A multifunctional autonomous navigation tree climbing robot with an environmental monitoring module

Abstract

The present application provides a kind of multifunctional autonomous navigation tree climbing robot with environmental monitoring module, comprising: annular main frame, including first half ring frame and second half ring frame, the two ends of first half ring frame and second half ring frame are realized controlled closure by locking screw;Self-adapting clamping drive module is set to the inside of annular main frame, including guide wheel assembly, drive wheel assembly, guide wheel assembly is equipped with compression spring and telescopic damper, drive wheel assembly is configured with pre-tightening force adjusting actuator and rotator;Work platform is located at the top of annular main frame, and the work platform is provided with standardized physical and electrical interface;Environment perception and navigation module is set in work platform, and the environment perception and navigation module is integrated with three-dimensional laser radar, nine-axis inertial measurement unit and multispectral vision sensor;Control system includes processor, solid-state lithium battery pack and wireless communication module, and is all set in work platform.

Description

Technical Field

[0001] This invention belongs to the field of robotics technology, and in particular relates to a multifunctional autonomous navigation tree-climbing robot with an environmental monitoring module. Background Technology

[0002] In complex forest environments, traditional manual inspection methods face extremely high safety risks and labor intensity due to limitations imposed by tree height, density, and terrain undulations. Furthermore, they suffer from inherent limitations in the timeliness, continuity, and vertical spatial coverage of data collection. Therefore, tree-climbing robots, capable of overcoming gravity and maneuvering vertically along tree trunks, have emerged and are gradually becoming key vehicles for performing tasks such as high-altitude environmental detection, pest and disease monitoring, and tree pruning.

[0003] Existing tree-climbing robots are generally classified into various configurations based on their kinematic characteristics, including wheeled, tracked, peristaltic, and biomimetic gripping types. Early designs focused on matching the gripping force and driving torque of the mechanical mechanism, maintaining the robot's displacement stability on the tree trunk through high-strength mechanical engagement or frictional actuation. However, as application scenarios shift towards complex outdoor environments and task requirements evolve towards multi-functionality and long-term monitoring, existing technologies still have some limitations when facing real-world working conditions.

[0004] (1) As application scenarios shift from controlled laboratory environments to natural forest conditions with high uncertainty, and task requirements evolve from simple displacement climbing to complex multifunctional operations, existing tree-climbing robot systems often focus on building basic motion capabilities, neglecting the deep coupling relationship between "motion-perception-protection" in complex unstructured environments. That is, the trunk of a tree in nature is not a regular physical entity; its surface is covered with irregular bark protrusions, broken knots, and randomly distributed horizontal branches. Traditional driving strategies are mostly based on preset trajectories or blind torque control. When facing such sudden obstacles, due to the lack of sensitive environmental perception and feedback mechanisms, it is very easy for the robot body or its high-value sensors to have violent rigid collisions with the branches.

[0005] (2) Existing robot platforms have significant shortcomings in terms of functional integration and operational adaptability. Currently, most robots adopt a dedicated model, lacking a general-purpose modular platform that can be compatible with environmental detection, plant protection pruning, and multi-dimensional monitoring tasks. For example, when a robot is equipped with operational modules with high dynamic load characteristics, such as pruning shears, the periodic vibrations or instantaneous reaction forces generated during operation are often directly fed back to the climbing body. In the absence of effective environmental constraint perception, such disturbances can easily cause the robot to deviate from the predetermined path, or even cause mechanical interference due to loss of posture control in narrow forest gaps. Summary of the Invention

[0006] The purpose of this invention is to provide a multifunctional autonomous navigation tree-climbing robot with an environmental monitoring module. Through ring structure design and multi-source sensor fusion, it achieves good results in terms of vertical space mobility, environmental adaptability, and versatility of operation functions.

[0007] To achieve the above objectives, the present invention provides a multifunctional autonomous navigation tree-climbing robot with an environmental monitoring module, comprising:

[0008] The ring-shaped main frame, which serves as the load-bearing and support base for the entire machine, adopts an openable and closable split locking structure, which includes a first semi-circular ring frame and a second semi-circular ring frame. The two ends of the first semi-circular ring frame and the second semi-circular ring frame are closed in a controlled manner by locking screws to form an annular cavity surrounding the tree trunk to be operated.

[0009] An adaptive clamping drive module is located inside the annular main frame and includes a guide wheel assembly and a drive wheel assembly. The guide wheel assembly is equipped with a compression spring and a retractable damper, and the drive wheel assembly is equipped with a preload adjustment actuator and a rotator for dynamically adjusting the normal pressure of the drive unit on the tree trunk surface.

[0010] The work platform is located at the top of the ring-shaped main frame. The work platform is equipped with standardized physical and electrical interfaces, including a dovetail groove type quick physical locking mechanism, an electrical connector, and a position sensor for identifying the load. The work platform is used to mount and drive a robotic arm or an environmental monitoring sampling module.

[0011] An environmental perception and navigation module, installed on the work platform, integrates a 3D LiDAR, a nine-axis inertial measurement unit, and a multispectral vision sensor. This module is used to construct a 3D point cloud map of the tree trunk and its surrounding environment and to perform path planning.

[0012] The control system, including the processor, solid-state lithium battery pack, and wireless communication module, is all located within the operating platform.

[0013] The aforementioned multifunctional autonomous navigation tree-climbing robot with an environmental monitoring module includes a drive unit for the drive wheel assembly comprising a high-torque brushless DC geared motor and a planetary gear reducer connected thereto. The output end of the planetary gear reducer drives the tires via a pulley.

[0014] The aforementioned multifunctional autonomous navigation tree-climbing robot with an environmental monitoring module has guide wheels and drive wheels made of nitrile rubber tires, with staggered V-shaped anti-slip protrusions on the surface.

[0015] The aforementioned multifunctional autonomous navigation tree-climbing robot with an environmental monitoring module includes a robotic arm mounted on the work platform, which is equipped with a camera and a saw blade.

[0016] The aforementioned multifunctional autonomous navigation tree-climbing robot with an environmental monitoring module includes a three-dimensional lidar and a multispectral vision sensor mounted on the upper part of the work platform, and a nine-axis inertial measurement unit mounted inside the work platform.

[0017] The aforementioned multifunctional autonomous navigation tree-climbing robot with an environmental monitoring module includes a control system that operates a gravity-compensated clamping force closed-loop adjustment program. Based on the robot's tilt angle θ relative to the gravity vector fed back by the nine-axis inertial measurement unit, the current feedback value of the drive wheel drive unit, and a preset dynamic friction coefficient μ, the system calculates the currently required normal force F. clamp And drive the preload adjustment actuator to perform a compensation action; the calculation process of its clamping force is as follows:

[0018]

[0019] Among them, K s The preset safety factor ranges from 1.2 to 1.5, where m is the total mass of the robot and its payload, g is the gravitational acceleration, and n is the number of drive units.

[0020] The aforementioned multifunctional autonomous navigation tree-climbing robot with an environmental monitoring module, wherein the environmental perception and navigation module executes an obstacle avoidance strategy based on point cloud segmentation and local cost maps:

[0021] The raw point cloud data acquired by the three-dimensional lidar is processed by the processor to remove noise points through statistical filtering, and then segmented into tree trunk main axis point cloud clusters and obstacle point cloud clusters through Euclidean clustering algorithm.

[0022] The processor projects the obstacle point cloud clusters onto a cylindrical coordinate system with the central axis of the tree trunk as the reference, constructs a dynamic local cost map, and calls the improved D*Lite algorithm to plan the climbing trajectory.

[0023] When the 3D LiDAR detects that the distance to an obstacle is lower than a preset safety threshold, the autonomous navigation system triggers avoidance logic: by controlling the drive unit to generate differential speed, the robot is driven to rotate circumferentially around the central axis of the tree trunk to bypass the obstacle area.

[0024] The aforementioned multifunctional autonomous navigation tree-climbing robot with an environmental monitoring module, wherein the control system controls the external contact force fed back by the drive unit. An impedance model of the simulated mass-spring-damped system is established, and its governing equations are as follows:

[0025]

[0026] in, These are the mass, damping, and stiffness matrices of the preset target impedance model. and These are the actual radial displacement and the desired displacement of the drive unit, respectively; by dynamically adjusting the damping and stiffness matrices, the mechanical impact generated when the robot crosses rough bark protrusions is flexibly buffered.

[0027] Compared with the prior art, the present invention has the following advantages:

[0028] (1) The present invention adopts a combination of guide wheel assembly and drive wheel assembly, which can fit the tree more accurately and prevent the robot from slipping and falling off; at the same time, by using the real-time calculated clamping force algorithm, it can dynamically provide enough traction force to overcome gravity and external disturbances while ensuring that the bark is not damaged due to overload.

[0029] (2) This invention integrates multi-source sensor data. By coupling lidar, inertial measurement unit and multispectral vision, it can improve the robot's perception ability in complex working conditions and provide multi-dimensional data support for autonomous navigation. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the overall structure of the present invention;

[0031] Figure 2 This is the present invention. Figure 1 Enlarged view of part A in the image;

[0032] Figure 3 This is a schematic diagram of the drive wheel assembly of the present invention;

[0033] Figure 4 This is a schematic diagram of the guide wheel assembly of the present invention;

[0034] Figure 5 This is a schematic diagram of the state of the three-dimensional lidar and multispectral vision sensor of the present invention;

[0035] Figure 6 This is a flowchart of the tree pruning process using the robotic arm of this invention;

[0036] Figure 7 This is a framework diagram illustrating the working principle of the present invention;

[0037] In the picture:

[0038] 1. First semicircular ring frame; 2. Drive wheel assembly; 21. Steering gear; 22. Second drive wheel; 23. Fixing plate; 24. Pulley; 25. Drive wheel; 3. Working platform; 4. Robotic arm; 41. Camera; 42. Saw blade; 5. Guide wheel assembly; 51. Telescopic damper; 52. Spring; 53. Guide wheel; 6. Second semicircular ring frame; 7. Environmental perception and navigation module; 71. 3D LiDAR; 72. Multispectral vision sensor. Implementation

[0039] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the specific embodiments according to the present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments.

[0040] Please see Figure 1-7 This invention provides a multifunctional autonomous navigation tree-climbing robot with an environmental monitoring module. Two sets of working platforms are mounted on the sides of the annular main frame. Two sets (six) of guide wheel assemblies 5 are located at the upper and lower ends of the annular main frame, and two sets of drive wheel assemblies 2 are symmetrically arranged in the middle. The two sets of working platforms 3 are respectively equipped with a robotic arm 4 and an environmental perception and navigation module 7. The annular main frame is made of carbon fiber reinforced epoxy resin composite material, with embedded reinforcing ribs. This design ensures structural strength while reducing the robot's weight and energy consumption during climbing.

[0041] The ring-shaped main frame, which serves as the load-bearing and support base for the entire machine, adopts an openable and closable split locking structure. It includes a first semi-circular ring frame 1 and a second semi-circular ring frame 6. The two ends of the first semi-circular ring frame 1 and the second semi-circular ring frame 6 are closed in a controlled manner by locking screws to form an annular cavity surrounding the tree trunk to be operated.

[0042] An adaptive clamping drive module is located inside the annular main frame and includes a guide wheel assembly 5 and a drive wheel assembly 2. The guide wheel assembly 5 is equipped with a compression spring 52 and a retractable damper 51. The drive wheel assembly 2 is equipped with a preload adjustment actuator and a rotator for dynamically adjusting the positive pressure of the drive unit on the tree trunk surface.

[0043] Furthermore, the drive unit of the drive wheel assembly includes a high-torque brushless DC geared motor and a planetary gear reducer connected thereto, the output end of which drives the tire through a pulley 25.

[0044] Specifically, the preload adjustment actuator is a high-precision electric push rod, which dynamically adjusts the positive pressure between the drive unit and the trunk surface according to the change in the trunk diameter. An incremental photoelectric encoder is also installed on the drive shaft of the drive wheel 25 to calculate the robot's travel distance and instantaneous speed in real time.

[0045] Specifically, the rotator drives the steering gear 21 to rotate under the action of the steering drive wheel 22 (motor driven). Since the entire drive wheel assembly 2 is fixed to the working platform 3 using the fixing plate 23, the steering gear 21 will drive the drive wheel 25 to rotate around the tree trunk.

[0046] Furthermore, both the guide wheel 53 and the drive wheel 25 are nitrile rubber tires, and their surfaces are provided with staggered V-shaped anti-slip protrusions. The hardness of the protrusions is greater than 55A on the Shore A scale, which enhances the grip on wet or moss-covered tree trunk surfaces without damaging the bark.

[0047] The work platform 3 is located at the top of the ring-shaped main frame. The work platform 3 is equipped with standardized physical and electrical interfaces, including a dovetail groove type quick physical locking mechanism and an electrical connector. The work platform 2 is also used to mount and drive a robotic arm or an environmental monitoring sampling module.

[0048] Furthermore, the work platform 2 is equipped with a robotic arm 4, which includes a camera 41 and a saw blade 42. When performing tree pruning tasks, the robotic arm 4 possesses three degrees of freedom of motion. Through inverse kinematics solutions in joint space, the cutting angle of the circular saw blade 42 relative to the branch is precisely controlled, guiding the robotic arm 4 to correct its path and ensure the smoothness of the pruning cut. In addition, during pruning operations, the pruning task is defined as the highest real-time priority. At this time, the environmental perception and navigation module 7 automatically enters a hovering steady-state holding mode, increasing the clamping preload to counteract the reaction force generated when sawing the branch.

[0049] The environmental perception and navigation module 7 is set on the work platform 3. The environmental perception and navigation module 7 integrates a three-dimensional lidar 71, a nine-axis inertial measurement unit and a multispectral vision sensor 72, which is used to construct a three-dimensional point cloud map of the tree trunk and the surrounding environment and perform path planning.

[0050] Furthermore, the 3D lidar 71 and multispectral vision sensor 72 are mounted on the upper part of the work platform 3, and the nine-axis inertial measurement unit is mounted inside the work platform 3. The multispectral vision sensor 72 includes an infrared thermal imaging component and a high-resolution visible light camera. The infrared thermal imaging component can be used to monitor the temperature difference inside the tree trunk and identify insect-infested cavities inside the tree through abnormal temperature differences. The visible light camera, combined with deep learning algorithms, can identify lesions or fungal growth on the surface of the tree trunk and mark the identification results on the corresponding spatial coordinates of the 3D point cloud map.

[0051] Specifically, the environment perception and navigation module 7 executes an obstacle avoidance strategy based on point cloud segmentation and local cost map:

[0052] The raw point cloud data collected by the three-dimensional lidar 71 is processed by the processor to remove noise points through statistical filtering, and then segmented into tree trunk main axis point cloud clusters and obstacle point cloud clusters through Euclidean clustering algorithm.

[0053] The processor projects the obstacle point cloud clusters onto a cylindrical coordinate system with the central axis of the tree trunk as the reference, constructs a dynamic local cost map, and calls the improved D*Lite algorithm to plan the climbing trajectory.

[0054] When the 3D LiDAR 71 detects that the distance to an obstacle is lower than a preset safety threshold, the navigation system triggers avoidance logic: by controlling the drive unit to generate differential speed, the robot is driven to rotate circumferentially around the central axis of the tree trunk to bypass the obstacle area.

[0055] The control system, including the processor, solid-state lithium battery pack, and wireless communication module, is all housed within the work platform. The wireless communication module employs a dual-mode communication architecture of LoRa and 4G / 5G. In areas with weak signal coverage, such as deep forests, LoRa technology is used to achieve low-speed data transmission and remote control command issuance. In areas with mobile network coverage, 4G / 5G networks are used to achieve real-time transmission of high-definition work video.

[0056] Furthermore, the control system operates a gravity-compensated clamping force closed-loop adjustment program. Based on the tilt angle θ of the robot relative to the gravity vector fed back by the nine-axis inertial measurement unit, the current feedback value of the drive wheel drive unit, and the preset dynamic friction coefficient μ, it calculates the currently required normal force F. clamp And drive the preload adjustment actuator to perform a compensation action; the calculation process of its clamping force is as follows:

[0057]

[0058] Among them, K s The preset safety factor ranges from 1.2 to 1.5, where m is the total mass of the robot and its payload, g is the gravitational acceleration, and n is the number of drive units.

[0059] Furthermore, the control system adjusts the external contact force fed back by the drive unit. An impedance model of the simulated mass-spring-damped system is established, and its governing equations are as follows:

[0060]

[0061] in, These are the mass, damping, and stiffness matrices of the preset target impedance model. and These are the actual radial displacement and the desired displacement of the drive unit, respectively; by dynamically adjusting the damping and stiffness matrices, the mechanical impact generated when the robot crosses rough bark protrusions is flexibly buffered.

[0062] In actual operation, during the task initiation phase, the robot is installed at the base of the target tree trunk via a locking structure. The control system activates the adaptive clamping drive module, establishing an initial preload based on the initial trunk diameter. During the ascent and climbing process, the environmental perception and navigation module 7 scans the space in front and around the robot in real time, constructing a travel route. When encountering tree branches or obstacles, the environmental perception and navigation module 7 plans an avoidance path based on data from the 3D LiDAR 71, controlling the drive wheel assembly 2 to achieve the robot's spiral ascent or circumferential obstacle avoidance. After reaching the predetermined working height or monitoring area, the pre-set accessories on the work platform 3 can perform pruning, sampling, or monitoring tasks according to preset instructions, such as using the saw blade 42 of the robotic arm 4 to cut branches. All collected environmental data, tree physiological data, and robot's own status data are uploaded to the ground monitoring terminal via a wireless communication module.

[0063] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A multifunctional autonomous navigation tree-climbing robot with an environmental monitoring module, characterized in that, include: The ring-shaped main frame, which serves as the load-bearing and support base for the entire machine, adopts an openable and closable split locking structure, which includes a first semi-circular ring frame and a second semi-circular ring frame. The two ends of the first semi-circular ring frame and the second semi-circular ring frame are closed in a controlled manner by locking screws to form an annular cavity surrounding the tree trunk to be operated. An adaptive clamping drive module is located inside the annular main frame and includes a guide wheel assembly and a drive wheel assembly. The guide wheel assembly is equipped with a compression spring and a retractable damper, and the drive wheel assembly is equipped with a preload adjustment actuator and a rotator for dynamically adjusting the normal pressure of the drive unit on the tree trunk surface. The work platform is located at the top of the ring-shaped main frame. The work platform is equipped with standardized physical and electrical interfaces, including a dovetail groove type quick physical locking mechanism, an electrical connector, and a position sensor for identifying the load. The work platform is used to mount and drive a robotic arm or an environmental monitoring sampling module. An environmental perception and navigation module, installed on the work platform, integrates a 3D LiDAR, a nine-axis inertial measurement unit, and a multispectral vision sensor. This module is used to construct a 3D point cloud map of the tree trunk and its surrounding environment and to perform path planning. The control system, including the processor, solid-state lithium battery pack, and wireless communication module, is all located within the operating platform.

2. The multifunctional autonomous navigation tree-climbing robot with an environmental monitoring module according to claim 1, characterized in that, The drive unit of the drive wheel assembly includes a high-torque brushless DC geared motor and a planetary gear reducer connected thereto. The output end of the planetary gear reducer drives the tire through a pulley.

3. The multifunctional autonomous navigation tree-climbing robot with an environmental monitoring module according to claim 1, characterized in that, Both the guide wheel and the drive wheel are made of nitrile rubber tires, and the surface is provided with staggered V-shaped anti-slip protrusions.

4. A multifunctional autonomous navigation tree-climbing robot with an environmental monitoring module according to claim 1, characterized in that, The work platform is equipped with a robotic arm, which is equipped with a camera and a saw blade.

5. A multifunctional autonomous navigation tree-climbing robot with an environmental monitoring module according to claim 1, characterized in that, The three-dimensional lidar and multispectral vision sensor are located at the top of the work platform, while the nine-axis inertial measurement unit is located inside the work platform.

6. A multifunctional autonomous navigation tree-climbing robot with an environmental monitoring module according to claim 1, characterized in that, The control system operates a gravity-compensated closed-loop adjustment program for clamping force. Based on the tilt angle θ of the robot relative to the gravity vector fed back by the nine-axis inertial measurement unit, the current feedback value of the drive wheel drive unit, and the preset dynamic friction coefficient μ, it calculates the required normal force F. clamp And drive the preload adjustment actuator to perform a compensation action; the calculation process of its clamping force is as follows: Among them, K s The preset safety factor ranges from 1.2 to 1.5, where m is the total mass of the robot and its payload, g is the gravitational acceleration, and n is the number of drive units.

7. A multifunctional autonomous navigation tree-climbing robot with an environmental monitoring module according to claim 1, characterized in that, The environmental perception and navigation module executes an obstacle avoidance strategy based on point cloud segmentation and local cost map: The raw point cloud data acquired by the three-dimensional lidar is processed by the processor to remove noise points through statistical filtering, and then segmented into tree trunk main axis point cloud clusters and obstacle point cloud clusters through Euclidean clustering algorithm. The processor projects the obstacle point cloud clusters onto a cylindrical coordinate system with the central axis of the tree trunk as the reference, constructs a dynamic local cost map, and calls the improved D*Lite algorithm to plan the climbing trajectory. When the 3D LiDAR detects that the distance to an obstacle is lower than a preset safety threshold, the autonomous navigation system triggers avoidance logic: by controlling the drive unit to generate differential speed, the robot is driven to rotate circumferentially around the central axis of the tree trunk to bypass the obstacle area.

8. A multifunctional autonomous navigation tree-climbing robot with an environmental monitoring module according to claim 1, characterized in that, The control system is based on the external contact force fed back by the drive unit. An impedance model of the simulated mass-spring-damped system is established, and its governing equations are as follows: in, These are the mass, damping, and stiffness matrices of the preset target impedance model. and These are the actual radial displacement and the desired displacement of the drive unit, respectively; by dynamically adjusting the damping and stiffness matrices, the mechanical impact generated when the robot crosses rough bark protrusions is flexibly buffered.

Images