866 results about "Legged robot" patented technology

The present invention provides an intelligent robot for power inspection and an inspection method thereof, comprising a quadruped robot, wherein a laser radar, a voiceprint sensor and an infrared sensor are provided at the front end of the quadruped robot; a pan-tilt base is provided at the middle of the quadruped robot, and a camera is provided on the pan-tilt base; a six-degree-of-freedom mechanical arm is also provided at the tail of the quadruped robot, and a mechanical claw is provided at the end of the mechanical arm. The robot flexibly plans tasks based on the layout of the substation, sets inspection cycles for different equipment according to their importance, and automatically generates accurate task plans, thereby avoiding the tediousness and omissions of manual planning and improving planning. The advanced path planning algorithm enables the robot to quickly plan the optimal path, integrates multiple positioning technologies to ensure accurate navigation, and effectively reduces travel time. Multiple inspection modes are carried out in parallel, non-stop inspections are dynamic screening, routine inspections are comprehensive inspections, and non-preset inspections are flexibly targeted at specific points, which greatly improves the coverage efficiency of the inspection and avoids detection blind spots.

A legged robot planning and control method and apparatus, a robot, and a storage medium. The method comprises: according to the motion state of a legged robot and swing parameters of each leg at the next moment, determining a foot-end reference state of each leg; for a leg to swing at the next moment, according to a foot-end dynamics model and a discrete collision model, planning a swing-leg trajectory having collision awareness; and, according to the motion state, a reference state sequence and the foot-end reference states, controlling the whole body of the legged robot, wherein for a leg which should be a planned supporting leg but is not in contact with the ground, whole-body control having collision awareness is performed according to a whole-body dynamics model and the discrete collision model. Thus, the problems are solved that legged robots in the prior art are limited in passive flexible buffering in collision, involves a large amount of control algorithm calculation and relatively low control precision, exhibit a relatively high degree of hardware wear and relatively short service lives, and cannot meet actual needs.

The invention discloses a quadruped robot motion control method and system based on reinforcement learning, and the method can simulate and analyze the motion behavior of a quadruped robot in a virtual environment through building a simulation model of the quadruped robot, remarkably reduces the research and development cost, remarkably shortens the research and development period, converts a balance control problem into a Markov decision process, defines a state, an action and a reward function, and improves the reliability of the quadruped robot. A more intelligent and adaptive balance strategy can be realized, a deterministic strategy gradient algorithm and a simulation model are adopted for interactive training, an efficient balance control strategy can be obtained, robot behaviors are adjusted in real time to keep balance, a walking task is decomposed into two sub-tasks of foothold selection and gait generation by utilizing a hierarchical control idea, and the walking efficiency is improved. Through reinforcement learning, walking flexibility and stability of the robot on irregular terrains are improved, the irregular terrains are built on a simulation platform for experimental verification, robot performance can be safely and repeatedly evaluated, and a strategy is further optimized.

The invention discloses a quadruped robot motion control method based on adaptive deep reinforcement learning. The method comprises the following steps: S1, determining a network model, a composite reward function, a state space and a bionic action generation mechanism of a simulation training environment; the state space provides environment information input, the network model processes the input information and generates a decision, the bionic action mechanism executes a specific decision behavior, and the composite reward function evaluates a behavior effect and optimizes a decision direction; s2, constructing a simulation training environment of the quadruped robot, wherein the simulation environment comprises quadruped robot model information and simulation environment information; s3, training the network model by using a deep reinforcement learning algorithm based on robot model information and simulated environment information to obtain a trained motion control strategy; and S4, verifying the feasibility of utilizing the trained motion control strategy by controlling the motion of the quadruped robot in a real environment. According to the invention, the self-adaptive capability to a complex environment is obviously improved.

A robot in accordance with at least some embodiments of the present technology includes a neck region through which the robot is configured to transmit and receive light. The robot defines a robot height and includes a body, a head carried by the body, and a neck extending between the head and the body along the robot height. The robot further includes a structural neck frame at a periphery of the neck in a plane perpendicular to the robot height. The structural neck frame defines windows distributed around the periphery of the neck. The robot further includes a sensor disposed at least partially within the structural neck frame. The sensor includes a laser configured to transmit light via the windows and a detector configured to receive light via the windows. A field of view of the sensor extends at least 320 degrees around the neck.

A balance control method for a wheel-legged robot is provided. The robot includes a moving wheel, n links, and n rotating joints, the moving wheel being connected to a first link through a first rotating joint of the n rotating joints, and the n links being connected in series through n−1 rotating joints other than the first rotating joint, n being a positive integer greater than 1. The method includes: acquiring a state quantity of the wheel-legged robot at a first moment; determining dynamics model parameters according to a dynamics equation of the wheel-legged robot and the state quantity at the first moment; establishing a sliding surface according to the state quantity at the first moment; calculating rotation torques of the n rotating joints according to the sliding surface and the dynamics model parameters; and controlling the rotating joints according to the rotation torques of the n rotating joints.

The invention discloses a self-adaptive gait control method, system and device for a quadruped robot in a limited space and a medium, and belongs to the technical field of gait control. Identifying terrain categories through a classifier and acquiring initial gait parameters from a gait library; adjusting the gait parameters according to the terrain gradient and the roughness to obtain optimized gait parameters; calculating stability indexes and fusing scores; when the score is lower than a threshold value, an adjustment strategy is determined according to an instability mode to correct gait parameters; a foot end track is generated and control is executed; and the executed information is fed back to terrain perception to form a closed loop. According to the method, the terrain recognition accuracy is improved through multi-modal information fusion, the adaptive mapping from the terrain to the gait is established to achieve automatic gait switching and parameter optimization, the stability is evaluated through multi-index fusion, a targeted adjustment strategy is selected according to an instability mode, the adaptive gait control problem under the limited space and the complex terrain is solved, and the adaptive gait recognition accuracy is improved. And the movement efficiency and the stability are improved.

The invention belongs to the related technical field of mobile robots, and discloses a rolling-walking composite motion control method and device for a double-wheel-foot robot, and the method comprises the steps: (1) simplifying the rolling motion of wheels of the double-wheel-foot robot into the sliding motion of a sliding block; (2) constructing a system kinetic equation of the double-wheel-foot robot based on the simplified kinetic model; constructing a constraint equation based on the contact state of the two legs and the ground; discretizing the system kinetic equation, the cost function and the constraint equation, and converting model prediction control problems corresponding to the system kinetic equation, the cost function and the constraint equation into nonlinear problems for numerical solution so as to complete leg motion planning; (3) calculating the rotation speed of the wheel based on the leg motion planning result; and (4) based on the rotation speed of the wheels and the leg motion planning result, a whole-body control algorithm is adopted to calculate and obtain moment instructions of leg joints and the wheels. According to the invention, the state space dimension is reduced, and the wheel-ground contact constraint is simplified.

A legged robot motion control method includes: acquiring centroid state data of a spatial path start point and a spatial path end point of a motion path; determining a target landing point of afoot of the legged robot in the motion path based on the spatial path start point and the spatial path end point; determining a change relationship between a centroid position change coefficient and a foot contact force based on the centroid state data; selecting, under constraint of a constraint condition set, a target centroid position change coefficient that meets the change relationship; the constraint condition set including a spatial landing point constraint condition; determining a target motion control parameter according to the target centroid position change coefficient and the target landing point of the foot; and controlling, based on the target motion control parameter, the legged robot to perform motion according to the motion path.

The invention belongs to the technical field of robot control, and provides a quadruped robot fall-down self-recovery control method and system under complex terrains. The method comprises the following steps: extracting a tumble self-recovery strategy feature from privilege information subjected to normalization processing by using a teacher network, so as to provide an optimization reference for a student network; learning strategy characteristics simulating a teacher network by using a student network so as to process the normalized historical observation information of the ontology state information; after the strategy converges, the Actor network is used for processing the output characteristics of the student network and the real-time observation information of the normalized body state information, the next state observation information, the reward signal, the cost feedback and the task end mark are obtained through interaction with the simulation environment, and the action of the quadruped robot is output; evaluating the value of adopting a specific action in the current state; and processing the privilege information subjected to normalization processing by using a Cost network, and evaluating the cost of the current action of the quadruped robot.

The invention provides a foot type robot task planning control method and system, and relates to the technical field of robot control, and the method comprises the steps: obtaining a task instruction and multi-source sensing data of a robot; extracting the task instruction to obtain a semantic feature, and constructing a structured environment representation according to the multi-source sensing data; based on the semantic features, performing retrieval from a preset historical task library to obtain historical task experience fragments corresponding to the task instruction; a hierarchical task plan of the robot is generated through a language model according to historical task experience fragments in combination with structured environment representation, feasibility and safety verification is carried out, a target skill sequence of the robot is obtained, skill arrangement is carried out, a skill execution scheduling strategy is generated, and the robot is driven to execute the skill; and meanwhile, when the robot executes skills, real-time closed-loop correction is performed on a skill execution scheduling strategy according to real-time sensing data of the robot. According to the invention, the execution effect of the foot-type robot in a complex long-time task is improved.

The invention discloses a quadruped robot reinforcement learning motion planning method and system based on depth vision, and relates to the technical field of quadruped robot motion planning. The method comprises the steps that a simulation environment is built, modeling is carried out on stair terrains with different gradients and robot kinetic parameters, and a stair terrain model comprises stair terrains with different difficulty differences; the robot model is loaded to move in the stair terrain model, the movement condition of the robot is tracked, the speed tracking effect is obtained, and the speed tracking effect is obtained by conducting strategy evaluation on the joint torque value output by the teacher strategy network and the corresponding movement linear speed in the movement process of the robot; the speed tracking effect is evaluated, and environment parameters in the stair terrain model are adaptively adjusted according to the evaluation result. Through the reinforcement learning framework based on the depth visual information, the irregular stair terrain can be effectively learned, and the quadruped robot can stably work in a complex environment in a self-adaptive mode.

The invention relates to a multi-modal data fusion-based multi-target tracking method for a legged robot. The method comprises the following steps of S1, data acquisition and preprocessing; s2, extracting data features; s3, multi-channel feature fusion extraction: based on the high-dimensional feature representation obtained in the S2, fusing the data features of the data of different sensor channels to obtain a multi-modal feature map, and processing the multi-modal feature map through a multi-task detection head to obtain a target detection result; s4, multi-target tracking: fusing data features of different sensor channels by using a feature sharing module, and outputting the fused data features to a multi-target tracking module; and the multi-target tracking module predicts the motion trail of the target in combination with the state of the target in the previous frame. According to the multi-modal data fusion-based multi-target tracking method of the legged robot, the accuracy and robustness of subsequent target detection and tracking can be improved, and continuous and stable identification and tracking of a sensitive target in a complex high-dynamic scene are realized.

The invention discloses a wheel-legged robot whole-body motion control system and method considering slippage steering, and relates to the technical field of wheel-legged machine control. The system comprises an input module used for receiving an expected motion track or a motion instruction from an upper layer planning system; the trajectory optimization module takes the motion trajectory output by the input module as a tracking target, so that the platform can be optimized in real time along an offline trajectory; the tracking control module is used for solving a joint torque through inverse dynamics by adopting a total rigid body dynamic model, realizing motion tracking through a whole body control WBC method, and solving a motion and contact force priority task by using a hierarchical optimization method; and the state estimation module is used for estimating all current state quantities of model predictive control MPC and information required by whole body control WBC through sensor information. By means of the system, high-dynamic stable motion control over the wheel-legged robot under the complex terrain can be achieved.

The invention discloses a bionic walking feature fused quadruped robot gait reinforcement learning training method, which comprises the steps of S1, bionic gait feature modeling for extracting key features from a motion mode of a natural quadruped animal and constructing a bionic template capable of directly guiding robot gait control; and S2, constructing a reinforcement learning training environment, and simulating diversified actual scenes by constructing a high-fidelity simulation platform. According to the method, key features are extracted from natural four-footed animal gaits, a bionic template library is constructed, and natural features such as nonlinear rhythm and dynamic symmetry of animal movement are fused into robot gait control, so that the problem of action mechanical stiffness caused by dependence on manual design of a track in a traditional method is effectively solved; movement of the robot is closer to natural biological gaits, impact generated when the robot interacts with the environment is reduced while movement energy consumption is reduced, and movement smoothness is improved.

The invention belongs to the technical field of legged robot control, and provides a quadruped robot robust motion control method and system based on joint learning, and the method comprises the steps: obtaining the body observation information and privileged observation information of a quadruped robot; extracting key features in the obtained privileged observation information by adopting a privileged encoder to obtain a first potential feature vector; obtaining a second potential feature vector matched with the first potential feature vector based on the obtained historical ontology observation information and an adaptive network; constructing a joint loss function according to the obtained first potential feature vector and the second potential feature vector, and performing strategy network updating training by taking the minimum joint loss function as a target; and robust motion control of the quadruped robot is completed according to the trained strategy network.

Disclosed in the present invention is a local terrain perception method for a legged robot, comprising the following steps: S1, providing on a legged robot at least one LiDAR, acquiring extrinsic parameters of the LiDAR with respect to a legged robot body coordinate system, and transforming to the legged robot body coordinate system point clouds scanned by the LiDAR; S2, removing motion distortion from the point clouds, transforming same to a legged robot gravity coordinate system, and keeping the point clouds within a set range; S3, reducing the dimensionality of the point clouds to a height information matrix; S4, performing hole filling on the basis of the height information matrix; S5, on the basis of the height information matrix, removing the effect of a leg point cloud of the legged robot on the height information matrix; and S6, analyzing the processed height information matrix, and computing a slope information matrix and an obstacle information matrix for the legged robot to plan footholds and avoid obstacles. The present invention has a low degree of dependency on high-precision odometers, and can directly observe terrains beneath and around legged robot bodies, allowing for more accurate perception.

The invention discloses a quadruped robot driving leg structure which comprises a first driving unit, a hip joint assembly, a thigh assembly and a shank assembly, the hip joint assembly is transversely arranged on a driving output shaft of the first driving unit, and the hip joint assembly comprises a hip joint shell, a second driving unit and a second driving connecting plate; the second driving connecting plate is arranged on the outer wall of the hip joint shell, a driving output shaft of the first driving unit is connected to one side of the front face of the second driving connecting plate, the first driving unit is used for driving the second driving unit to rotate within a specified angle range, and the thigh assembly is downwards obliquely provided with the driving output shaft of the second driving unit. The lower end of the thigh assembly is movably connected with the upper end of the shank assembly, and the lower end of the shank assembly is provided with a foot end. Radial impact borne by the driving motor during operation can be reduced, the accuracy and reliability of leg movement can be effectively ensured, and the effective loading capacity of the robot is further improved.

The invention discloses a quadruped vehicle sensing and decision-making system based on reinforcement learning. The quadruped vehicle sensing and decision-making system comprises a multi-mode sensing module, a dynamic fusion module, a reinforcement learning decision-making module and an execution module. The multi-modal sensing module is used for collecting multi-modal environment information including visual, tactile and inertial data; the dynamic fusion module carries out dynamic fusion on the multi-modal data by using a Transform-based feature extraction and time sequence alignment method to generate a unified environment representation; the reinforcement learning decision-making module is used for analyzing the multi-modal sensing result on the basis of a Proximal Policy Optimization (PPO) algorithm, and generating an optimized path planning and dynamic obstacle avoidance instruction; and the execution module generates a gait and motion control command according to the decision instruction to ensure the autonomous navigation and environment adaptability of the quadruped robot. According to the system, the sensing precision and the decision-making efficiency of the quadruped robot in a complex scene are remarkably improved, and the system has a wide application prospect.

The invention discloses an in-tunnel unmanned aerial vehicle and robot dog combined positioning method and system based on prism reference points, and belongs to the technical field of information processing. The method comprises the following steps: constructing a tunnel prism reference point array, acquiring an initial reflection signal, and preprocessing the initial reflection signal to obtain a processed signal set; determining a signal dynamic threshold, and obtaining a reference point set based on the signal dynamic threshold; acquiring reflected signal data of the unmanned aerial vehicle and the robot dog through the reference point set, and extracting reflected signal features; determining initial position distribution of the unmanned aerial vehicle and the robot dog based on the reflected signal features; obtaining dynamic reflection data of the unmanned aerial vehicle and the robot dog on the basis of the initial position distribution, and processing the dynamic reflection data to obtain a smooth position sequence; calculating a relative motion trend based on the smooth position sequence and motion difference characteristics of the unmanned aerial vehicle and the robot dog; the position consistency of the unmanned aerial vehicle and the robot dog is determined based on the relative movement trend; and determining real-time positioning coordinates of the unmanned aerial vehicle and the robot dog based on the position consistency.

Provided are a planning and control method for a legged robot, an apparatus, a robot, and a storage medium. The method includes: determining, based on the movement state and the swing parameter of each leg at the next moment, a foot-end reference state of each leg, and planning, based on a foot-end dynamic model and a discrete collision model, an impact-aware swing leg trajectory for a leg that starts to swing at the next moment; and performing, based on the movement state, the reference state sequence, and the foot-end reference state, whole-body control on the legged robot, and performing, based on a whole-body dynamic model and the discrete collision model, impact-aware whole-body control for a leg that is a supporting leg in planning but does not touch the ground.

The invention discloses a multi-robot area coverage path planning method based on a wheel-foot type platform, and belongs to the technical field of intelligent robot cooperative control. According to the method, the characteristic that the wheel-foot robot can adapt to various terrains is combined, the environment is divided into a plurality of sub-regions through a multi-robot task pre-distribution method introducing terrain parameters, each robot is responsible for covering one sub-region, and the calculated amount and the search space of a path planning algorithm are reduced; in the sub-regions, a path covering all task regions can be constructed from any free grid through an improved STC algorithm, and 100% path coverage is realized under the condition of a grid map; in the actual task stage, the change of the actual terrain and the actual state is fed back in real time through the sensing ability of the wheel-foot robot to the environment, and task allocation and path planning are adjusted in real time.

The invention discloses a double-foot wheel-legged robot pose control method and device based on hierarchical cooperative control, a medium and a product, and relates to the field of robot control, and the method comprises the steps that a hierarchical cooperative control framework is constructed; the layered cooperative control framework comprises a balance controller and a whole body attitude controller; the balance controller determines a reference wheel joint driving torque based on a simplified inverted pendulum dynamic model according to the pose information of the upper mass center of the biped wheel-leg robot; the whole-body attitude controller performs optimization control on all joints of the biped wheel-leg robot by adopting an incremental model prediction control algorithm combined with a reference wheel joint driving moment based on a whole-body dynamic model; state information of the biped wheel-legged robot is obtained; according to the state information, a driving torque instruction of each joint is obtained based on a hierarchical cooperative control framework; and driving the double-foot wheel-leg robot to move according to the driving torque instruction of each joint. The precision, robustness and real-time performance of robot motion control can be improved.

The invention provides an external force interference resisting gait control method of a hydraulic quadruped robot. The external force interference resisting gait control method comprises the steps that firstly, a stable walking controller of the quadruped robot is built; comprising an impedance controller of a swing phase and a virtual force controller of a support phase; secondly, when the quadruped robot walks, the foot end contact force of the robot is processed through an impedance controller in the swing phase, the position compensation amount is output, the position output amount of a single position control closed loop is corrected through the compensation amount, and impedance closed loop control is conducted; the supporting phase adopts a virtual force control method through the virtual force controller, and the posture of the quadruped robot is adjusted from crossing gait planning and posture angle adjustment so as to be switched to a normal gait. Thirdly, the swing phase is controlled in a closed-loop mode according to impedance, and the supporting phase conducts stable walking of the robot according to the adjusted normal gaits. The stability of the hydraulic quadruped robot when the hydraulic quadruped robot is disturbed by the outside is improved, and the risks of rollover and toppling are reduced; the energy loss is reduced by reducing the impact force at the foot end and optimizing the gait.

The present invention discloses a deep reinforcement learning quadruped robot motion control method and system based on constraint reward, establishes a simulation training environment for deep reinforcement learning of a quadruped robot; determines the reward function, domain randomization parameter and cost constraint function of the simulation training environment; based on robot information and first simulation environment information, trains the initial policy network model in the simulation training environment through the reward function and the cost constraint function to obtain a trained policy network model; establishes an inference test environment, and deploys the trained policy network model to the inference test environment for model inference test tuning to obtain a target policy network model; deploys the target policy network model to the quadruped robot to control the motion of the quadruped robot. The present invention reduces the difference between the simulation training environment and the real environment, so that the quadruped robot is stably controlled by the target policy network model in the real environment.

The invention relates to a hexapod robot leg impedance control method and device, equipment and a medium, and relates to the technical field of automation control, and the method comprises the steps: training a primary function neural network through domain adversarial invariant learning based on a force-displacement compensation data set; a robot contact force deviation value is calculated, the contact force deviation value is input into the primary function neural network to extract environment invariant features, a Kalman filter is utilized to estimate an adaptive coefficient on line according to the environment invariant features, and a displacement compensation amount is calculated in combination with the environment invariant features and the adaptive coefficient; and the displacement compensation amount is injected into a robot impedance control loop, and leg movement of the hexapod robot is adjusted. According to the method, the environment invariant features are extracted, so that the method can better adapt to various complex terrains, the sensitivity to environment parameters is reduced, and the urgent requirements of the hexapod robot on high robustness, high precision and rapid self-adaptive contact control in actual operation are met.

The invention discloses an unstructured environment-oriented wheel-legged robot motion control method, system and device, a storage medium and computer equipment, and the control method comprises the steps: constructing a wheel-legged robot simulation model, building a learning training scene of a wheel-legged robot, and carrying out the training through a reinforcement learning algorithm, so as to obtain a nonlinear controller model; converting the nonlinear controller model into a nonlinear controller which can be deployed and operated on an actual physical prototype platform; acquiring current motion state data of the wheel-legged robot, and inputting the current motion state data into a nonlinear controller to obtain an expected joint position and an expected wheel speed of the wheel-legged robot; and the expected joint position and the expected wheel speed are input into a low-level controller, and a joint torque used for controlling the wheel-legged robot to execute a control task is output. According to the method, the development period of the wheel-legged robot controller is greatly shortened, and the robustness, the dynamics and the flexibility of the wheel-legged robot moving in the unstructured environment are also improved.

The invention provides an interaction control method and device of a foot type robot, equipment and a medium. The interaction control method comprises the following steps: determining local accumulated point cloud data according to pose transformation information and registered point cloud data; performing passable judgment on the foot-type robot through the local accumulated point cloud data to obtain a local passable region; according to the local accumulated point cloud data, low obstacle point cloud data and impassable obstacle point cloud data of the foot-type robot in a local passable region are determined; clustering the low obstacle point cloud data to obtain a low obstacle representation shape; clustering the impassable obstacle point cloud data to obtain an impassable obstacle representation shape; and rendering the low obstacle representation shape and the impassable obstacle representation shape into a local passable region displayed in a first display interface at the handle end of the foot robot in real time. Therefore, the user experience is effectively improved.

The invention relates to the technical field of foot type robot control, and provides a robot ground touching force estimation method and system based on a high-order finite time observer, and the method comprises the steps: building a foot type robot leg dynamic model; joint positions, speeds and joint acceleration driving items are selected as state variables, and a leg state space model of the legged robot is established; according to the established state space model, step-by-step iteration of an estimated value of a state variable is utilized, a power form of a nonlinear sign function and a set gain parameter matrix are combined, a high-order finite time observer is established in a recursive mode, and estimation of the ground touching force of the foot end of the foot type robot is obtained. A high-order finite time observer is established in combination with a nonlinear sign function and a designed parameter matrix, so that a ground touching force estimation error can be converged to a certain boundary within finite time, and the problems that a traditional extended state observer is low in ground touching force estimation precision and only can estimate slowly-changing ground touching force are effectively solved.

In a method for planning and controlling the balance and locomotion of a humanoid bipedal robot, with at least one control and planning device that performs real-time planning and control of DCM trajectories in 3D using a model of the three-dimensional divergent motion component (3D-DCM), The divergent motion component (3D-DCM) model is designed to include equations of motion for the flight phase.