器人相对位置的障碍物信息叠加到领航机器人的障碍物信息上，建立障碍物环境信息。
机器人队列路径规划步骤如下：
步骤1.1，以领航机器人的当前位置为初始点，以最终位置为目标点。
步骤1.2，在格栅地图中随机生成随机点X rand。
步骤1.3，在格栅地图的已知树的点中找到距离X rand最近的X near。
步骤1.4，从X near到X rand方向上以步长m截取点X new。
步骤1.5，如果X near到X new之间没有障碍物，则在X near周围以固定半径L寻找X near的备选父节点。
步骤1.6，计算从起点到各个备选父节点再到X new的路径，找出路径最短的备选父节点作为X new的重选父节点。
步骤1.7，计算以X new为圆心以L为半径内的节点，如果从起点到X new再到周围节点的距离比从起点到X new周围节点的距离短，则将X new作为周围节点的父节点。
步骤1.8，将X new加入到树的集合中。同时返回步骤1.2。
步骤1.9，当生长到目标点时，选择最短距离的路径作为规划的最优路径。
移动机器人将自身的运行状况实时地发送到上位机，用户可以通过上位机实时地监测移动机器人运行状况。
领航机器人运动后通过GPS定位模块实时采集位置信息(x,y)，通过Wi-Fi模块发送给跟随机器人，跟随机器人的的Wi-Fi模块接收到信息后发送给控制模块，进行轨迹跟踪处理，使跟随机器人实时跟随领航机器人。
跟随机器人轨迹跟踪的主要步骤如下：
跟随机器人实时获取领航机器人的位置信息，即领航机器人开始工作，并将位置信息实时传送给跟随机器人
在跟随机器人内建立跟随机器人运动学模型；
在跟随机器人运动学模型内引入位姿误差，得到跟随机器人跟随运动模型；
跟随移动机器人身上的传感器实时采集当前的位移线速度、旋转角速度并反馈给跟随移动机器人的控制模块，跟随移动机器人的控制模块进行计算，得到轨迹跟踪误差，然后利用轨迹跟踪控制方法对随移动机器人的轨迹进行实时调整，实现对领航机器人的跟随。
建立跟随机器人运动学模型如下：
Figure PCTCN2021098376-appb-000002
Figure PCTCN2021098376-appb-000003
Figure PCTCN2021098376-appb-000004
Figure PCTCN2021098376-appb-000005
其中x(t)，y(t)是t时刻下跟随机器人的位置坐标，θ(t)是t时刻下跟随机器人运动方向相对X轴的偏角。
Figure PCTCN2021098376-appb-000006
和
Figure PCTCN2021098376-appb-000007
分别是x(t)，y(t)，θ(t)和v(t)的一阶导数。v(t)表示跟随机器人传感器反馈回的线速度，
Figure PCTCN2021098376-appb-000008
表示前轮轮角，L表示车体长度，a表示移动机器人的加速度。
领航机器人的位置信息由x o(t)、y o(t)和θ o(t)构成，是领航机器人在笛卡尔坐标下的坐标及相对X轴的偏角。通过队列的组成方式，可以得到跟随机器人相对领航机器人的位置信息为x r(t)、y r(t)和θ r(t)，所以跟随机器人的目标位置为x r(t)，y r(t)和θ r(t)。
x o(t)、y o(t)由GPS传感器测得，θ o(t)由陀螺仪测得，
Figure PCTCN2021098376-appb-000009
由角度传感器测得。
在上述公式的基础上，引入位姿误差为[x e(t) y e(t) θ e(t)] T，进行坐标变换如 下：
Figure PCTCN2021098376-appb-000010
其中x e(t)，y e(t)是t时刻下跟随机器人实际位置与期望距离X轴方向的误差和Y轴方向的误差，θ e(t)是t时刻下跟随机器人实际运动方向相对X轴的偏角与期望位置相对X轴偏角的误差。
根据GPS定位模块确定当前位置信息，根据已知的地图信息和传感器实时采集到的环境信息确定当前位姿信息，计算跟随机器人期望位姿与实际位姿的误差，求出移动机器人跟随所需控制的加速度和前轮转角，由控制模块发出指令，驱动模块驱动执行机构实现跟随。
进一步的，在机器人队列运行过程中遇到障碍物时进行避障。
图5为机器人队列移动避障的流程图。
机器人队列在运行过程中设置必要的安全距离d1 n和d2 n，由移动机器人车体上的超声波传感器实时控制，每一个移动机器人车体的前后左右各安装一个超声波传感器。
超声波传感器的编号为h 1，h 2，h 3和h 4，分别代表前后左右四个超声波传感器，在运行过程中移动超声波传感器实时地检测周围环境状况。
当移动机器人直线运行时，h 1传感器检测前方障碍物，并设定减速距离d1和停止距离d2，d1和d2的距离随着速度的增加而增加。
h 2检测机器人后方障碍物，在移动机器人直行时不用设置。
h 3和h 4检测机器人左右方向的障碍物，在移动机器人直行时只需设置停止区d2，保证机器人安全通过。
当移动机器人转弯时，根据前轮转向角度
Figure PCTCN2021098376-appb-000011
的不同来设置传感器h 3和h 4的安全距离d1和d2。
步骤3.1，当领航机器人的超声波传感器检测到障碍物距离领航机器人为d1时，立即减速，同时通过Wi-Fi通信模块发送信息给跟随机器人，跟随机器人也进行减速，当离开障碍物后，领航机器人和跟随机器人加速到原速度。
步骤3.2，当领航机器人的传感器检测到障碍物距离领航机器人为d2时，立即减速直至停止，同时跟随机器人停止到跟随位置。
步骤3.3，移动机器人检测障碍物信息，并重新进行路径规划。
步骤3.4，当跟随机器人的传感器检测到障碍物距离领航机器人为d1时，立即减速，当离开障碍物后，加速到达跟随位置；当领航机器人的传感器检测到障碍物距离领航机器人为d2时，立即减速直至停止，发送信息给领航机器人，领航机器人停止运动，跟随机器人规划路径到达跟随位置，当跟随机器人到达后机器人队列开始运动。
步骤3.5，机器人队列停止运动后，利用GPS定位装置检测当前位置信息，若没有达到目标点则返回步骤3.3。
本发明采用的方法具有适用性高，成本低，稳定性强等优点，同时可以应对各种突发状况。
以上所述仅为本发明的优选实施例而已，并不用于限制本发明，对于本领域的技术人员来说，本发明可以有各种更改和变化。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (9)

  1. 一种移动机器人队列***，包括多个机器人和一个上位机，其特征在于，每个机器人通过第一通讯模块与上位机进行通讯；各个机器人之间通过第二通讯模块进行相互通讯；所述的上位机可指定任意一个或者多个机器人作为领航机器人，其余的移动机器人作为跟随机器人；所述的领航机器人通过自身携带的感知模块和第二通讯模块获得的环境信息进行路径规划；所述的跟随机器人通过自身携带的定位模块获得自己与领航机器人的位置关系，实现跟随；所述的上位机通过设定领航机器人与跟随机械人之间的距离实现领航机器人和跟随机器人之间的队形变换。
  2. 如权利要求1所述的移动机器人队列***，其特征在于，每个机器人包括控制模块，控制模块输入端连接感知模块、定位模块、第一通信模块和第二通信模块，输出端连接驱动模块、第一通信模块和第二通信模块；
    所述的驱动模块，驱动机器人加速、减速以及转向动作；
    所述的感知模块，采集环境信息和自身的信息；
    所述的定位模块，用于获得移动机器人中心点的位置坐标，确定移动机器人的位置；
    所述的第一通讯模块，用于移动机器人与上位机之间通信；
    所述的第二通讯模块，用于移动机器人之间的通讯。
  3. 如权利要求1所述的移动机器人队列***，其特征在于，所述的感知模块包括超声波传感器，位姿传感器，温度传感器，电源检测传感器和视觉传感器；
    视觉传感器安装在移动机器人车体上，检测环境障碍物信息；
    超声波传感器安装在移动机器人车体的四个方向上，辅助检测环境信息和 障碍物信息；
    温度传感器安装在移动机器人内部和外部，分别检测电机温度和外部空气温度；
    电源检测传感器安装在机器人电源上，检测机器人电量情况；
    位姿传感器包括角度传感器、陀螺仪和加速度传感器，所述角度传感器安装在前轮上，测量前轮转角；所述陀螺仪和加速度传感器安装在机器人内部，检测移动机器人的姿态和加速度。
  4. 如权利要求1-3任一所述的移动机器人队列***的路径规划方法，其特征在于，
    步骤1，任一机器人获取上位机指令，作为领航机器人；其余机器人获取上位机指令作为跟随机器人，跟随机器人移动到指定位置组成机器人队列；
    步骤2，领航机器人采集环境障碍物信息；
    步骤3，领航机器人获取跟随机器人采集的障碍物相对位置信息；
    步骤4，领航机器人将所有障碍物位置信息应用到栅格地图中；
    步骤5，以领航机器人的当前位置为初始位置，以最终位置为目标位置；
    步骤6，在栅格地图中随机生成随机点X rand；
    步骤7，在栅格地图的已知树的点中找到距离X rand最近的X near；
    步骤8，从X near到X rand方向上以步长m截取点X new；
    步骤9，如果X near到X new之间没有障碍物，则在X near周围以固定半径L寻找X near的备选父节点；
    步骤10，计算从起点到各个备选父节点再到X new的路径，找出路径最短的备选父节点作为X new的重选父节点；
    步骤11，计算以X new为圆心以L为半径内的节点，如果从起点到X new再到周围节点的距离比从起点到X new周围节点的距离短，则将X new作为周围节点的父节点；
    步骤12，将X new加入到树的集合中，同时返回步骤6；
    步骤13，当生长到目标点时，选择最短距离的路径作为规划的最优路径。
  5. 如权利要求1-3任一所述的基于多传感器的移动机器人队列***的跟随方法，其特征在于，
    跟随机器人实时获取领航机器人的位置信息；
    在跟随机器人内建立跟随机器人运动学模型；
    在跟随机器人运动学模型内引入位姿误差，得到跟随机器人跟随运动模型；
    跟随移动机器人身上的传感器实时采集当前的位移线速度、旋转角速度并反馈给跟随移动机器人的控制模块，跟随移动机器人的控制模块进行计算，得到轨迹跟踪误差，然后利用轨迹跟踪控制方法对随移动机器人的轨迹进行实时调整，实现对领航机器人的跟随。
  6. 如权利要求5所述的基于多传感器的移动机器人队列***的跟随方法，其特征在于，在机器人运动时，在其周围一定距离设置停止区d2和减速区d1。
  7. 如权利要求6所述的基于多传感器的移动机器人队列***的跟随方法，其特征在于，当领航机器人的传感器检测到障碍物距离领航机器人为d1时，减速，同时跟随机器人也进行减速，当离开障碍物后，领航机器人和跟随机器人加速到原速度；当领航机器人的传感器检测到障碍物距离领航机器人为d2时，立即减速直至停止，同时跟随机器人停止到跟随位置，移动机器人检测环境障碍物信息，并由领航机器人重新进行路径规划。
  8. 如权利要求6所述的基于多传感器的移动机器人队列***的跟随方法，其特征在于，当跟随机器人的传感器检测到障碍物距离领航机器人为d1时，立即减速，当离开障碍物后，加速到达跟随位置；当跟随机器人的传感器检测到障碍物距离领航机器人为d2时，立即减速直至停止，发送信息给领航机器人，领航机器人停止运动，跟随机器人规划路径到达跟随位置，当跟随机器人到达后机器人队列开始运动；机器人队列停止运动后，利用定位装置检测当前位置信息，若没有达到目标点继续前行。
  9. 如权利要求5所述的基于多传感器的移动机器人队列***的跟随方法，其特征在于，当机器人队列到达目的地需要停靠或者需要变换队形时，上位机通过第二无线通信模块传输给领航机器人及跟随机器人，跟随机器人接收到指令后移动到新的跟随位置。
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