Unmanned vehicle autonomous exploration system in unknown environment

Abstract

The invention provides an unmanned vehicle autonomous exploration system in an unknown environment. The unmanned vehicle autonomous exploration system comprises a Mecanum wheel unmanned vehicle, a control module, a communication module, a driving module and a sensing module, the sensing module senses the environment around the unmanned vehicle to generate point cloud; in the control module, a mapping positioning module performs three-dimensional and two-dimensional mapping by using the point cloud; an exploration planner explores a logic path based on the three-dimensional map in combination with global planning and local planning and determines a target point; and the path planning module performs path planning according to the two-dimensional map and the target point, and then the motion control module generates a control instruction and transmits the control instruction to the driving module so as to realize control of the chassis of the Mecanum wheel unmanned vehicle. And the visualization module is used for carrying out visualization display on maps, paths and the like. According to the method, the problem that the autonomous exploration capability of the unmanned vehicle is insufficient in an unknown environment can be solved, and particularly the problem that global planning and local path planning are difficult to effectively carry out in a complex or dynamic environment can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent unmanned systems, and particularly to an autonomous exploration system for unmanned vehicles in unknown environments. Background Art

[0002] With the rapid development of autonomous driving technology, unmanned vehicles have gradually become key equipment in intelligent transportation, logistics transportation, and exploration missions. Unmanned vehicles can perform autonomous tasks in complex and unknown environments, reducing the need for human intervention and greatly improving work efficiency and safety. However, in these application scenarios, one of the main technical challenges faced by unmanned vehicles is how to achieve efficient environmental perception and path planning, especially in dynamic or unknown environments for exploration and navigation.

[0003] LiDAR (Light Detection and Ranging), as the core sensor for environmental perception of unmanned vehicles, has become the mainstream environmental detection means due to its high-precision, long-distance, and all-weather perception capabilities. LiDAR can generate high-resolution three-dimensional point cloud data to help unmanned vehicles identify surrounding obstacles and terrain. However, traditional LiDAR systems often can only work within a certain range and angle, restricting the environmental perception ability of unmanned vehicles. To solve this problem, 360-degree LiDAR developed in recent years can achieve real-time perception of the omnidirectional environment, providing more comprehensive data support for the autonomous exploration of unmanned vehicles.

[0004] Existing autonomous exploration systems for unmanned vehicles mostly rely on accurate maps and predefined paths, but in unknown environments, the system often lacks effective strategies to dynamically respond to complex scenarios. To achieve better adaptability in the exploration of unknown environments by unmanned vehicles, researchers have proposed various exploration and path planning algorithms. However, how to improve the exploration efficiency of unmanned vehicles while ensuring their safety and reliability remains a key issue in the current field of unmanned vehicles. Summary of the Invention

[0005] In view of this, the present invention provides an autonomous exploration system for unmanned vehicles in unknown environments, aiming to solve the problem of insufficient autonomous exploration ability of unmanned vehicles in the prior art, especially the problem that unmanned vehicles are difficult to effectively perform global planning and local path planning in complex or dynamic environments.

[0006] To solve the above technical problems, the present invention is implemented as follows.

[0007] An autonomous exploration system for unmanned vehicles in unknown environments, the system includes: a Mecanum wheel unmanned vehicle, a control module, a communication module, a drive module, and a perception module;

[0008] The perception module senses the environment around the unmanned vehicle through LiDAR to generate real-time point cloud data;

[0009] The control module includes a mapping and localization module, an exploration planner, a path planning module, a motion control module, and a visualization module; the mapping and localization module uses real-time point cloud data to perform 3D mapping and 2D mapping; the exploration planner explores the logical path and determines the target point based on the 3D map in combination with global planning and local planning; the path planning module performs the execution path planning according to the 2D map and the target point; the motion control module calculates the control quantity according to the execution path, generates a control instruction and transmits it to the drive module through the communication module; the visualization module visually displays the 3D map, 2D map, the current position of the unmanned vehicle, the target point, and the planned path;

[0010] The drive module is used to control the chassis of the Mecanum wheel unmanned vehicle.

[0011] Preferably, the entire exploration space obtains global exploration units through grid division, the global exploration units are further divided into local exploration units through grid division, and multiple point clouds are included in the local exploration units; the local exploration units are identified as three states: unknown, occupied, and unoccupied according to the statistics of the internal point cloud state; the global exploration units are identified as three states: being explored, unexplored, and explored according to the state statistics of the internal local exploration units; the exploration window is an area composed of multiple global exploration units selected with the unmanned vehicle as the center; the passable area is an area composed of local exploration units determined to be unoccupied; the obstacle area is an area composed of local exploration units determined to be occupied; the unknown area is an area composed of local exploration units determined to be unknown;

[0012] The exploration planner includes an environmental information update unit, a viewpoint generation and screening unit, a traveling salesman problem calculation unit, and a target point determination unit;

[0013] The environmental information update unit is used to update the environmental information according to the 3D map when the unmanned vehicle crosses the central global exploration unit within the exploration window, including local exploration unit update, global exploration unit update, exploration window sliding update, passable area and obstacle area update; among them, the local exploration unit update is to update the state according to the point cloud within the unit; the global exploration unit update is to update the states of multiple global exploration units that leave after the exploration window slides, and add the global exploration units updated to the being explored state to the exploration queue;

[0014] The viewpoint generation and screening unit is used to fill the candidate viewpoints at a set distance interval in the passable area, and screen out a group of preferred viewpoints according to the observable obstacle situation corresponding to the candidate viewpoints and the cost for the unmanned vehicle to reach the candidate viewpoints;

[0015] The traveling salesman problem calculation unit is used to calculate the asymmetric traveling salesman problem of the set of preferred viewpoints, obtain the exploration order of the set of preferred viewpoints, that is, obtain the local exploration logical path; calculate the traveling salesman problem of all global exploration units in the exploration queue, obtain the exploration order of these global exploration units in the exploration, that is, obtain the global exploration logical path; merge the local exploration logical path and the global exploration logical path to obtain the overall logical path, and send it to the visualization module and the target point determination unit;

[0016] The target point determination unit is used to determine the next target point of the driverless vehicle according to the overall logical path, and send the determined target point to the path planning module and the visualization module.

[0017] Preferably, in the viewpoint generation and screening unit, the method of screening preferred viewpoints is as follows:

[0018] Update the parameters of all candidate viewpoints. The parameters include the observable obstacle area Observable_n within the exploration window, the observable boundary size Frontier_n between the passable area and the unknown area within the exploration window, and the cost Inertial_n for the driverless vehicle to reach the candidate viewpoint;

[0019] Conduct the first round of screening: If the Observable_n parameter of the candidate viewpoint is less than the area threshold, or the Frontier_n parameter is less than the boundary size threshold, the current candidate viewpoint is eliminated;

[0020] Conduct the second round of screening: For the remaining candidate viewpoints, calculate the weighted sum of the three parameters and sort them, and select a set number of candidate viewpoints as the preferred viewpoints in descending order of scores.

[0021] Preferably, the method for determining the cost Inertial_n for the driverless vehicle to reach the candidate viewpoint is as follows:

[0022] Inertial_n = k1·orientation_from_robot + k2·distance_from_robot

[0023] + k3·orientation_from_path

[0024] where, orientation_from_robot is the angle between the candidate viewpoint orientation and the heading angle of the driverless vehicle; distance_from_robot is the absolute distance from the candidate viewpoint to the driverless vehicle; orientation_from_path is the angle between the candidate viewpoint orientation and the path orientation of the driverless vehicle, where the path orientation is represented by the average value of the heading angles of the driverless vehicle in the past period of time; k1, k2, and k3 are weight values.

[0025] Preferably, the target point determination unit determines the next target point of the driverless vehicle according to the overall logical path as follows:

[0026] Determine the head view point and the tail view point of the overall logical path, calculate the weighted sum of orientation_from_robot and distance_from_robot for the head view point and the tail view point respectively, and take the larger one as the next target point of the driverless vehicle; where orientation_from_robot is the angle between the candidate view point azimuth and the heading angle of the driverless vehicle; distance_from_robot is the absolute distance from the candidate view point to the driverless vehicle.

[0027] Preferably, in the view point generation and screening unit, the determination method of the local exploration unit state is as follows: count the states of the point cloud in a local exploration unit, including unknown, occupied, and unoccupied, and divide the local exploration unit into three states: unknown, occupied, and unoccupied according to the proportion of the point cloud states;

[0028] The determination method of the global exploration unit state is as follows: count the states of the local exploration units included in the global exploration unit; if the quantity of the local exploration units in the occupied state or the unoccupied state is lower than the set exploration threshold, the global exploration unit is determined to be in the unexplored state; if the quantity of the local exploration units in the known state is higher than the exploration threshold, the global exploration unit is determined to be in the exploring state; if the quantity of the local exploration units in the known state is higher than the set completion threshold, the global exploration unit is determined to be in the explored state.

[0029] Preferably, the exploration window consists of 3×3 global exploration units.

[0030] Preferably, in the control module, the mapping and localization module uses the Aloam algorithm for 3D mapping and the Gmapping algorithm for 2D mapping; the path planning module uses the Teb-local-planner local path algorithm to dynamically plan the execution path of the driverless vehicle.

[0031] Preferably, the lidar uses a 360-degree lidar.

[0032] Preferably, the communication module uses the serial communication method to transmit the speed command of the driverless vehicle chassis in the form of 7 hexadecimal data; the speed command includes the check information of the first 2 bits and the last 2 bits, and the middle 3 bits of data respectively represent the front and rear, left and right linear speeds and the rotational angular speed of the driverless vehicle.

[0033] Beneficial effects:

[0034] (1) The present invention proposes an autonomous exploration system for driverless vehicles based on 360-degree lidar, which combines high-precision lidar sensing technology, Mecanum wheel drive system and advanced exploration and path planning algorithms to solve the problems of environmental perception and autonomous exploration in the prior art. This system can achieve three-dimensional environment reconstruction, global and local exploration planning during the driving process of the driverless vehicle, and present the exploration status of the driverless vehicle in real time through a visualization interface, meeting the autonomous exploration requirements in unknown environments.

[0035] (2) Multi-level exploration planning mechanism: When the exploration planner of this system plans the logical path, it adopts an exploration strategy that combines global planning and local planning, which can not only determine the exploration path of the driverless vehicle from a global perspective, but also make flexible adjustments in the local environment. Compared with the single planning method in the prior art, the present invention ensures that the exploration path of the driverless vehicle is more reasonable and efficient through candidate viewpoint generation and optimal selection, thus significantly improving the speed and accuracy of task completion.

[0036] (3) Rational selection of target points: In the prior art, since the beginning and end of the path are relatively similar, it is difficult to make a choice, resulting in the phenomenon of the driverless vehicle shaking its head. This system improves the method of determining the target point after obtaining the logical path, and uses the angle between the viewpoint azimuth and the heading angle of the driverless vehicle and the distance between the viewpoint and the driverless vehicle to characterize the characteristics of the beginning and end of the logical path, so as to rationally select the target point.

[0037] (4) Efficient mapping and positioning capabilities: The present invention combines the ALOAM three-dimensional mapping algorithm and the Gmapping two-dimensional mapping algorithm, which can quickly and accurately perform three-dimensional reconstruction and positioning of the environment. Compared with the prior art, the present invention can not only provide an accurate three-dimensional map for global exploration, but also quickly generate path planning through a two-dimensional map, reducing the computational complexity and achieving a faster real-time response.

[0038] (5) Adaptive path planning: The system dynamically plans the actual travel path of the driverless vehicle through the Teb-local-planner local path algorithm, and can respond to changes and uncertainties in the environment in real time. Compared with traditional path planning methods, the present invention has higher flexibility and can adjust the path in real time according to the relative positions of the driverless vehicle, obstacles and target points, ensuring that the driverless vehicle can complete the exploration task safely and smoothly.

[0039] (6) Reliability of data transmission: The present invention realizes precise control of the driverless vehicle chassis through serial communication, and uses hexadecimal data in a specific format for the transmission of speed commands. Compared with traditional communication methods, the communication module of the present invention has strong anti-interference ability, can ensure the accuracy and reliability of data transmission, and improves the stability of the system. Description of the Drawings

[0040] Figure 1 This is the design drawing of the mecanum wheeled unmanned vehicle mechanical structure provided by the present invention.

[0041] Figure 2 This is the structure diagram of the unmanned vehicle autonomous exploration system provided by the present invention.

[0042] Figure 3 This is the working principle diagram of the unmanned vehicle autonomous exploration system provided by the present invention

[0043] Figure 4 This is the working flow chart of the exploration system provided by the present invention.

[0044] Figure 5 This is the schematic diagram of the visualization interface of the exploration system provided by the present invention. Detailed implementation manners

[0045] The following takes embodiments in conjunction with the attached drawings and describes the present invention in detail.

[0046] The embodiment of the present invention provides an unmanned vehicle autonomous exploration system in an unknown environment. The overall framework of the system consists of a perception module, a control module, a communication module, a drive module, and a mecanum wheeled unmanned vehicle. The control module communicates with the drive module through the communication module. The drive module drives the chassis of the mecanum wheeled unmanned vehicle to move to realize the movement of the unmanned vehicle. Figure 1 This is the mechanical structure diagram of the mecanum wheeled unmanned vehicle.

[0047] Figure 2 It shows the specific hardware composition of an unmanned vehicle autonomous exploration system. The hardware includes a 360-degree lidar, an on-vehicle computer, and a mecanum wheeled unmanned vehicle.

[0048] Among them, the 360-degree lidar is the perception module, which can obtain the 360-degree environmental point cloud for the on-vehicle computer to use. The 360-degree perception ability of the lidar ensures that while the unmanned vehicle perceives the environment in all directions, it can avoid obstacles and improve the efficiency and safety of autonomous exploration.

[0049] The on-vehicle computer constitutes the control module of the entire exploration system in the form of software and executes main functions such as mapping and positioning, exploration planning, and path planning;

[0050] The Kename wheeled unmanned vehicle is the execution unit of the system. A drive module is provided on the Kename wheeled unmanned vehicle. The control instructions sent by the control module reach the drive module through the communication module. The drive module converts them into corresponding currents to drive the motor, driving the mecanum wheel chassis, so that the system moves at the desired linear velocity and angular velocity. The 360-degree lidar and the on-vehicle computer are also integrated on the Kename wheeled unmanned vehicle.

[0051] The communication module can adopt the serial communication method to transmit the speed command of the unmanned vehicle chassis in the form of 7 hexadecimal data; the speed command includes the check information of the first 2 bits and the last 2 bits, and the middle 3 bits of data respectively represent the front and rear, left and right linear speeds and the rotational angular speed of the unmanned vehicle, ensuring the accuracy and reliability of data transmission.

[0052] Figure 2 The schematic diagram of the unmanned vehicle autonomous exploration system of the present invention is shown. As shown in the figure, the 360-degree lidar obtains the initial point cloud information of the environment around the unmanned vehicle, and it is received and processed by the control module.

[0053] The control module includes a mapping and localization module, an exploration planner, a path planning module, a motion control module, and a visualization module; after receiving the update of the point cloud information, the mapping and localization module in the control module performs 3D mapping and 2D mapping to obtain a 3D map and a 2D map. The 3D map is used for the global reconstruction of the environment, and the 2D map is used for rapid path planning. The 3D map is sent to the visualization module for display and also sent to the exploration planner; the 2D map is sent to the path planning module.

[0054] The exploration planner explores the logical path and determines the target point based on the 3D map in combination with the global planning and local planning exploration logics, ensuring that the unmanned vehicle can efficiently determine the next exploration target and the optimal traveling path in the unknown environment. The logical path and the target point are sent to the visualization module for display, and the target point is sent to the path planning module to participate in path planning.

[0055] The path planning module performs path planning according to the 2D map and the target point to ensure that the unmanned vehicle can drive to the next target point along the planned path; the planned execution path is sent to the visualization module for display and also sent to the motion control module.

[0056] The motion control module calculates the control quantity according to the execution path, generates a control command and transmits it to the drive module through the communication module to drive the movement of the Mecanum wheel unmanned vehicle chassis. The control quantity includes the linear speed and angular speed control quantities of the unmanned vehicle, and generates hexadecimal data that can be used for the movement command of the unmanned vehicle.

[0057] In a preferred embodiment, the mapping and localization module uses the Aloam algorithm for 3D mapping and localization; uses the Gmapping algorithm for 2D mapping. The path planning module uses the Teb-local-planner local path algorithm to dynamically plan the execution path of the unmanned vehicle. The visualization module uses the rviz visualization interface to display the 3D map, 2D map, the current position of the unmanned vehicle, the target point, and the planned path in real time, facilitating intuitive monitoring and management by the operator or the monitoring system.

[0058] The exploration planner includes an environmental information update unit, a viewpoint generation and screening unit, a traveling salesman problem calculation unit, and a target point determination unit. Figure 3 It shows the workflow of these component units after the exploration planner receives the update information of the three-dimensional map and enters the exploration planning function. The relationship between the various modules in the process is as Figure 3 shown in the exploration planner on the right side in the figure. The exploration planning process of the exploration planner includes the following steps:

[0059] Step S31: The environmental information update unit updates the environmental information.

[0060] First, the following definitions are made: The entire exploration space obtains global exploration units through grid division. The global exploration units are further divided by grids to obtain local exploration units, and multiple point clouds are included in the local exploration units. The local exploration units are identified as three states: unknown, occupied, and unoccupied according to the statistics of the internal point cloud states; the global exploration units are identified as three states: being explored, unexplored, and explored according to the state statistics of the internal local exploration units; the exploration window is an area composed of multiple global exploration units centered on the unmanned vehicle, preferably a 3×3 area. The passable area is an area composed of local exploration units determined to be unoccupied; the obstacle area is an area composed of local exploration units determined to be occupied; the unknown area is an area composed of local exploration units determined to be unknown.

[0061] Among them, the determination method of the local exploration unit state is: counting the states of all point clouds in a local exploration unit, including unknown, occupied, and unoccupied, and dividing the local exploration unit into three states: unknown, occupied, and unoccupied according to the proportion of the point cloud states. Among them, the point cloud state is determined by a threshold according to the point cloud intensity.

[0062] The determination method of the global exploration unit state is: counting the states of the local exploration units included in the global exploration unit; if the quantity of local exploration units in the occupied state or the unoccupied state is lower than the set exploration threshold, the global exploration unit is identified as the unexplored state; if the quantity of local exploration units in the known state is higher than the exploration threshold, the global exploration unit is identified as the being explored state; if the quantity of local exploration units in the known state is higher than the set completion threshold, the global exploration unit is identified as the explored state.

[0063] In this step, the environmental information update includes local unit update, global unit update, exploration window sliding update, and passable area and obstacle area update. When the unmanned vehicle crosses the central global exploration unit within the exploration window, these environmental information are updated according to the three-dimensional map.

[0064] Among them, the local exploration unit is updated to update the state according to the point cloud within the unit; the global exploration unit is updated to update the states of multiple global exploration units that leave after the exploration window slides, and add the global exploration units updated to the exploration state to the exploration queue. That is to say, the global exploration units stored in the exploration queue are the global exploration units that have been slid over by the exploration window and are still in the exploration state after being drawn out of the exploration window.

[0065] Step S32: The viewpoint generation and screening unit generates candidate viewpoints and generates a set of preferred viewpoints through screening.

[0066] In this step, the viewpoint generation and screening unit spreads the candidate viewpoints in the passable area at a certain distance interval and then enters the screening.

[0067] Viewpoint screening is to screen out a set of preferred viewpoints according to the observable obstacle situation corresponding to the candidate viewpoints and the cost for the unmanned vehicle to reach the candidate viewpoints.

[0068] In this embodiment, the screening process is divided into several steps:

[0069] (1) First, update the relevant parameters of all candidate viewpoints. The parameters include the observable obstacle area Observable_n within the exploration window, the size of the observable boundary Frontier_n between the passable area and the unknown area within the exploration window, and the cost Inertial_n for the unmanned vehicle to reach the candidate viewpoints. Among them

[0070] Observable_n is the number of local exploration units in the observable obstacle area within the exploration window.

[0071] Frontier_n is the number of local exploration units on the boundary between the passable area and the unknown area within the exploration window.

[0072] Inertial_n are some factors affecting the driving inertia of the unmanned vehicle. In an optimized implementation, Inertial_n includes the angle between the azimuth of the candidate viewpoint and the heading angle of the unmanned vehicle, the distance between the viewpoint and the unmanned vehicle, and the smoothness characterization of the exploration path. The determination method of Inertial_n is:

[0073] Inertial_n = k1·orientation_from_robot + k2·distance_from_robot

[0074] + k3·orientation_from_path

[0075] Among them, orientation_from_robot is the angle between the candidate viewpoint orientation and the heading angle of the driverless vehicle, expressed in the form of the dot product of two vectors. When the angle is less than 90 degrees, the value is positive; when it is greater than 90 degrees, the value is negative; distance_from_robot is the absolute distance from the candidate viewpoint to the driverless vehicle. Adjusting k2 can adjust the priority of the viewpoint distance in the exploration strategy; orientation_from_path is the angle between the candidate viewpoint orientation and the path orientation of the driverless vehicle, expressed in the form of the dot product of two vectors, where the path orientation is represented by the average value of the heading angles of the driverless vehicle in the past period of time. This item is used to increase the smoothness of the exploration path; k1, k2, and k3 are weights.

[0076] (2) After updating the parameters of the candidate viewpoints, the first-round screening is carried out: if Observable_n < Observable_thr or Frontier_n < Frontier_thr of the candidate viewpoint, the current candidate viewpoint is eliminated. Among them, Observable_thr is the set area threshold, and Frontier_thr is the set boundary size threshold.

[0077] (3) For the remaining candidate viewpoints, calculate the weighted sum of the above three parameters, sort them, and select a set number of candidate viewpoints as the preferred viewpoints according to the scores from high to low.

[0078] Step S33: The traveling salesman problem calculation unit calculates the traveling salesman problem to obtain a logical path.

[0079] In this step, the traveling salesman problem calculation unit calculates the asymmetric traveling salesman problem of a group of preferred viewpoints determined in Step S2 to obtain the exploration order of this group of preferred viewpoints, that is, to obtain the local exploration logical path. And calculate the traveling salesman problem of all the global exploration units in the exploration queue during exploration to obtain the exploration order of these global exploration units during exploration, that is, to obtain the global exploration logical path; merge the local exploration logical path and the global exploration logical path to get the overall logical path and send it to the visualization module for display on the rviz visualization interface.

[0080] Step S34: The target point determination unit determines the target point.

[0081] After obtaining the overall logical path in the previous step, it is necessary to determine in which direction the unmanned vehicle will execute exploration along this path. In the prior art, the head and tail of the path are relatively similar, making it difficult to make a choice and resulting in the phenomenon of the unmanned vehicle shaking its head. In this step, the head viewpoint and tail viewpoint of the logical path are obtained, and the weighted sums of orientation_from_robot and distance_from_robot are calculated respectively for the head viewpoint and tail viewpoint, and the larger one is taken as the next target point of the unmanned vehicle and sent to the path planning module and the visualization module.

[0082] After completing the exploration planning, the teb-local-planner node plans the execution path to the target point and displays it on the rviz visualization interface. At the same time, the movebase node performs the desired speed calculation and sends the desired speed of the unmanned vehicle to the vel-control node. The vel-control node converts the speed into hexadecimal data according to the rules, encapsulates it and sends it to the chassis. Finally, the Mecanum wheel chassis receives the encapsulated speed command data, calculates the current required for each motor from the data, and drives the Mecanum wheels to rotate, enabling the unmanned vehicle system to achieve the desired movement.

[0083] The final operation effect of the exploration system is as Figure 5 shown, including information such as a 3D map, a logical path, and an execution path.

[0084] By introducing the omnidirectional environment perception technology based on a 360-degree lidar, combining the independently designed Mecanum wheel unmanned vehicle chassis and advanced exploration and path planning algorithms, the present invention can achieve efficient autonomous exploration tasks in complex unknown environments. This system can not only be used for the exploration of unmanned vehicles in indoor and outdoor environments, but also be widely applied to fields such as rescue, reconnaissance, and logistics that require autonomous navigation.

[0085] The above specific embodiments only describe the design principle of the present invention. The shapes and names of the components in this description can be different and are not limited. Therefore, those skilled in the art of the present invention can modify or equivalently replace the technical solutions recorded in the foregoing embodiments; and these modifications and replacements do not depart from the gist and technical solutions of the present invention, and shall all fall within the protection scope of the present invention.

Claims

1. An autonomous exploration system for an unmanned vehicle in an unknown environment, characterized in that, The system includes: a Mecanum wheeled unmanned vehicle, a control module, a communication module, a driving module, and a sensing module; The sensing module senses the environment around the unmanned vehicle through a lidar to generate real-time point cloud data; The control module includes a mapping and localization module, an exploration planner, a path planning module, a motion control module, and a visualization module; The mapping and localization module uses the real-time point cloud data for 3D mapping and 2D mapping; The exploration planner explores the logical path and determines the target point based on the 3D map combined with global planning and local planning; The path planning module performs the execution path planning according to the 2D map and the target point; The motion control module calculates the control quantity according to the execution path and generates control instructions to be transmitted to the driving module through the communication module; The visualization module visually displays the 3D map, the 2D map, the current position of the unmanned vehicle, the target point, and the planned path; The driving module is used to control the chassis of the Mecanum wheeled unmanned vehicle.

2. The unmanned vehicle autonomous exploration system in an unknown environment according to claim 1, wherein The entire exploration space obtains global exploration units through grid division, and the global exploration units are further divided into local exploration units, and multiple point clouds are included in the local exploration units; The local exploration units are determined to be in three states: unknown, occupied, and unoccupied according to the statistics of the internal point cloud states; The global exploration units are determined to be in three states: being explored, unexplored, and explored according to the statistics of the internal local exploration unit states; The exploration window is an area composed of multiple global exploration units centered on the unmanned vehicle; The passable area is an area composed of local exploration units determined to be unoccupied; The obstacle area is an area composed of local exploration units determined to be occupied; The unknown area is an area composed of local exploration units determined to be unknown; The exploration planner includes an environmental information update unit, a viewpoint generation and screening unit, a traveling salesman problem calculation unit, and a target point determination unit; The environmental information update unit is used to update the environmental information according to the 3D map when the unmanned vehicle crosses the central global exploration unit within the exploration window, including local exploration unit update, global exploration unit update, exploration window sliding update, passable area and obstacle area update; Among them, the local exploration unit update is to update the state according to the point cloud within the unit; The global exploration unit update is to update the states of multiple global exploration units that leave after the exploration window slides, and add the global exploration units updated to the explored state to the exploration queue; The viewpoint generation and screening unit is used to fill the candidate viewpoints at a set distance interval in the passable area, and screen out a group of preferred viewpoints according to the observable obstacle situation corresponding to the candidate viewpoints and the cost for the unmanned vehicle to reach the candidate viewpoints; The traveling salesman problem calculation unit is used to calculate the asymmetric traveling salesman problem of the set of preferred viewpoints, obtain the exploration order of the set of preferred viewpoints, that is, obtain the local exploration logical path; calculate the traveling salesman problem of all global exploration units in the exploration queue, obtain the exploration order of these global exploration units in the exploration, that is, obtain the global exploration logical path; merge the local exploration logical path and the global exploration logical path to obtain the overall logical path, and send it to the visualization module and the target point determination unit; The target point determination unit is used to determine the next target point of the unmanned vehicle according to the overall logical path, and send the determined target point to the path planning module and the visualization module.

3. The autonomous exploration system for a driverless vehicle in an unknown environment according to claim 2, characterized in that, In the viewpoint generation and screening unit, the method for screening preferred viewpoints is as follows: Update the parameters of all candidate viewpoints. The parameters include the observable obstacle area Observable_n within the exploration window, the observable boundary size Frontier_n between the passable area and the unknown area within the exploration window, and the cost Inertial_n for the unmanned vehicle to reach the candidate viewpoint; Perform the first round of screening: If the Observable_n parameter of the candidate viewpoint is less than the area threshold, or the Frontier_n parameter is less than the boundary size threshold, the current candidate viewpoint is eliminated; Perform the second round of screening: For the remaining candidate viewpoints, calculate the weighted sum of the three parameters and sort them. Select a set number of candidate viewpoints with the highest scores as the preferred viewpoints according to the scores from high to low.

4. The autonomous exploration system for driverless vehicles in an unknown environment according to claim 3, characterized in that, The method for determining the cost Inertial_n for the unmanned vehicle to reach the candidate viewpoint is as follows: Inertial_n = k1·orientation_from_robot + k2·distance_from_robot + k3·orientation_from_path where orientation_from_robot is the angle between the azimuth of the candidate viewpoint and the heading angle of the unmanned vehicle; distance_from_robot is the absolute distance from the candidate viewpoint to the unmanned vehicle; orientation_from_path is the angle between the azimuth of the candidate viewpoint and the azimuth of the unmanned vehicle path, where the path azimuth is represented by the average value of the heading angles of the unmanned vehicle in the past period of time; k1, k2, and k3 are weight values.

5. The autonomous exploration system for an unmanned vehicle in an unknown environment according to claim 2, wherein The target point determination unit determines the next target point of the unmanned vehicle according to the overall logical path as follows: Determine the head viewpoint and the tail viewpoint of the overall logical path, calculate the weighted sum of orientation_from_robot and distance_from_robot for the head viewpoint and the tail viewpoint respectively, and take the larger one as the next target point of the unmanned vehicle; where orientation_from_robot is the angle between the azimuth of the candidate viewpoint and the heading angle of the unmanned vehicle; distance_from_robot is the absolute distance from the candidate viewpoint to the unmanned vehicle.

6. The autonomous exploration system for an unmanned vehicle in an unknown environment according to claim 2, wherein In the viewpoint generation and screening unit, the local exploration unit state is determined as follows: count the states of the point cloud within a local exploration unit, including unknown, occupied, and unoccupied, and divide the local exploration unit into three states: unknown, occupied, and unoccupied according to the proportion of the point cloud states. The global exploration unit state is determined as follows: count the states of the local exploration units included in the global exploration unit; if the number of local exploration units in the occupied state or the unoccupied state is lower than the set exploration threshold, the global exploration unit is determined to be in the unexplored state; if the number of local exploration units in the known state is higher than the exploration threshold, the global exploration unit is determined to be in the exploring state; if the number of local exploration units in the known state is higher than the set completion threshold, the global exploration unit is determined to be in the explored state.

7. The autonomous exploration system for driverless vehicles in an unknown environment according to any one of claims 1-6, characterized in that, The exploration window consists of 3×3 global exploration units.

8. The autonomous exploration system for driverless vehicles in an unknown environment according to claim 1, wherein In the control module, the mapping and localization module uses the Aloam algorithm for 3D mapping and the Gmapping algorithm for 2D mapping; the path planning module uses the Teb-local-planner local path algorithm to dynamically plan the execution path of the unmanned vehicle.

9. The autonomous exploration system for a driverless vehicle in an unknown environment according to claim 1, characterized in that, The lidar uses a 360-degree lidar.

10. The autonomous exploration system for a driverless vehicle in an unknown environment according to claim 1, wherein The communication module uses serial communication to transmit the speed command of the unmanned vehicle chassis in the form of 7 hexadecimal data; the speed command includes the check information of the first 2 bits and the last 2 bits, and the middle 3 bits of data represent the front and rear, left and right linear velocities, and the rotational angular velocity of the unmanned vehicle respectively.

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