Autonomous navigation method for unmanned guniting robot

Abstract

The invention relates to the technical field of coal mine intelligent operation robots, in particular to an autonomous navigation method for an unmanned guniting robot, hardware comprises a laser radar, an inertial measurement unit (IMU) and an industrial personal computer, software comprises a map construction module, a path planning module and an upper computer, and the laser radar obtains point cloud data; the IMU obtains attitude information of the robot and calculates an inter-frame speedometer; the industrial personal computer receives and analyzes sensor data, constructs an environment map, plans an optimal path and transmits a control instruction; the map construction module constructs an environment map, obtains positioning information, sends the positioning information to the path planning module, and calculates odometer data at the same time; the path planning module generates path information and a speed instruction. The method has the two advantages that firstly, an environment map does not need to be scanned in advance in the robot deployment process; and 2, the mapping module provides odometer information for the path planning module while constructing an environment map, so that the dependence on hardware is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent coal mine operation robots, and in particular to an autonomous navigation method for an unmanned shotcrete robot. Background Art

[0002] Underground tunnels are important channels for coal mining and coal transportation, and shotcrete support is an important measure to ensure the stability and safety of tunnels. However, traditional manual shotcrete has disadvantages such as low operating efficiency, unstable operation quality, and high safety risks. Therefore, with the continuous advancement of science and technology and the continuous improvement of the level of intelligence, it is an inevitable development trend to use robots to autonomously complete shotcrete operations instead of manual shotcrete.

[0003] In order to achieve completely unmanned operation, autonomous navigation technology is essential for shotcrete robots. However, most of the current conventional navigation technologies are based on the method of first building a map and then navigating, that is, the robot must scan the environment map in advance and save it before it can be used by the path planning module. This brings certain difficulties and inconveniences to the on-site deployment of shotcrete robots. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes an autonomous navigation method for an unmanned spraying robot to achieve navigation and mapping. This method does not need to establish an environmental map in advance, but continuously performs incremental mapping during the robot's movement. First, the robot's position in the map is obtained, and then the next target point is calculated based on the current position information and the single spraying distance and sent to the path planning module. The path planning module generates a travel path and sends a speed control instruction to the robot to achieve the movement of the robot. During the robot's forward movement, the map will be continuously expanded to facilitate the next path planning. After the robot moves to the designated target point, it starts the spraying operation. After the spraying operation is completed, the robot will repeat the above process until the entire spraying task is completed.

[0005] In a first aspect, the present application provides an autonomous navigation method for an unmanned shotcrete robot, which adopts the following technical solution: An autonomous navigation method for an unmanned shotcrete robot includes a hardware part and a software part. The hardware part includes a laser radar, an IMU, an industrial computer, and a hydraulic controller. The software part includes a map construction module, a path planning module, and a host computer. The software part runs on the industrial computer. The path planning module includes a global planner and a local planner, wherein: Step S1: The laser radar is used to obtain point cloud data; The IMU is used to obtain the posture of the robot and calculate the inter-frame odometer information by pre-integration; Step S2: The industrial computer, as the control center, is responsible for receiving and parsing the point cloud data, the posture and the inter-frame odometer information, building a real-time environment map, obtaining the robot positioning and planning the optimal path, communicating with the host computer, and obtaining the posture of the next target point, completing the reception and transmission of control instructions, wherein, The map construction module uses the point cloud data, the posture and the inter-frame odometer information to construct a real-time environment map, and sends the environment map and the positioning information to the path planning module. At the same time, the module can also calculate the odometer information for use by the local planner through multi-sensor fusion; The path planning module is used to generate path information and speed instructions for controlling the movement of the robot based on the position and posture of the next target point, the environment map and the obstacle data fed back by the laser radar, wherein the path information includes a global path and a local path; Step S3: The hydraulic controller is used to receive the speed control instruction issued by the path planning module and convert it into a control signal of the proportional control valve to realize the movement of the shotcrete robot.

[0006] In a preferred embodiment of the present application, the obstacle data may be further configured as follows: the obstacle data includes static obstacle information and dynamic obstacle information; the path planning module is used to generate path information based on the posture of the next target point, the environment map and the obstacle data fed back by the laser radar, including: The global planner is used to generate the global path based on the posture of the next target point and the static obstacle information; The local planner is used to generate the local path based on the global path and the dynamic obstacle information.

[0007] In a preferred embodiment, the present application may be further configured as follows: the environment map includes the location and outline of obstacles and the passable area information; after the path planning module is used to generate the path information based on the posture of the next target point, the environment map and the obstacle data, it also includes: obtaining information for controlling the motion ability of the robot, the motion ability information including the omnidirectional movement ability and the obstacle avoidance ability; Determining a desired speed based on the obstacle and traversable area information, the omnidirectional mobility capability, and the obstacle avoidance capability, and generating a speed instruction based on the desired speed; Correspondingly, the hydraulic controller is used to generate a control signal of a proportional control valve based on the speed instruction so as to drive the hydraulic motor to realize the movement of the robot.

[0008] In a preferred embodiment, the present application may be further configured such that the functions of the path planning module further include: Comparing in real time whether the current posture is the same as the posture of the next target point, the current posture including the posture and the detection position; If they are different, it is determined that the robot has not reached the next target point, and the remaining arrival time is obtained; Sending the remaining arrival time and remaining distance information to the operation side device; If they are the same, it is determined that the robot has reached the next target point, and an arrival instruction is sent to the control system.

[0009] In a preferred embodiment, the present application may be further configured as follows: after sending the arrival instruction to the control system, the following further includes: The control system generates a laser radar shield closing instruction and executes a spraying operation.

[0010] In a preferred embodiment, the present application may be further configured as follows: after the spraying operation is performed, the following steps are further included: Obtaining an actual shotcrete distance of the robot, and determining whether the actual shotcrete distance is the same as a preset total shotcrete distance; If not, the posture information of the next target point is determined according to the current posture information and the single spraying distance, and the spraying operation is repeated until the preset total spraying distance is completed.

[0011] In summary, this application includes the following beneficial technical effects: 1. By acquiring point cloud data and building maps while moving forward based on the point cloud data, there is no need to build an environmental map in advance, which facilitates the deployment of the shotcrete robot.

[0012] 2. The odometer information of the robot during movement can be constructed without relying on encoders, which reduces the dependence on hardware and has a wider range of applications.

[0013] 3. By comparing the actual shotcrete distance with the preset total shotcrete distance, when the actual shotcrete distance is different from the preset total shotcrete distance, the robot continues to move forward until the actual shotcrete distance is the same as the preset total shotcrete distance, so that the robot can independently complete the entire shotcrete process, greatly reducing the participation of staff. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a robot hardware block diagram provided by an embodiment of the present application; Figure 2 It is a flow chart of an autonomous navigation algorithm provided in an embodiment of the present application; Figure 3 This is a workflow diagram of a shotcrete robot provided in an embodiment of the present application. DETAILED DESCRIPTION

[0015] The following is combined with Figure 1 To Attachment Figure 3 This application is described in further detail.

[0016] After reading this specification, those skilled in the art may make non-creative modifications to this embodiment as needed, but such modifications are protected by patent law as long as they are within the scope of the claims of this application.

[0017] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally indicates that the objects associated before and after are in an "or" relationship. The present invention proposes an autonomous navigation method for an unmanned spraying robot. The spraying robot mainly adopts the following steps to achieve autonomous movement: first, a local environment map is constructed, and its position and posture in the map are obtained; then, the position and posture of the next target point are calculated according to the current position and posture of the spraying robot and the single spraying distance, and the position and posture of the next target point are sent to the path planning module; the path planning module generates a global path and a local path according to the position and posture of the next target point and the obstacle information around the robot, and generates a speed control instruction to realize the movement of the robot; during the forward process, the environmental map will be continuously expanded to provide a basis for subsequent path planning; the above steps are continuously repeated until the entire spraying task is completed. The present invention has two advantages: first, during the robot deployment process, a navigation-and-map-building method is adopted, and there is no need to scan the environment map in advance; second, the mapping module can provide odometer information for the path planning module while building the environment map, thereby reducing the dependence on hardware.

[0019] Furthermore, the autonomous navigation method of the unmanned shotcrete robot is developed based on the ROS architecture, which is equipped with multiple devices and modules. The hardware part includes a laser radar, an IMU (inertial measurement unit), an industrial computer and a hydraulic controller, etc. The software part includes a map construction module, a path planning module and a host computer, etc. The software part runs on the industrial computer. The path planning module includes a global planner and a local planner. Data can be transmitted between various devices and modules in the form of topics, among which, Figure 1As shown, it is a hardware schematic diagram provided in an embodiment of the present application. The industrial computer serves as the control center of the robot. It constructs an environmental map and odometer information by fusing the information of the lidar and the IMU, generates the optimal moving path and movement speed, and sends control instructions to the hydraulic controller, thereby controlling the movement of the chassis of the shotcrete robot.

[0020] During the working process of the shotcrete robot, the program is first started and initialized. The industrial computer can establish a communication connection with the laser radar through the network port to obtain the data collected by the laser radar. The industrial computer can read the data collected by the IMU through the USB port. The industrial computer can also communicate with the host computer through a wireless connection to realize remote monitoring command issuance and status feedback. The industrial computer can communicate with the hydraulic controller through the CAN bus. The hydraulic controller drives the hydraulic motor by controlling the proportional control valve, thereby realizing the movement of the shotcrete robot.

[0021] Step S1: LiDAR is used to scan the surrounding environment by emitting laser beams and receiving reflected signals to obtain point cloud data, providing a basis for building a lane map; the above point cloud data includes static obstacle information and dynamic obstacle information.

[0022] IMU is used to provide real-time feedback on the robot's posture information and calculate the inter-frame odometer information through pre-integration operations. The robot's posture can be the robot's yaw angle, etc. The inter-frame odometer information represents the robot's moving distance, such as the robot moves 2 meters forward and 4 meters backward. Among them, IMU can obtain the robot's angular velocity and acceleration, and perform pre-integration operations on the angular velocity and acceleration to obtain the posture and inter-frame odometer information. By using IMU, the positioning accuracy of the robot can be effectively improved. The embodiment of the present application does not limit the specific calculation process of calculating the inter-frame odometer information.

[0023] Step S2: The industrial computer can serve as the control center to receive and analyze point cloud data, posture and inter-frame odometer information, and build a real-time environment map; then obtain the robot positioning and plan the optimal path, and communicate with the host computer; and obtain the posture of the next target point to complete the reception and transmission of control instructions, such as Figure 2As shown, it is a flowchart of an autonomous navigation algorithm provided by an embodiment of the present application, which is mainly composed of two modules: map construction and path planning. The map construction module constructs the environment map by acquiring information from sensors such as laser radar and IMU, namely point cloud data, posture and inter-frame odometer information, and sends the map (probability grid map) and the positioning information of the robot in the map to the path planning module. At the same time, the map construction module generates the robot's odometer information for local path planning; the path planning module first expands the above-mentioned probability grid map, and then the global planner generates a global path based on the target point information and the global cost map (static obstacle information after expansion), and the local planner generates a local path and a robot speed control instruction based on the global path and the local cost map (dynamic obstacle information after expansion) and sends it to the hydraulic controller to realize the movement of the robot. The local path will try to fit the global path while avoiding obstacles; the robot will expand the environment map while moving forward.

[0024] Furthermore, the path planning module is an important component of the robot's autonomous navigation system, which can generate path information based on the posture of the next target point, the environmental map and obstacle data; wherein the path planning module includes a global planner and a local planner, and the path information includes a global path and a local path.

[0025] Furthermore, the global planner generates a global path based on the pose of the next target point and the static obstacle data in the environment map; the static obstacle data is the point cloud data corresponding to the obstacles that do not move; specifically, the global planner traverses each grid in the environment map, and when there is an obstacle in the grid, the grid is not planned into the global path, thereby generating an optimal path, namely the global path.

[0026] Furthermore, the local planner is used to generate a local path based on the global path and dynamic obstacle data. At the same time, in order to make the local path smoother, the local planner needs to obtain the real-time feedback speed of the shotcrete robot. This information is calculated by the odometer information sent by the map construction module. Specifically, the dynamic obstacle information is the point cloud data of the moving obstacle; the robot moves along the above-mentioned global path. When there is a dynamic obstacle in the global path, if the obstacle can be avoided, the robot automatically avoids the obstacle; if the robot cannot avoid the obstacle, the robot stops moving until the obstacle disappears and continues to move forward, that is, in the embodiment of the present application, the local path will try to fit the global path as closely as possible, and when avoiding obstacles, it will deviate from the global path for a short time. While moving forward, the laser radar will acquire new point cloud data in real time to expand the map.

[0027] Furthermore, the path planning module can also control the motion capability of the shotcrete robot, and the motion capability information includes the ability to realize the omnidirectional movement and obstacle avoidance of the shotcrete robot; the expected speed is determined based on the obstacle information, the passable area information and the robot's motion capability, that is, if there is an obstacle or a turn is required during the robot's movement, the robot is controlled to slow down to avoid problems such as the robot rolling over, and the robot will travel at the expected speed; in the embodiment of the present application, the expected speed of the robot changes in real time. Among them, the passable area information represents a grid without obstacles.

[0028] Step S3: The hydraulic controller drives the hydraulic motor by controlling the proportional control valve, so that the robot can drive autonomously according to the planned path; wherein the industrial computer can communicate with the hydraulic controller through the CAN bus.

[0029] At the same time, the path planning module will also detect in real time whether the robot has reached the next target point, that is, compare the current position of the robot with the position of the next target point. The current position includes the detection position and posture of the current robot; if the detection position of the robot is the same as the position of the next target point, and the posture of the robot is the same as the preset posture of the next target point, it means that the robot has reached the next target point. Generate an arrival instruction and send it to the control center to generate a laser radar shield closing instruction to close the laser radar shield, and then perform the spraying operation; when the spraying operation is completed, generate a laser radar shield opening instruction.

[0030] If the detected position of the robot is different from the position of the next target point, or the posture of the robot is different from the preset posture of the next target point, it indicates that the robot has not reached the next target point; if it has not reached the next target point, the remaining arrival time is obtained, and the remaining arrival time and remaining distance information are sent to the operating side device (for example, a handheld terminal), where the remaining arrival time is determined based on the remaining distance of the robot and the preset moving speed of the robot.

[0031] Further, after the spraying operation is completed, the actual spraying distance of the robot is obtained, and it is determined whether the actual spraying distance is the same as the preset total spraying distance; If they are the same, it means that the robot has completed the spraying operation; If they are different, the posture of the next target point is determined according to the current posture information and the single shotcrete distance, and the above steps are repeated until the preset total shotcrete distance is reached. It can be understood that during the entire shotcrete operation, the robot's autonomous navigation technology ensures that it can complete the shotcrete task efficiently and accurately, while reducing manual intervention.

[0032] Furthermore, the industrial computer will also save the map that the robot has walked for later use.

[0033] like Figure 3 As shown, it is a workflow diagram of a shotcrete robot provided in an embodiment of the present application. The industrial computer can also obtain a single shotcrete distance and a preset total shotcrete distance, which are input by the user in the front-end interface when the robot is started; it can be understood that the single shotcrete distance and the total shotcrete distance are the basis for the robot to perform autonomous navigation and operation planning, which determines the coverage range of the robot's shotcrete each time and the total working area that needs to be covered. The posture of the next target point includes the position of the next target point and the posture of the next target point; the position of the next target point is obtained according to the positioning of the robot and the single shotcrete distance; the process of determining the position of the next target point includes: adding the current position to the single shotcrete distance to obtain the position of the next target point; the posture of the next target point is given manually to ensure that the robot is parallel to both sides of the lane.

[0034] The above description is only a partial implementation method of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. An autonomous navigation method for an unmanned shotcrete robot, characterized in that: It includes a hardware part and a software part. The hardware part includes a laser radar, an IMU, an industrial computer, and a hydraulic controller. The software part includes a map construction module, a path planning module, and a host computer. The software part runs on the industrial computer. The path planning module includes a global planner and a local planner, wherein: Step S1: The laser radar is used to obtain point cloud data; The IMU is used to obtain the posture of the robot and calculate the inter-frame odometer information by pre-integration; Step S2: The industrial computer, as the control center, is responsible for receiving and parsing the point cloud data, the posture and the inter-frame odometer information, building a real-time environment map, obtaining the robot positioning and planning the optimal path, communicating with the host computer, and obtaining the posture of the next target point, completing the reception and transmission of control instructions, wherein, The map construction module uses the point cloud data, the posture and the inter-frame odometer information to construct a real-time environment map, and sends the environment map and the positioning information to the path planning module. At the same time, the module can also calculate the odometer information for use by the local planner through multi-sensor fusion; The path planning module is used to generate path information and speed instructions for controlling the movement of the robot based on the position and posture of the next target point, the environment map and the obstacle data fed back by the laser radar, wherein the path information includes a global path and a local path; Step S3: The hydraulic controller is used to receive the speed control instruction issued by the path planning module and convert it into a control signal of the proportional control valve to realize the movement of the shotcrete robot.

2. The autonomous navigation method for an unmanned shotcrete robot according to claim 1, characterized in that: The obstacle data includes static obstacle information and dynamic obstacle information. The path planning module is used to generate path information based on the position of the next target point, the environment map and the obstacle data fed back by the laser radar, including: The global planner is used to generate the global path based on the posture of the next target point and the static obstacle information; The local planner is used to generate the local path based on the global path and the dynamic obstacle information.

3. The autonomous navigation method for an unmanned shotcrete robot according to claim 1, characterized in that: The environment map includes the location and outline of obstacles and passable area information. After the path planning module is used to generate path information based on the position and posture of the next target point, the environment map and the obstacle data, it also includes: information for controlling the robot's motion ability, the motion ability information includes omnidirectional movement ability and obstacle avoidance ability; Determining a desired speed based on the obstacle and traversable area information, the omnidirectional mobility capability, and the obstacle avoidance capability, and generating a speed instruction based on the desired speed; Correspondingly, the hydraulic controller is used to generate a control signal of a proportional control valve based on the speed instruction so as to drive the hydraulic motor to realize the movement of the robot.

4. An autonomous navigation method for an unmanned shotcrete robot according to any one of claims 1 to 3, characterized in that: The functions of the path planning module also include: Comparing in real time whether the current posture is the same as the posture of the next target point, the current posture including the posture and the detection position; If they are different, it is determined that the robot has not reached the next target point, and the remaining arrival time is obtained; Sending the remaining arrival time and remaining distance information to the operation side device; If they are the same, it is determined that the robot has reached the next target point, and an arrival instruction is sent to the control system.

5. The autonomous navigation method for an unmanned shotcrete robot according to claim 4, characterized in that: After sending the arrival instruction to the control system, the method further includes: The control system generates a laser radar shield closing instruction and executes a spraying operation.

6. The autonomous navigation method for an unmanned shotcrete robot according to claim 5, characterized in that: After the shotcrete operation is performed, the method further comprises: Obtaining an actual shotcrete distance of the robot, and determining whether the actual shotcrete distance is the same as a preset total shotcrete distance; If not, the posture information of the next target point is determined according to the current posture information and the single shotcrete distance, and the shotcrete operation is repeated until the preset total shotcrete distance is completed.

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