A mobile lifting robot and a hierarchical intelligent planning method thereof

Abstract

This disclosure relates to a mobile lifting robot and its hierarchical intelligent planning method. The robot includes: a chassis for moving the robot and serving as a platform for its support; a lifting mechanism for adjusting the robot's working height, mounted on the chassis; a lidar for scanning the construction site, mounted on the lifting mechanism; a robotic arm for performing binding operations, mounted on the lifting mechanism; and a 3D camera for scanning binding points and guiding the robotic arm to perform binding operations at the binding points. The method includes: obtaining a set of obstacle coordinates and a set of task points from a pre-constructed high-precision 3D environment map; assigning position coordinates to each task point in the task point set and classifying all task points into types; obtaining the optimal task point access sequence based on the task point set and generating a globally optimal movement path; and performing access movement based on the globally optimal movement path, executing a preset binding operation strategy.

Description

Technical Field

[0001] This disclosure relates to the field of rebar tying technology, specifically to a mobile lifting robot and its layered intelligent planning method. Background Technology

[0002] Rebar tying is a crucial part of construction engineering. Traditional manual tying methods are often inefficient and labor-intensive, especially when working at heights or on large vertical structures, posing significant safety risks. Therefore, in recent years, rebar tying robots have emerged to address the shortcomings of manual methods. However, whether it's the foreign Tybot (gantry crane type), T-iROBO (mesh-walking type), or the domestic China Construction Eighth Engineering Bureau RBBD-Bot2.0, existing rebar tying robots all suffer from the following fatal flaws: (1) Limited functionality: It can only operate on flat, horizontal steel mesh. It is often powerless against vertical and inclined steel cages (such as columns and walls) that are common on construction sites, especially vertical steel cages that are several meters high.

[0003] (2) Lack of global perspective: lack of path planning ability for multiple task points to be tied, such as being unable to independently decide on a construction site "first tie the pillar at point A, then go to the floor slab at point B".

[0004] (3) No autonomous obstacle avoidance: It relies on a flat working surface and cannot autonomously identify and avoid holes or temporary obstacles during walking, making it very easy to fall or collide.

[0005] (4) When facing a complex construction site with multiple, dispersed, and different types of steel cages (such as horizontal, vertical, and inclined), there is often a problem of dual path planning. There is a lack of overall coordination between the global movement path and the local binding operation, resulting in a large number of ineffective or unnecessary path movements.

[0006] It is evident that existing rebar tying robots often have extremely poor scene adaptability, are unable to handle various types and heights of tying tasks, and are also unable to achieve safe autonomous navigation on complex construction site surfaces.

[0007] At present, there is an urgent need for new technical solutions that can solve the above-mentioned technical problems. Summary of the Invention

[0008] To address the problems existing in the prior art, this disclosure proposes a mobile lifting robot and its hierarchical intelligent planning method to solve at least one of the aforementioned technical problems. The technical solution adopted in this disclosure is as follows: In a first aspect, this disclosure provides a mobile lifting robot, comprising: A chassis for moving the robot and serving as a platform for carrying the robot; A lifting mechanism, used to adjust the working height of the robot, is mounted on the chassis; A lidar, used to scan the construction site, is installed on the lifting mechanism; A robotic arm, used to perform binding operations, is mounted on the lifting mechanism; A 3D camera is used to scan the points to be bound, guiding the robotic arm to perform the binding operation.

[0009] Preferably, the chassis is a tracked chassis.

[0010] Preferably, the robotic arm includes: A lashing actuator is used to perform specific lashing operations.

[0011] A second aspect of this disclosure provides a hierarchical intelligent planning method for the aforementioned mobile lifting robot, the method comprising: Obtain obstacle coordinates and task point sets from a pre-constructed high-precision 3D environment map. Assign position coordinates to each task point in the task point set and classify all task points into types such as "horizontal", "vertical" or "tilted". Each task point is a steel cage to be tied. The optimal task point access sequence is obtained based on the set of task points, and a globally optimal movement path that can avoid known obstacles, holes, etc. is generated based on the optimal task point access sequence. The robot moves based on the globally optimal movement path and executes the preset binding operation strategy.

[0012] Preferably, before obtaining the obstacle coordinate set and task point set from the preset high-precision 3D environment map, the method further includes: After the robot is activated, it scans the construction site to build a high-precision 3D environmental map containing obstacle information.

[0013] Preferably, the construction site is scanned using a lidar mounted on the robot, and a high-precision three-dimensional environment map containing information such as obstacles and voids is constructed using a SLAM algorithm.

[0014] Preferably, the order of visiting all task points can be abstracted into a Traveling Salesman Problem (TSP), and a heuristic algorithm (such as a genetic algorithm) can be used to solve for the optimal task point visiting sequence that minimizes the total movement cost.

[0015] Preferably, in the process of generating a globally optimal movement path that can avoid known obstacles, holes, etc. based on the optimal task point access sequence, each time from the current task point to the next task point, the A* algorithm is used to generate a locally optimal movement path between the current task point and the next task point under the constraints of the high-precision three-dimensional environment map. All of the aforementioned globally optimal movement paths together constitute the globally optimal movement path.

[0016] Preferably, when the robot moves based on the globally optimal movement path, it uses the Dynamic Window Method (DWA) for real-time detection and obstacle avoidance.

[0017] Preferably, it specifically includes: The robot moves based on the global optimal movement path, visiting each task point along the path one by one, and executing a preset binding operation strategy according to the type of the current task point after reaching each task point.

[0018] The beneficial effects of this disclosure are as follows: This disclosure provides a mobile lifting robot and its hierarchical intelligent planning method, which can flexibly operate on mobile lifting binding robots, etc. It can flexibly address the pain points of binding operations in construction sites where rebar cages are scattered, diverse in type (such as horizontal, vertical, and inclined), and in complex environments, breaking through the limitations of the single-dimensional control of traditional existing rebar binding robots. This disclosure innovatively decomposes the rebar cage binding task into two levels: global path planning and local operation planning. Global path planning is responsible for traversing and visiting all rebar cages to be bound, while local operation planning is responsible for executing specific preset binding operations according to the type of rebar cage to be bound. This achieves overall coordination between global path planning and local binding operations, avoiding a large amount of or ineffective movement and improving the efficiency of movement and binding operations.

[0019] Global path planning involves obtaining the optimal task point access sequence based on the task point set, and then generating a globally optimal movement path that avoids known obstacles and holes based on this optimal task point access sequence, enabling the robot to move based on the globally optimal movement path. Local task planning involves executing a preset binding operation strategy according to the type of each task point when the robot moves to each task point. In particular, a binding lifting and lowering cycle strategy is executed for "vertical" type task points to complete the binding operations for "horizontal", "vertical", and "tilted" type task points.

[0020] By employing a binding and lifting cycle strategy, the robot is no longer limited to planar rebar cages. It can autonomously understand and respond to the "height" of vertical rebar cages. By performing a binding cycle on each vertical rebar cage until the binding operation of each vertical rebar cage is completed, the robot can proactively incorporate "lifting" as a planable action into the overall operation strategy. This solves the major pain point in the industry of being unable to automatically bind vertical rebar cages with large heights.

[0021] Through a tilting collaborative operation strategy, robots can move vertically, horizontally, or tilted, unlike simple single-dimensional movements in the vertical or horizontal plane, enabling three-dimensional binding operations. When designing the tilting collaborative operation strategy, the Z-axis movement of the lifting mechanism, the X / Y-axis translation of the chassis, and the 6-DoF posture compensation of the robotic arm can be decoupled and jointly controlled. This overcomes the challenges of limited reachable space and difficulty in adjusting the end effector pose when working with complex tilted components (such as stair reinforcement).

[0022] Global path planning deconstructs the rebar cage tying task from a macroscopic perspective, focusing on solving the problem of the access order of all rebar cages to be tied. By endowing the rebar tying robot with global perception and multi-task optimization capabilities, it can autonomously plan the most efficient walking path, visit all task points to be tied in sequence, and actively avoid obstacles such as holes during movement, ensuring the safety and efficiency of chassis movement.

[0023] Localized task planning deconstructs the rebar cage binding task from a microscopic, localized perspective, focusing on solving the binding operation problems of each individual rebar cage to be bound. Once the robot safely arrives at the current task point, this invention can autonomously plan the binding operation actions based on the specific type of the task point. Particularly for vertical rebar cages several meters high, this invention enables the robot to proactively incorporate "lifting" as a plannable action into its execution strategy. Through intelligent coordination of the lifting mechanism and the robotic arm, it completely solves the industry pain point that traditional equipment, including rebar binding robots, cannot adapt to changes in height.

[0024] This disclosure utilizes a two-tiered mechanism of "macro" and "micro" to avoid known obstacles and voids based on the globally optimal movement path. Furthermore, this disclosure organically combines complex site-level mobile navigation with component-level fine binding, realizing a highly autonomous and intelligent operation process for the rebar binding robot.

[0025] Compared with the prior art, this disclosure has the following advantages: (1) Extremely high versatility in various scenarios: It solves the problem that most existing robots can only tie planar steel cages. The "layered intelligent planning method and system" proposed in this invention is applicable to various construction scenarios such as horizontal steel cages and vertical steel cages, and perfectly matches the construction scenarios of power transmission and transformation projects.

[0026] (2) It truly realizes autonomous operation on the construction site: The robot can not only operate autonomously, but also walk safely on complex construction sites (including holes), which greatly improves construction efficiency and reduces dependence on people.

[0027] (3) Solving the dangers of working at height: Through the binding and lifting cycle strategy, the robot can replace the manual labor to complete the binding of vertical steel cages that are several meters high, completely solving the "danger" among the "dangerous, complicated, dirty and heavy". Attached Figure Description

[0028] The accompanying drawings, which form part of this application, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.

[0029] Figure 1 This is a perspective view of a mobile lifting robot as described in Embodiment 1 of this disclosure.

[0030] Figure 2 This is a side view of a mobile lifting robot according to Embodiment 2 of this disclosure.

[0031] Figure 3 This is a front view of a mobile lifting robot as described in Embodiment 1 of this disclosure.

[0032] Figure 4 Partial disassembly of a mobile lifting robot as described in Embodiment 1 of this disclosure. Figure 1 .

[0033] Figure 5 Partial disassembly of a mobile lifting robot as described in Embodiment 1 of this disclosure. Figure 2 .

[0034] Figure 6 This is a flowchart of the hierarchical intelligent planning method for the mobile lifting robot described in Embodiment 2 of this disclosure. Detailed Implementation

[0035] The present disclosure will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the present application can be combined with each other.

[0036] The following detailed descriptions are exemplary and intended to provide further detailed explanation of this disclosure. Unless otherwise specified, all technical terms used in this disclosure have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this disclosure.

[0037] Example 1: like Figure 1-3 As shown, this embodiment provides a mobile lifting robot, including: The chassis 100 is used to move the robot and serve as a carrying platform for the robot. A lifting mechanism 200, used to adjust the working height of the robot, is mounted on the chassis 100; The lidar 300, used to scan the construction site, is installed on the lifting mechanism 200; A robotic arm 400, used to perform binding operations, is mounted on the lifting mechanism 200; A 3D camera 500 is used to scan the points to be bound and guide the robotic arm 400 to perform the binding operation at the points to be bound.

[0038] Preferably, the chassis 100 is a tracked chassis.

[0039] Furthermore, such as Figure 4 As shown, the chassis 100 includes a chassis body 101, and tracks 102 are provided on both sides of the chassis body 101.

[0040] Furthermore, such as Figure 4 As shown, the track 102 rotates through the cooperating drive wheel 103 and driven wheel 104, thereby driving the robot to move.

[0041] Furthermore, such as Figure 4 As shown, the lifting mechanism 200 includes: The lifting platform 201 is used to support the lidar 300 and the robotic arm 400, and to adjust the height of the lidar 300 and the robotic arm 400 by lifting. The scissor boom 202 is used to support the lifting platform 201 and to lift the lifting platform 201. The scissor boom 202 is mounted on the chassis body 101.

[0042] Furthermore, the lidar 300 is mounted on the lifting mechanism 200 via a lidar bracket 301.

[0043] Furthermore, such as Figure 5 As shown, the robotic arm 400 includes: The tying actuator 401 is used to perform specific tying operations.

[0044] Furthermore, the tying actuator 401 is preferably an end tying gun.

[0045] Furthermore, such as Figure 5 As shown, the binding actuator 401 is preferably mounted on the working end of the robotic arm 400 via a flange 402.

[0046] Example 2: like Figure 6 As shown, this embodiment provides a hierarchical intelligent planning method for the mobile lifting robot described in Embodiment 1, the method comprising: S100: After the robot is started, it scans the construction site to build a high-precision three-dimensional environmental map containing obstacle information. S200: Obtain the obstacle coordinate set and task point set from the pre-built high-precision 3D environment map, assign position coordinates to each task point in the task point set, and classify all task points into types such as "horizontal", "vertical" or "tilted"; each task point is a steel cage to be tied. S300: Obtain the optimal task point access sequence based on the task point set, and generate a globally optimal movement path that can avoid known obstacles, holes, etc. based on the optimal task point access sequence. S400: The robot moves based on the globally optimal movement path and executes the preset binding operation strategy.

[0047] Furthermore, in step S100, the LiDAR 300 mounted on the robot is used to scan the construction site, and a high-precision three-dimensional environment map containing information such as obstacles and cavities is constructed by combining the SLAM algorithm.

[0048] Furthermore, in S300, the access order of all task points is abstracted into the Traveling Salesman Problem (TSP), and a heuristic algorithm (such as a genetic algorithm) is used to solve for the optimal task point access sequence that minimizes the total movement cost.

[0049] Furthermore, in step S300, during the process of generating a globally optimal movement path that can avoid known obstacles, holes, etc. based on the optimal task point access sequence, each time moving from the current task point to the next task point, the A* algorithm is used to generate a locally optimal movement path between the current task point and the next task point under the constraints of the high-precision three-dimensional environment map.

[0050] It is understood that all the aforementioned globally optimal movement paths collectively constitute the globally optimal movement path. The globally optimal movement path is relative to the determined starting and ending task points. The starting and ending task points are determined according to actual needs; they can be separated by several task points, or they can be directly adjacent. For example, when the starting and ending task points are adjacent, the globally optimal movement path can serve as the optimal path between them.

[0051] Furthermore, in S400, when the robot visits and moves based on the globally optimal movement path, it uses the Dynamic Window Method (DWA) for real-time detection and obstacle avoidance.

[0052] Furthermore, the robot's velocity space can be sampled, and velocity commands that maximize the utility function (considering target orientation, obstacle distance, and travel speed) can be calculated and executed, thereby ensuring that the robot's chassis safely arrives at each task point.

[0053] Furthermore, the S400 specifically includes: The robot moves based on the global optimal movement path, visiting each task point along the path one by one, and executing a preset binding operation strategy according to the type of the current task point after reaching each task point.

[0054] Furthermore, the current task point refers to the task point that the robot is visiting in real time. Each time the robot visits a task point, that visited task point becomes the current task point.

[0055] Furthermore, the binding operation strategy is determined according to actual needs and can be implemented using existing technologies, or may specifically include: S410: If the type of the current task point is "horizontal" - that is, the current task point is a horizontal steel cage to be tied, the lifting mechanism 200 of the robot is controlled to stay at the basic working height of the current task point, and the 3D camera 500 scans the point to be tied at the current task point, guiding the robotic arm 400 to perform the tying operation at the point to be tied, and completing the tying of the current task point. S420: If the type of the current task point is "vertical" - that is, the current task point is a vertical steel cage to be tied, then control the robot to execute the preset tying lifting cycle strategy for the current task point to complete the tying of the current task point. S430: If the type of the current task point is "tilted" - that is, the current task point is a tilted steel cage to be tied, then control the robot to execute the preset tilted collaborative operation strategy for the current task point to complete the tying of the current task point.

[0056] Understandably, the binding operation strategy is based on the type of task point selected, controlling the robot's "lifting mechanism 200 + robotic arm 400" to work together. For example, when the robot safely arrives at a task point... After that, the mission point That is, as the current task point, based on the task point Type Call the corresponding preset job strategy library. If the task point If the type is "horizontal", then the robot's lifting mechanism 200 will remain at the task point. Base height, scanning task points The binding point, the completion point of the task. The binding. If the task point If the type is "vertical", then control the robot to move to the task point. Execute the preset binding and lifting cycle strategy to complete the task point. Binding. The binding-and-lift cycle strategy enables the robot to autonomously complete the binding operation of vertical rebar cages, and can handle ordinary vertical rebar cages as well as vertical rebar cages that are several meters high.

[0057] Furthermore, the binding lifting cycle strategy may specifically include: S421: Control the lifting mechanism 200 to move to the basic working height of the current task point, and initialize the current working height of the current task point through the basic working height; S422: The 3D camera 500 scans the binding point of the current task point at the current working height, and guides the robotic arm 400 to perform binding operation on the binding point to complete the binding of the current task point at the current working height. S423: If the lifting mechanism 200 has not reached the top of the steel cage at the current task point, control the lifting mechanism 200 to lift it up by a preset step length and update the current working height, and return to execute S422; otherwise, it means that the binding work of all working heights at the current task point has been completed - that is, the binding work of the current task point has been completed, and continue to S424. S424: Control the lifting mechanism 200 to return to the basic working height of the current task point.

[0058] Furthermore, the tilted collaborative operation strategy may specifically include: S431: Based on the LiDAR 300 and / or 3D camera 500, identify the tilt angle and spatial extension length of the current task point to extract the three-dimensional operation trajectory surface of the current task point; S432: Control the lifting mechanism 200 to move to the basic working height of the current task point, initialize the binding point of the current task point below the basic working height as the current binding point, and link the chassis 100 to make fine adjustments to the position of the robot to ensure that the robotic arm 400 is within the optimal working arm span range for binding operations. S433: Scan the current binding point of the current task point using LiDAR 300 and / or 3D camera 500, adjust the posture of robotic arm 400 to ensure that robotic arm 400 performs binding operation at an angle perpendicular to the inclined steel bar surface, and complete the binding operation of the current binding point. S434: If the current binding point is located at the end of the current task point, it means that the binding operation of the current task point has been completed, and continue to S435; otherwise, it means that the binding operation of the current task point has not been completed, so move along the three-dimensional operation trajectory surface of the current task point, simultaneously calculate and output the small displacement of the chassis 100 and the elevation change of the lifting mechanism 200 (joint interpolation), complete the update of the current binding point, and return to execute S433; S435: Reset the chassis 100 and lifting mechanism 200 to the safe navigation state.

[0059] In summary, the mobile lifting robot and its hierarchical intelligent planning method provided in Embodiments 1-2 of this disclosure can be flexibly applied to mobile lifting tying robots, etc., and can flexibly address the challenges of scattered and diverse types of rebar cages (such as horizontal, vertical, and inclined) and complex environments at construction sites, breaking through the limitations of the single-dimensional control of traditional rebar tying robots. This disclosure innovatively decomposes the rebar cage tying task into two levels: global path planning and local operation planning. Global path planning is responsible for traversing all rebar cages to be tied, while local operation planning is responsible for executing specific preset tying operations based on the type of rebar cage. This achieves overall coordination between global path planning and local tying operations, avoiding excessive or ineffective movement and improving the efficiency of movement and tying operations.

[0060] Global path planning involves obtaining the optimal task point access sequence based on the task point set, and then generating a globally optimal movement path that avoids known obstacles and holes based on this optimal task point access sequence, enabling the robot to move based on the globally optimal movement path. Local task planning involves executing a preset binding operation strategy according to the type of each task point when the robot moves to each task point. In particular, a binding lifting and lowering cycle strategy is executed for "vertical" type task points to complete the binding operations for "horizontal", "vertical", and "tilted" type task points.

[0061] By employing a binding and lifting cycle strategy, the robot is no longer limited to planar rebar cages. It can autonomously understand and respond to the "height" of vertical rebar cages. By performing a binding cycle on each vertical rebar cage until the binding operation of each vertical rebar cage is completed, the robot can proactively incorporate "lifting" as a planable action into the overall operation strategy. This solves the major pain point in the industry of being unable to automatically bind vertical rebar cages with large heights.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure and not to limit them. Although this disclosure has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of this disclosure. Any modifications or equivalent substitutions that do not depart from the spirit and scope of this disclosure should be covered within the protection scope of the claims of this disclosure.

Claims

1. A mobile lifting robot, characterized in that, include: A chassis (100) for moving the robot and serving as a platform for carrying the robot; A lifting mechanism (200), used to adjust the working height of the robot, is mounted on the chassis (100); A lidar (300) is used to scan the construction site and is installed on the lifting mechanism (200); A robotic arm (400) for performing binding operations is mounted on the lifting mechanism (200); A 3D camera (500) is used to scan the points to be bound and guide the robotic arm (400) to perform binding operations on the points to be bound.

2. A hierarchical intelligent planning method for the mobile lifting robot of claim 1, characterized by, The method includes: S200: Obtain the obstacle coordinate set and task point set from the pre-built high-precision 3D environment map, assign position coordinates to each task point in the task point set, and classify all task points into "horizontal", "vertical" or "sloping" types; each task point is a steel cage to be tied. S300: Obtain the optimal task point access sequence based on the task point set, and generate a globally optimal movement path that can avoid known obstacles and holes based on the optimal task point access sequence; S400: The robot moves based on the globally optimal movement path and executes the preset binding operation strategy.

3. The hierarchical intelligent planning method of claim 2, wherein, Before step S200: obtaining the obstacle coordinate set and task point set from a preset high-precision 3D environment map, the method further includes: S100: After the robot is started, it scans the construction site to build a high-precision three-dimensional environmental map containing obstacle information.

4. The hierarchical intelligent planning method of claim 3, wherein, In step S100, the construction site is scanned using a lidar (300) mounted on the robot, and a high-precision three-dimensional environment map containing information on obstacles and voids is constructed using a SLAM algorithm.

5. The hierarchical intelligent planning method of claim 2, wherein, In S300, the access order of all task points is abstracted into a traveling salesman problem, and a heuristic algorithm is used to solve for the optimal task point access sequence with the minimum total movement cost. In S300, during the process of generating a globally optimal movement path that can avoid known obstacles and holes based on the optimal task point access sequence, each time from the current task point to the next task point, the A* algorithm is used to generate a locally optimal movement path between the current task point and the next task point under the constraints of the high-precision three-dimensional environment map. All of the aforementioned globally optimal movement paths together constitute the globally optimal movement path.

6. The hierarchical intelligent planning method of claim 2, wherein, In S400, when the robot visits and moves based on the globally optimal movement path, it uses a dynamic window method for real-time detection and obstacle avoidance.

7. The hierarchical intelligent planning method of claim 2, wherein, The S400 specifically includes: The robot moves based on the global optimal movement path, visiting each task point along the path one by one, and executing a preset binding operation strategy according to the type of the current task point after reaching each task point.

8. The hierarchical intelligent planning method of claim 2, wherein, The binding operation strategy specifically includes: S410: If the type of the current task point is "horizontal" - that is, the current task point is a horizontal steel cage to be tied, the lifting mechanism (200) of the robot is controlled to stay at the basic working height of the current task point, and the 3D camera (500) scans the tying point of the current task point, guides the robotic arm (400) to perform the tying operation on the tying point, and completes the tying of the current task point. S420: If the type of the current task point is "vertical" - that is, the current task point is a vertical steel cage to be tied, then control the robot to execute the preset tying lifting cycle strategy for the current task point to complete the tying of the current task point. S430: If the type of the current task point is "tilted" - that is, the current task point is a tilted steel cage to be tied, then control the robot to execute the preset tilted collaborative operation strategy for the current task point to complete the tying of the current task point.

9. The hierarchical intelligent planning method of claim 8, wherein, The binding and lifting cycle strategy specifically includes: S421: Control the lifting mechanism (200) to move to the basic working height of the current task point, and initialize the current working height of the current task point through the basic working height; S422: The robot arm (400) is guided to perform binding operations on the binding points at the current working height by scanning the current task point with the 3D camera (500) to complete the binding of the current task point at the current working height. S423: If the lifting mechanism (200) has not reached the top of the steel cage at the current task point, control the lifting mechanism (200) to lift it up by a preset step length and update the current working height, and return to execute S422; otherwise, it means that the binding operation of all working heights at the current task point has been completed - that is, the binding operation of the current task point has been completed, and continue to S424. S424: Control the lifting mechanism (200) to return to the basic working height of the current task point.

10. The hierarchical intelligent planning method of claim 8, wherein, The tilted collaborative operation strategy specifically includes: S431: Identify the tilt angle and spatial extension length of the current task point based on the lidar (300) and / or 3D camera (500) to extract the three-dimensional operation trajectory surface of the current task point; S432: Control the lifting mechanism (200) to move to the basic working height of the current task point, initialize the binding point of the current task point below the basic working height as the current binding point, and link the chassis (100) to make fine adjustments to the position of the robot to ensure that the robotic arm (400) is within the optimal working arm span range for binding operations. S433: Scan the current binding point of the current task point using a lidar (300) and / or a 3D camera (500), adjust the posture of the robotic arm (400) to ensure that the robotic arm (400) performs the binding operation at an angle perpendicular to the inclined steel bar surface, and complete the binding operation of the current binding point. S434: If the current binding point is located at the end of the current task point, it means that the binding operation of the current task point has been completed, and continue to S435; otherwise, it means that the binding operation of the current task point has not been completed, so move along the three-dimensional operation trajectory surface of the current task point, simultaneously calculate and output the small displacement of the chassis (100) and the elevation change of the lifting mechanism (200), update the current binding point, and return to execute S433; S435: Reset the chassis (100) and lifting mechanism (200) to the safe navigation state.

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