Artificial intelligence driven embodied intelligent four-legged construction robot system with mounted robotic arm

Abstract

The application discloses an AI-driven somatic intelligent four-foot construction robot system with a mechanical arm, relates to the technical fields of artificial intelligence and construction robots, and comprises the following steps: collecting construction site environment information through an intelligent sensing module, generating a three-dimensional environment model and identifying a construction operation target position by an AI control module; planning an optimal moving path after receiving a construction task instruction, dynamically adjusting the path according to obstacle information during the moving process; controlling the mechanical arm to execute the construction operation after reaching the operation area, and dynamically adjusting the action sequence and operation strength according to the real-time feedback construction operation parameters. The application can replace manual work to complete high-risk repetitive construction tasks, has the abilities of environment self-adaptation, high-precision operation and remote monitoring, and effectively improves the construction efficiency and safety.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and construction robot technology, and in particular to an AI-driven, embodied intelligent quadruped construction robot system equipped with a robotic arm. Background Technology

[0002] As the demographic dividend continues to weaken and the workforce in the construction industry ages, traditional construction methods face the dual challenges of rising labor costs and a shortage of skilled workers. High-risk, repetitive tasks in high-altitude and confined spaces still pose significant safety hazards. Therefore, developing intelligent construction robots to replace manual high-risk operations, alleviate labor shortages, and achieve cost reduction and efficiency improvement has become an inevitable choice for the transformation and upgrading of the construction industry.

[0003] While existing construction robots for tasks such as bricklaying and plastering have alleviated the labor shortage to some extent, they generally suffer from limitations such as limited functionality and bulky size, making them difficult to adapt to the complex and ever-changing working conditions on construction sites. Furthermore, these robots rely heavily on pre-programmed instructions, lack autonomous execution and flexible adaptability, and have a low level of intelligence, resulting in high purchase and maintenance costs and poor actual application effects, making it difficult to meet the needs of large-scale and refined construction.

[0004] In recent years, cutting-edge technologies such as artificial intelligence, 5G communication, and the Internet of Things have been deeply integrated with construction machinery, driving the iterative upgrade of construction equipment towards intelligence and autonomy. Embodied intelligent construction robots, relying on intelligent sensing technology and AI decision-making systems, possess environmental perception, real-time decision-making, and multi-machine collaborative operation capabilities. They can achieve millimeter-level construction accuracy, dynamic path planning, and autonomous execution of construction tasks, providing a new technological path for effectively overcoming the technical bottlenecks of existing traditional construction robots. Summary of the Invention

[0005] The technical problem to be solved by this invention is to address the shortcomings of existing technologies. Specifically, it provides an AI-driven, embodied intelligent quadruped construction robot system equipped with a robotic arm, as detailed below: 1) In a first aspect, the present invention provides an AI-driven, embodied intelligent quadruped construction robot system equipped with a robotic arm, the specific technical solution of which is as follows: Includes: a quadruped mobile chassis, a robotic arm, an intelligent sensing module, and an AI control module; The robotic arm is mounted on the quadrupedal mobile chassis; The intelligent sensing module is installed on the quadrupedal mobile chassis and / or the robotic arm, and is used to collect environmental information at the construction site. The AI ​​control module is communicatively connected to the quadrupedal mobile chassis, the robotic arm, and the intelligent sensing module, respectively. The AI ​​control module generates a three-dimensional environmental model of the construction site based on the environmental information collected by the intelligent sensing module, and identifies the location of the construction operation target. Upon receiving a construction task instruction, the system plans the optimal movement path from its current location to the construction work area based on the three-dimensional environment model and the location of the construction target. During the movement, the system dynamically adjusts the optimal movement path based on obstacle information detected in real time by the intelligent sensing module. Control the four-legged mobile chassis to move to the construction work area according to the optimal movement path; After the quadrupedal mobile chassis arrives at the construction work area, a robotic arm motion sequence is generated based on the position of the construction work target, and the robotic arm is controlled to perform construction work according to the robotic arm motion sequence. During the construction operation, the robotic arm's motion sequence and / or construction force are dynamically adjusted based on the construction operation parameters fed back in real time by the intelligent sensing module, until all construction operation targets within the construction operation area are completed.

[0006] The beneficial effects of the AI-driven, embodied intelligent quadruped construction robot system equipped with a robotic arm provided by this invention are as follows: This invention combines a quadrupedal mobile chassis with a multi-degree-of-freedom robotic arm, and is equipped with an intelligent sensing module and an AI control module. This enables the robot to autonomously construct a 3D environmental model of the construction site and identify the location of the work target. It plans the optimal movement path and dynamically adjusts the path based on real-time obstacle information during movement. Upon reaching the work area, it automatically generates a sequence of robotic arm movements to execute the construction work. It also dynamically adjusts the robotic arm's movements and force based on real-time feedback of work parameters. This invention solves the problems of traditional construction robots, such as limited functionality, bulky body, reliance on preset programs, weak autonomous adaptability, and the high safety risks and rising labor costs associated with manual operation in high-risk scenarios. It enables the robot to move flexibly and perform autonomous and precise operations in complex and confined spaces, improving construction safety, work accuracy, and project quality, while reducing labor costs and equipment maintenance costs.

[0007] Based on the above solution, the present invention can be further improved as follows.

[0008] Furthermore, the quadrupedal mobile chassis is made of lightweight, high-strength alloy material. The quadrupedal mobile chassis includes four legs, each of which is equipped with a buffer and shock absorption structure and an anti-slip and wear-resistant pad. The quadrupedal mobile chassis has a built-in drive motor and steering mechanism, which are controlled by the AI ​​control module to realize forward, backward, steering, climbing and obstacle crossing actions.

[0009] Furthermore, the AI ​​control module incorporates a path planning algorithm, an obstacle avoidance decision algorithm, and a construction operation action sequence control algorithm. The path planning algorithm is used to autonomously plan the optimal movement path and dynamically adjust it based on the environmental data collected by the intelligent sensing module. The obstacle avoidance decision algorithm is used to determine whether to bypass or cross obstacles based on their size and distance. The construction operation action sequence control algorithm is used to dynamically adjust the robotic arm's action sequence based on real-time data fed back by the robotic arm.

[0010] Furthermore, the intelligent sensing module includes: a lidar, a vision sensor, a distance sensor, and a force sensor; the lidar and the vision sensor are used to collect location information of the terrain, obstacles, and construction targets at the construction site; the distance sensor is installed on the four limbs of the quadrupedal mobile chassis and the end of the robotic arm to detect the distance to surrounding objects; the force sensor is installed on the end of the robotic arm to detect the binding force during the construction operation; the location information, the distance, and the binding force constitute the environmental information.

[0011] Furthermore, it also includes a communication module, which is used to interact with the back-end management system in real time, receive the construction task instructions, and upload the operation status information.

[0012] 2) In a second aspect, the present invention also provides an AI-driven control method for an embodied intelligent quadruped construction robot equipped with a robotic arm, the specific technical solution of which is as follows: The construction site environmental information is collected through an intelligent sensing module; the intelligent sensing module is installed on the four-legged mobile chassis and / or the robotic arm, and the robotic arm is mounted on the four-legged mobile chassis; Based on the environmental information collected by the intelligent sensing module, the AI ​​control module generates a three-dimensional environmental model of the construction site and identifies the location of the construction operation target. Upon receiving a construction task instruction, the system plans the optimal movement path from its current location to the construction work area based on the three-dimensional environment model and the location of the construction target. During the movement, the system dynamically adjusts the optimal movement path based on obstacle information detected in real time by the intelligent sensing module. Control the four-legged mobile chassis to move to the construction work area according to the optimal movement path; After the quadrupedal mobile chassis arrives at the construction work area, a robotic arm motion sequence is generated based on the position of the construction work target, and the robotic arm is controlled to perform construction work according to the robotic arm motion sequence. During the construction operation, the robotic arm's motion sequence and / or construction force are dynamically adjusted based on the construction operation parameters fed back in real time by the intelligent sensing module, until all construction operation targets within the construction operation area are completed.

[0013] Based on the above solution, the present invention can be further improved as follows.

[0014] Furthermore, the quadrupedal mobile chassis is made of lightweight, high-strength alloy material. The quadrupedal mobile chassis includes four legs, each of which is equipped with a buffer and shock absorption structure and an anti-slip and wear-resistant pad. The quadrupedal mobile chassis has a built-in drive motor and steering mechanism, which are controlled by the AI ​​control module to realize forward, backward, steering, climbing and obstacle crossing actions.

[0015] Furthermore, the AI ​​control module incorporates a path planning algorithm, an obstacle avoidance decision algorithm, and a construction operation action sequence control algorithm. The path planning algorithm is used to autonomously plan the optimal movement path and dynamically adjust it based on the environmental data collected by the intelligent sensing module. The obstacle avoidance decision algorithm is used to determine whether to bypass or cross obstacles based on their size and distance. The construction operation action sequence control algorithm is used to dynamically adjust the robotic arm's action sequence based on real-time data fed back by the robotic arm.

[0016] Furthermore, the intelligent sensing module includes: a lidar, a vision sensor, a distance sensor, and a force sensor; the lidar and the vision sensor are used to collect location information of the terrain, obstacles, and construction targets at the construction site; the distance sensor is installed on the four limbs of the quadrupedal mobile chassis and the end of the robotic arm to detect the distance to surrounding objects; the force sensor is installed on the end of the robotic arm to detect the binding force during the construction operation; the location information, the distance, and the binding force constitute the environmental information.

[0017] Furthermore, the system interacts with the backend management system in real time via a communication module, receiving construction task instructions and uploading operation status information.

[0018] 3) In a third aspect, the present invention also provides an electronic device, the electronic device including a processor coupled to a memory, the memory storing at least one computer program, the at least one computer program being loaded and executed by the processor to enable the electronic device to perform any of the above methods.

[0019] 4) In a fourth aspect, the present invention also provides a computer-readable storage medium storing at least one computer program, which is loaded and executed by a processor to enable a computer to implement any of the above methods.

[0020] It should be noted that the beneficial effects of the technical solutions of the second to fourth aspects of the present invention and their corresponding possible implementations can be found in the above description of the technical effects of the first aspect and its corresponding possible implementations, and will not be repeated here. Attached Figure Description

[0021] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating an AI-driven, embodied intelligent quadrupedal construction robot system equipped with a robotic arm, according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the architecture of an AI-driven, embodied intelligent quadrupedal construction robot system equipped with a robotic arm, according to an embodiment of the present invention. Figure 3 This is a structural framework diagram of an electronic device according to the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0023] like Figure 1 as well as Figure 2 As shown in the figure, an AI-driven, embodied intelligent quadrupedal construction robot system equipped with a robotic arm according to an embodiment of the present invention includes: Includes: a quadruped mobile chassis, a robotic arm, an intelligent sensing module, and an AI control module; The robotic arm is mounted on the quadrupedal mobile chassis; The intelligent sensing module is installed on the quadrupedal mobile chassis and / or the robotic arm, and is used to collect environmental information at the construction site. The AI ​​control module is communicatively connected to the quadrupedal mobile chassis, the robotic arm, and the intelligent sensing module, respectively. The AI ​​control module generates a three-dimensional environmental model of the construction site based on the environmental information collected by the intelligent sensing module, and identifies the location of the construction operation target. Upon receiving a construction task instruction, the system plans the optimal movement path from its current location to the construction work area based on the three-dimensional environment model and the location of the construction target. During the movement, the system dynamically adjusts the optimal movement path based on obstacle information detected in real time by the intelligent sensing module. Control the four-legged mobile chassis to move to the construction work area according to the optimal movement path; After the quadrupedal mobile chassis arrives at the construction work area, a robotic arm motion sequence is generated based on the position of the construction work target, and the robotic arm is controlled to perform construction work according to the robotic arm motion sequence. During the construction operation, the robotic arm's motion sequence and / or construction force are dynamically adjusted based on the construction operation parameters fed back in real time by the intelligent sensing module, until all construction operation targets within the construction operation area are completed.

[0024] The beneficial effects of the AI-driven, embodied intelligent quadruped construction robot system equipped with a robotic arm provided by this invention are as follows: This invention combines a quadrupedal mobile chassis with a multi-degree-of-freedom robotic arm, and is equipped with an intelligent sensing module and an AI control module. This enables the robot to autonomously construct a 3D environmental model of the construction site and identify the location of the work target. It plans the optimal movement path and dynamically adjusts the path based on real-time obstacle information during movement. Upon reaching the work area, it automatically generates a sequence of robotic arm movements to execute the construction work. It also dynamically adjusts the robotic arm's movements and force based on real-time feedback of work parameters. This invention solves the problems of traditional construction robots, such as limited functionality, bulky body, reliance on preset programs, weak autonomous adaptability, and the high safety risks and rising labor costs associated with manual operation in high-risk scenarios. It enables the robot to move flexibly and perform autonomous and precise operations in complex and confined spaces, improving construction safety, work accuracy, and project quality, while reducing labor costs and equipment maintenance costs.

[0025] To facilitate understanding, the following explanations are provided for the terms used in this plan: Artificial Intelligence (AI) refers to the theories, methods, technologies, and application systems that enable computers or machines to simulate and extend human intelligence. The AI ​​control module in this solution embeds an AI processor and related intelligent algorithms.

[0026] Quadruped mobile chassis: refers to a robot chassis system that uses a four-legged structure for movement. Unlike wheeled or tracked chassis, it has a stronger ability to adapt to complex terrain.

[0027] A robotic arm is a multi-joint linkage mechanism that simulates the function of a human arm, capable of performing operations such as grasping, moving, and rotating within space. In this design, the robotic arm employs a six-degree-of-freedom (DOF) serial structure (six DEFs refer to the six parameters that allow the robotic arm's end effector (the furthest working point) to move independently in space, including: three positional DEFs: linear movement along the X, Y, and Z axes (forward, backward, left, right, up, down); and three attitude DEFs: rotational movement around the X, Y, and Z axes (pitch, yaw, roll). A serial structure means the robotic arm is composed of multiple links and joints connected one after another. Each joint drives all subsequent parts to move together. The opposite of a serial structure is a parallel structure (such as some high-precision positioning platforms), where multiple branches connect to the moving platform.). The base is fixed to the top platform of a four-legged mobile chassis, and the robotic arm's end effector (the furthest point from the base, also known as the lower end) is the execution terminal. The robotic arm's end effector is equipped with replaceable grippers for grasping wire or other construction tools.

[0028] The vision and force sensors in the intelligent sensing module are both installed at the end of the robotic arm: the vision sensor is located directly behind the gripper and moves with the end effector, actively aligning with the construction target for high-definition imaging; the force sensor is installed between the end flange and the gripper to detect the force between the gripper and the rebar during the binding process (i.e., binding force). In addition, a miniature ultrasonic distance sensor is installed on each side of the robotic arm to dynamically detect the distance between each arm segment and surrounding obstacles.

[0029] Because the vision sensor moves continuously with the robotic arm's end effector, the AI ​​control module incorporates a motion compensation algorithm (based on the robotic arm's forward kinematics model and joint encoder feedback) to acquire stable and clear environmental information. This algorithm reads the angle data of each joint of the robotic arm in real time, calculates the precise spatial coordinates of the end effector's vision sensor, and performs geometric correction on the acquired images to eliminate blur and distortion caused by motion. Simultaneously, during the binding operation, the robotic arm's end effector remains stationary relative to the rebar, allowing the force sensor to accurately acquire the binding force under static conditions, unaffected by motion. Distance sensors mounted on the joints continuously detect during the robotic arm's movement, and their measurements change dynamically. The AI ​​control module uses this real-time data to determine if there is a collision risk at different parts of the robotic arm and adjusts the action sequence accordingly.

[0030] Intelligent sensing module: refers to a collection of components consisting of various sensors used to collect information about the robot itself and its external environment.

[0031] LiDAR (Light Detection and Ranging): An active optical remote sensing technology that uses laser beams to accurately measure target distances and generate three-dimensional spatial information by emitting laser beams and receiving reflected signals.

[0032] Visual sensors: These are devices that use image sensors to collect environmental image information and use visual algorithms to identify the location, shape, and other features of targets.

[0033] Back-end management system: refers to the host computer system located at the construction site or remote control center, used to issue task instructions to the robot and receive and display the operation status and data returned by the robot.

[0034] The following detailed description is provided in conjunction with specific embodiments.

[0035] Example 1: An AI-driven, embodied intelligent quadruped construction robot equipped with a robotic arm is used for tying steel bars in floor slabs at construction sites.

[0036] Step 1: Initialization and 3D Environment Modeling After the robot is activated, the intelligent sensing modules installed on the head of the quadrupedal mobile chassis and the end of the robotic arm begin to operate. The LiDAR scanner performs a 360-degree scan of the construction site, while the visual sensors collect image data of the construction area. The AI ​​control module receives the preliminary terrain and obstacle location information collected by the sensors and uses the Simultaneous Localization and Mapping (SLAM) algorithm to generate a 3D environment model of the construction site. Simultaneously, the AI ​​control module uses the visual sensors to identify the locations of the intersections of the floor slab reinforcement bars, using these as the locations of the construction targets.

[0037] By combining the AI ​​control module with LiDAR and vision sensors, the robot can autonomously complete 3D modeling of the construction site and precise positioning of the work target, which solves the shortcomings of traditional construction robots that rely on preset maps and cannot adapt to unfamiliar environments, and significantly improves the robot's environmental adaptability.

[0038] Step 2: Receiving the task and planning the route The AI ​​control module receives the rebar tying task instructions from the backend management system via the communication module. These instructions include the boundary coordinates of the tying area and tying parameters (such as tying spacing and target tying force). Based on the 3D environment model generated in step one and the identified construction target location, the AI ​​control module calls a built-in path planning algorithm (e.g., a fast random search tree algorithm) to calculate the optimal collision-free movement path from the robot's current position to the tying area. As the quadrupedal mobile chassis moves along this optimal path, distance sensors mounted on the four legs continuously monitor the distance between the robot and surrounding obstacles such as construction materials and temporary supports. When the AI ​​control module determines that an obstacle is within a preset safe distance threshold, it immediately calls the obstacle avoidance decision algorithm. Based on the obstacle's size, it determines whether to use a bypass method (if there is sufficient lateral space) or a crossing method (if the obstacle height is less than 15 cm), dynamically adjusting the original optimal movement path.

[0039] Relying on the path planning and obstacle avoidance algorithms built into the AI ​​control module, the robot can autonomously plan the optimal path and respond to sudden obstacles in real time during movement, solving the problem of "extreme reliance on preset programs and insufficient flexibility" mentioned in the background technology, and realizing autonomous movement capability in dynamic environments.

[0040] Step 3: Move to the construction work area The AI ​​control module converts the adjusted movement path into gait control commands for the quadruped mobile chassis, controlling the built-in drive motor and steering mechanism to work together. The quadruped mobile chassis uses a diagonal or triped gait, executing forward, turning, climbing, or traversing movements according to the AI ​​control module's commands until it reaches the designated area for slab rebar tying. Upon arrival, the AI ​​control module adjusts the robot's overall posture so that the range of motion of the robotic arm's end effector completely covers the tying area.

[0041] Compared to traditional wheeled or tracked chassis, the quadruped mobile chassis can easily traverse narrow spaces on construction sites (such as between walls and columns) and climb uneven ground, solving the shortcomings of existing construction robots that are "bulky and lack flexibility".

[0042] Step 4: The robotic arm performs the construction work. Based on the three-dimensional coordinates of the rebar intersections, the AI ​​control module generates a sequence of robotic arm movements using inverse kinematics. This sequence includes the rotation angles and movement speeds of each joint, as well as the grasping timing of the end effector. The AI ​​control module then initiates the six-degree-of-freedom robotic arm. The end effector grasps the binding wire from the robot's storage compartment and moves sequentially to each rebar intersection according to the movement sequence, completing the binding wire winding and tightening actions. During the tightening process, a force sensor installed at the end effector detects the binding force in real time and feeds the force data back to the AI ​​control module.

[0043] By combining a multi-degree-of-freedom robotic arm with AI control, the automated execution of rebar tying operations has been achieved, replacing manual high-risk and repetitive labor and solving the industry pain points of rising labor costs and a shortage of skilled workers.

[0044] Step 5: Real-time feedback and dynamic adjustment During the binding operation, the AI ​​control module continuously receives binding force values ​​from the force sensor. The pre-set construction action sequence control algorithm within the AI ​​control module compares the current force value with the target binding force (e.g., 100 Newtons). If the detected binding force is less than the target value, the AI ​​control module increases the tightening angle of the end effector or applies pressure until the force reaches the target. If the binding force exceeds the target value, the AI ​​control module immediately reduces the tightening amplitude to prevent wire breakage or rebar damage. Simultaneously, the vision sensor continuously monitors the rebar intersection positions. If a slight shift in the rebar position is detected due to the binding action, the AI ​​control module synchronously adjusts the target position coordinates in the robotic arm's action sequence. This dynamic adjustment process continues in a closed-loop control manner until the binding operation of all rebar intersections within the binding area is completed.

[0045] By using real-time feedback from force and vision sensors and dynamic adjustments from the AI ​​control module, precise control of the binding force is achieved, ensuring that the construction quality meets the standards. This solves the problem of "low construction accuracy" in traditional robots and improves construction efficiency.

[0046] Step Six: Completion and Continued Work Once the AI ​​control module determines that all construction targets within the current work area have been completed, it generates a work completion signal and uploads it to the backend management system via the communication module. If the backend management system still has construction task instructions for the next tying area, the AI ​​control module automatically repeats steps two through five above until all floor slab rebar tying tasks are completed.

[0047] The robot has the ability to operate continuously and autonomously, which can significantly improve construction progress and shorten the construction period. At the same time, it can interact with the back-end management system in real time through the communication module, realizing automatic continuation and remote scheduling of tasks in multiple areas, and reducing on-site management costs.

[0048] Based on the above solution, the present invention can be further improved as follows.

[0049] Furthermore, the quadrupedal mobile chassis is made of lightweight, high-strength alloy material. The quadrupedal mobile chassis includes four legs, each of which is equipped with a buffer and shock absorption structure and an anti-slip and wear-resistant pad. The quadrupedal mobile chassis has a built-in drive motor and steering mechanism, which are controlled by the AI ​​control module to realize forward, backward, steering, climbing and obstacle crossing actions.

[0050] As described in Example 1, the quadruped mobile chassis is made of lightweight, high-strength aluminum alloy to reduce the overall weight while ensuring structural strength. Each leg is equipped with a rubber cushioning and shock-absorbing structure to absorb the impact force generated when the robot walks or crosses obstacles, protecting the internal precision sensors. Anti-slip and wear-resistant pads are attached to the bottom of the legs to increase friction with rough surfaces (such as concrete slabs and steel mesh) on the construction site and prevent slippage. The quadruped mobile chassis integrates a drive motor (e.g., a brushless DC motor) and a planetary gear steering mechanism. The AI ​​control module precisely controls the motor speed and direction through pulse width modulation (PWM) signals, thereby enabling actions such as forward movement, backward movement, turning in place, climbing (up to 30 degrees), and crossing obstacles (up to 15 cm in height).

[0051] Furthermore, the AI ​​control module incorporates a path planning algorithm, an obstacle avoidance decision algorithm, and a construction operation action sequence control algorithm. The path planning algorithm is used to autonomously plan the optimal movement path and dynamically adjust it based on the environmental data collected by the intelligent sensing module. The obstacle avoidance decision algorithm is used to determine whether to bypass or cross obstacles based on their size and distance. The construction operation action sequence control algorithm is used to dynamically adjust the robotic arm's action sequence based on real-time data fed back by the robotic arm.

[0052] The AI ​​control module has three core algorithms embedded within it: Path planning algorithm: based on The search algorithm, combined with real-time environmental data (such as obstacle location and ground flatness) collected by the intelligent sensing module, searches for the energy-optimal or time-optimal path from the current location to the target location in the three-dimensional environment model, and replans the path when the terrain changes or temporary obstacles appear.

[0053] Obstacle avoidance decision algorithm: Based on the obstacle distance measured by the distance sensor and the obstacle size (height and width) obtained by the lidar, the algorithm makes a judgment according to the preset decision tree: if the obstacle height is lower than the crossing capability threshold (15 cm) of the four-legged mobile chassis, the crossing method is selected; otherwise, the algorithm searches for side avoidance space and plans the avoidance path.

[0054] Construction operation action sequence control algorithm: The algorithm framework based on model predictive control (MPC) is adopted. The action command for the next moment is adjusted in real time according to the binding force feedback from the force sensor at the end of the robotic arm, forming a closed loop control to ensure the smoothness and accuracy of the binding action.

[0055] The three algorithms work together to solve the autonomous control problems in three aspects: mobile navigation, obstacle avoidance response, and precision operation, which significantly improves the robot's intelligence level and enables it to cope with complex construction conditions without relying on preset programs.

[0056] Furthermore, the intelligent sensing module includes: a lidar, a vision sensor, a distance sensor, and a force sensor; the lidar and the vision sensor are used to collect location information of the terrain, obstacles, and construction targets at the construction site; the distance sensor is installed on the four limbs of the quadrupedal mobile chassis and the end of the robotic arm to detect the distance to surrounding objects; the force sensor is installed on the end of the robotic arm to detect the binding force during the construction operation; the location information, the distance, and the binding force constitute the environmental information.

[0057] Furthermore, it also includes a communication module, which is used to interact with the back-end management system in real time, receive the construction task instructions, and upload the operation status information.

[0058] This invention also provides an AI-driven control method for an embodied intelligent quadruped construction robot equipped with a robotic arm, the specific technical solution of which is as follows: The construction site environmental information is collected through an intelligent sensing module; the intelligent sensing module is installed on the four-legged mobile chassis and / or the robotic arm, and the robotic arm is mounted on the four-legged mobile chassis; Based on the environmental information collected by the intelligent sensing module, the AI ​​control module generates a three-dimensional environmental model of the construction site and identifies the location of the construction operation target. Upon receiving a construction task instruction, the system plans the optimal movement path from its current location to the construction work area based on the three-dimensional environment model and the location of the construction target. During the movement, the system dynamically adjusts the optimal movement path based on obstacle information detected in real time by the intelligent sensing module. Control the four-legged mobile chassis to move to the construction work area according to the optimal movement path; After the quadrupedal mobile chassis arrives at the construction work area, a robotic arm motion sequence is generated based on the position of the construction work target, and the robotic arm is controlled to perform construction work according to the robotic arm motion sequence. During the construction operation, the robotic arm's motion sequence and / or construction force are dynamically adjusted based on the construction operation parameters fed back in real time by the intelligent sensing module, until all construction operation targets within the construction operation area are completed.

[0059] It should be noted that the beneficial effects of the AI-driven control method for an intelligent quadrupedal construction robot equipped with a robotic arm provided in the above embodiments are the same as the beneficial effects of the AI-driven control system for an intelligent quadrupedal construction robot equipped with a robotic arm described above, and will not be repeated here. Furthermore, the system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiments, and will not be repeated here.

[0060] like Figure 3 As shown, an electronic device 300 according to an embodiment of the present invention includes a processor 320 coupled to a memory 310. The memory 310 stores at least one computer program 330, which is loaded and executed by the processor 320 to enable the electronic device 300 to implement any of the above-mentioned methods. Specifically: The electronic device 300 can vary considerably due to differences in configuration or performance. It may include one or more processors 320 (Central Processing Units, CPUs) and one or more memories 310. The memories 310 store at least one computer program 330, which is loaded and executed by the processors 320 to enable the electronic device 300 to implement the AI-driven control method for an intelligent quadrupedal construction robot equipped with a robotic arm as described in the above embodiment. Of course, the electronic device 300 may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. It may also include other components for implementing device functions, which will not be elaborated upon here.

[0061] An embodiment of the present invention provides a computer-readable storage medium storing at least one computer program, which is loaded and executed by a processor to enable a computer to implement any of the above-described methods.

[0062] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc.

[0063] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform any of the methods described above.

[0064] It should be noted that the terms "first" and "second" in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0065] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this disclosure can be specifically implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0066] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0067] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. An AI-driven, embodied intelligent quadruped construction robot system equipped with a robotic arm, characterized in that, include: A quadruped mobile chassis, a robotic arm, an intelligent sensing module, and an AI control module; The robotic arm is mounted on the quadrupedal mobile chassis; The intelligent sensing module is installed on the quadrupedal mobile chassis and / or the robotic arm, and is used to collect environmental information at the construction site. The AI ​​control module is communicatively connected to the quadrupedal mobile chassis, the robotic arm, and the intelligent sensing module, respectively. The AI ​​control module generates a three-dimensional environmental model of the construction site based on the environmental information collected by the intelligent sensing module, and identifies the location of the construction operation target. Upon receiving a construction task instruction, the system plans the optimal movement path from its current location to the construction work area based on the three-dimensional environment model and the location of the construction target. During the movement, the system dynamically adjusts the optimal movement path based on obstacle information detected in real time by the intelligent sensing module. Control the four-legged mobile chassis to move to the construction work area according to the optimal movement path; After the quadrupedal mobile chassis arrives at the construction work area, a robotic arm motion sequence is generated based on the position of the construction work target, and the robotic arm is controlled to perform construction work according to the robotic arm motion sequence. During the construction operation, the robotic arm's motion sequence and / or construction force are dynamically adjusted based on the construction operation parameters fed back in real time by the intelligent sensing module, until all construction operation targets within the construction operation area are completed.

2. The AI-driven, robotic-arm-equipped, embodied intelligent quadruped construction robot system according to claim 1, characterized in that, The four-legged mobile chassis is made of lightweight, high-strength alloy material. The four-legged mobile chassis includes four legs, each of which is equipped with a buffer and shock absorption structure and an anti-slip and wear-resistant pad. The four-legged mobile chassis has a built-in drive motor and steering mechanism, which are controlled by the AI ​​control module to realize forward, backward, steering, climbing and obstacle crossing actions.

3. The AI-driven, robotic-arm-equipped, embodied intelligent quadruped construction robot system according to claim 1, characterized in that, The AI ​​control module incorporates a path planning algorithm, an obstacle avoidance decision algorithm, and a construction operation action sequence control algorithm. The path planning algorithm is used to autonomously plan the optimal movement path and dynamically adjust it based on the environmental data collected by the intelligent sensing module. The obstacle avoidance decision algorithm is used to determine whether to bypass or cross obstacles based on their size and distance. The construction operation action sequence control algorithm is used to dynamically adjust the robotic arm's action sequence based on real-time data fed back by the robotic arm.

4. The AI-driven, robotic-arm-equipped, embodied intelligent quadruped construction robot system according to claim 1, characterized in that, The intelligent sensing module includes: a lidar, a vision sensor, a distance sensor, and a force sensor; the lidar and the vision sensor are used to collect location information of the terrain, obstacles, and construction targets at the construction site; the distance sensor is installed on the four limbs of the quadrupedal mobile chassis and the end of the robotic arm to detect the distance to surrounding objects; the force sensor is installed on the end of the robotic arm to detect the binding force during the construction operation; the location information, the distance, and the binding force constitute the environmental information.

5. The AI-driven, robotic-arm-equipped, embodied intelligent quadruped construction robot system according to claim 1, characterized in that, Also includes: The communication module is used to interact with the back-end management system in real time, receive the construction task instructions, and upload the operation status information.

6. A control method for an AI-driven, embodied intelligent quadrupedal construction robot equipped with a robotic arm, characterized in that, The embodied intelligent quadruped construction robot includes: a quadruped mobile chassis and a robotic arm, and the method is specifically as follows: The construction site environmental information is collected through an intelligent sensing module; the intelligent sensing module is installed on the four-legged mobile chassis and / or the robotic arm, and the robotic arm is mounted on the four-legged mobile chassis; Based on the environmental information collected by the intelligent sensing module, the AI ​​control module generates a three-dimensional environmental model of the construction site and identifies the location of the construction operation target. Upon receiving a construction task instruction, the system plans the optimal movement path from its current location to the construction work area based on the three-dimensional environment model and the location of the construction target. During the movement, the system dynamically adjusts the optimal movement path based on obstacle information detected in real time by the intelligent sensing module. Control the four-legged mobile chassis to move to the construction work area according to the optimal movement path; After the quadrupedal mobile chassis arrives at the construction work area, a robotic arm motion sequence is generated based on the position of the construction work target, and the robotic arm is controlled to perform construction work according to the robotic arm motion sequence. During the construction operation, the robotic arm's motion sequence and / or construction force are dynamically adjusted based on the construction operation parameters fed back in real time by the intelligent sensing module, until all construction operation targets within the construction operation area are completed.

7. The control method for an AI-driven, embodied intelligent quadrupedal construction robot equipped with a robotic arm according to claim 6, characterized in that, The four-legged mobile chassis is made of lightweight, high-strength alloy material. The four-legged mobile chassis includes four legs, each of which is equipped with a buffer and shock absorption structure and an anti-slip and wear-resistant pad. The four-legged mobile chassis has a built-in drive motor and steering mechanism, which are controlled by the AI ​​control module to realize forward, backward, steering, climbing and obstacle crossing actions.

8. The control method for an AI-driven, embodied intelligent quadrupedal construction robot equipped with a robotic arm according to claim 6, characterized in that, The AI ​​control module incorporates a path planning algorithm, an obstacle avoidance decision algorithm, and a construction operation action sequence control algorithm. The path planning algorithm is used to autonomously plan the optimal movement path and dynamically adjust it based on the environmental data collected by the intelligent sensing module. The obstacle avoidance decision algorithm is used to determine whether to bypass or cross obstacles based on their size and distance. The construction operation action sequence control algorithm is used to dynamically adjust the robotic arm's action sequence based on real-time data fed back by the robotic arm.

9. An electronic device, characterized in that, The electronic device includes a processor coupled to a memory storing at least one computer program, which is loaded and executed by the processor to enable the electronic device to perform the method as described in any one of claims 6 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer program, which is loaded and executed by a processor to enable the computer to perform the method as described in any one of claims 6 to 8.

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