583 results about "Legged robot" patented technology

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 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 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.

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 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 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 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.

A system in accordance with at least some embodiments of the present technology includes a robot and a dock. The robot includes a body and a plurality of legs connected to the body through which the robot is configured to ambulate. The robot further includes a hanger carried by the body and a charge-receiving electrode at the hanger. The dock includes a hook, a charge-dispensing electrode at the hook, and a guide that urges the hanger into alignment with the hook. The system is transitionable between an undocked state and a docked state. In the undocked state, the robot and the dock are spaced apart from one another. In the docked state, the hanger is received at the hook, the dock supports at least a portion of a weight of the robot via the hook, and the charge-receiving electrode is electrically connected to the charge-dispensing electrode.

The invention relates to a quadruped robot parkour navigation method and system based on multi-modal feature fusion, and the method comprises the steps: constructing a multi-modal parkour topographic data set which comprises a depth image, body feeling information and a control instruction; encoding the depth image sequence and the body sensing sequence of the historical time step to obtain space-time fusion features; encoding the control instruction and the corresponding target position vector to obtain a historical decision feature; encoding a depth image collected at the current moment to obtain fine spatial features; fusing the space-time fusion feature, the historical decision feature and the fine space feature to form a conditional context; and based on the diffusion strategy model, by taking the conditional context as a condition, carrying out de-noising processing, and generating a navigation control instruction so as to complete a parkour navigation task. Compared with the prior art, the method has the advantages that the navigation success rate is high, path planning is diversified, complex 3D terrains can be effectively processed, and the problem that high-level planning and bottom-level motion control are disjointed is solved.

The invention discloses a decoupling motion control method and system for a four-footed mobile operation robot considering the acting force of a mechanical arm, and belongs to the field of motion control of foot type mobile operation robots. An expected motion track input by a user can be tracked by performing planning through linear model prediction control MPC; on the basis of the tracked motion trail, mechanical arm joint control torque is calculated through a mechanical arm dynamic model and PD feedback; through nonlinear model predictive control NMPC planning, a quadruped robot whole-body motion trail of mechanical arm acting force obtained through calculation according to a mechanical arm dynamic model is considered, and an expected speed trail and an expected force trail input by a tracking user are obtained; and according to an expected speed trajectory and an expected force trajectory input by a tracking user, a whole body controller WBC based on hierarchical quadratic programming calculates a joint driving torque for tracking the expected trajectory according to task priorities. According to the method, the acting force / torque of the mechanical arm to the robot body is considered during motion control of the quadruped robot, and decoupling control over the mechanical arm and the quadruped robot in the quadruped mobile operation robot is achieved.

The invention discloses a quadruped robot control method and system based on a Transform reinforcement learning architecture. According to the method, the body state and environment information of the quadruped robot are obtained in real time through a multi-mode sensor; constructing the information of the current moment and the historical moment into a time sequence state sequence; performing feature embedding and time position coding on the sequence to form an input token sequence; inputting the token sequence into a strategy network which takes a Transform encoder as a core and is pre-trained through reinforcement learning; the network captures a long-range global dependency relationship between states by using a self-attention mechanism, and outputs a continuous action instruction of a joint level; and finally, each joint of the robot is driven by the bottom layer controller to execute corresponding motion. The system comprises corresponding sensing, calculating and executing modules. According to the method, the problems of gradient disappearance and memory attenuation of a traditional recurrent neural network in long sequence decision making are effectively solved, and the motion intelligence, robustness and energy efficiency of the quadruped robot in a complex and unstructured environment are remarkably improved.

The application discloses a novel mobile grabbing integrated bionic multi-legged robot, which comprises a front body provided with two leg parts and a rear body provided with four leg parts; the front body and the rear body are connected through a tension structure capable of being lifted and deformed; when a driving wire is driven to be tightened, the tension structure can be lifted and deformed upward; when the driving wire is loosened, the tension structure can be lowered and restored under the action of a reset spring; the front body and the tension structure move synchronously, so that the front body can be lifted and deformed or lowered and restored. When the front body is not lifted, the robot moves in a three-legged gait through six leg part mechanisms; when the front body is lifted, two leg parts of the front body serve as a mechanical arm, a steering wheel at a hip joint is used to realize a clamping function, the robot moves through four full leg parts of the rear body, and a four-legged gait is adopted, so that the robot can complete a carrying operation.

The invention discloses a wheel-legged robot for teenager education and a control system thereof.The wheel-legged robot comprises a vehicle-mounted frame and a driving assembly arranged on the outer wall of the vehicle-mounted frame, and the driving assembly comprises a joint motor, a driving arm, a torsion frame, a torsion spring, a first lifting rod, a second lifting rod, a driving wheel, a limiting shaft, a driving motor and a control assembly; the joint motors are fixedly arranged on the two sides of the outer wall of the vehicle-mounted frame. A four-connecting-rod and torsion spring balance mechanism is used as a core, smooth wheel-leg switching, easy obstacle crossing, LQR and reinforcement learning fusion, online self-adaption, more stable and more power-saving movement and voice, vision and gesture multi-mode interaction facing teenagers are achieved, complex tasks can be completed by speaking sentences and moving hands, a low-cost motor is developed by itself, a full-open-source design is adopted, and the robot is more convenient to use. Students can disassemble, assemble, expand and re-create, mechanical, electronic and programming knowledge is communicated in practical practice, and the education of department and creations is really extended from class to family and future.

The invention relates to a quadruped robot motion control method based on visual fault tolerance, and the method comprises the steps: building a simulation environment according to the motion control demands of a robot; defining a blind estimator and a visual estimator, organizing a training environment according to scene requirements, and training a switching strategy between the blind estimator and the visual estimator; determining the motion constraint of the robot based on a cMDP constraint Markov decision process, and solving a robot motion control strategy in combination with the trained blind estimator and visual estimator; and correspondingly controlling the motion state of the robot according to the robot motion control strategy. Compared with the prior art, the advantages of visual estimation and ontology perception are combined, the robot can move more flexibly and stably in the complex terrain, a switching strategy between estimators is designed, the visual fault-tolerant capability can be enhanced, the motion stability of the robot is guaranteed, in addition, an end-to-end one-stage training framework is adopted, and the training efficiency is improved. The training and deployment efficiency can be improved.

The utility model discloses a leg device of a foot type robot and the foot type robot. The leg device of the foot type robot comprises a robot leg and further comprises a driving motor arranged in the robot leg, the driving motor comprises a shell, a power cavity formed in the shell and a power assembly arranged in the power cavity, and a first containing cavity is formed in the end face of the tail of the shell; a motor controller electrically connected with the power assembly is arranged in the first containing cavity, and cooling fins are arranged on the motor controller and connected with the shell in a heat conduction mode. The utility model further discloses the foot type robot which adopts the leg device of the foot type robot. The motor controller has the advantage of solving the problem that the motor controller for installing the motor in the leg of the humanoid robot is easy to overheat. The cooling fins are connected with the shell in a heat conduction mode, heat generated by the motor controller can be rapidly transmitted to the shell and then dissipated to the surrounding environment through the shell, faults caused by overheating of the motor controller are avoided, and stable operation of the motor controller is guaranteed.

The invention discloses a chain type hybrid deep reinforcement learning quadruped robot control system. The system comprises a sensing module, a data processing module, a chain type hybrid reinforcement learning model module, a feedback module and the like. The invention further discloses a control method of the chain type hybrid deep reinforcement learning quadruped robot control system, and the method comprises the steps: S1, collecting bionic motion data, robot state data and environment data, and training based on an attention mechanism space-time diagram neural network and antagonistic imitation learning to obtain a reference gait model containing motion prior; and S2, constructing a three-level parallel simulation environment, setting a progressive course target, training the progressive course target by adopting an improved PPO algorithm, and optimizing the gait stability, the movement efficiency and the environmental adaptability. By means of high-precision data acquisition with the sampling frequency larger than or equal to 240 Hz and antagonistic imitation learning, motion priori knowledge is introduced, the training period can be shortened by 50% or above, abnormal gaits are avoided, and the training process is controllable.

The invention provides a design method of a two-wheeled wheel-legged robot, which relates to the field of wheeled robots, and comprises the following steps: determining a machine body of the two-wheeled wheel-legged robot, adopting a leg four-bar mechanism as a leg configuration, and taking the leg four-bar mechanism and the machine body as a five-bar mechanism together, a universal leg rod length parameter calculation method is deduced based on a planar five-connecting-rod model, key geometric parameters are determined, and robot structure design is completed; establishing a wheel type motion dynamical model and a leg type jump phase motion dynamical model, deducing a universal motor model selection method, calculating driving wheel and joint motor parameters, and completing wheel train design and whole machine motion simulation; carrying out finite element analysis and lightweight design on the key parts; and finally, an LQR control strategy is adopted, and cooperative control verification is completed through analysis and simulation software. Wheeled efficient movement and leg type terrain adaptive capacity are fused, limitation of a single movement mode is broken through, and the environment adaptive boundary of the robot is expanded.

The utility model discloses a high-adaptability walking mechanism of a wheel-foot type robot, which relates to the technical field of mobile robots and comprises a base mechanism, a transmission mechanism is arranged on the top surface of the base mechanism, a connecting rod mechanism is arranged on the side surface of the base mechanism, and a damping mechanism is arranged at the bottom end of the connecting rod mechanism. The seat mechanism, the transmission mechanism and the connecting rod mechanism are arranged and matched with one another, so that the device can easily cross an uneven road surface, a transmission motor only needs to be started, an output shaft of the transmission motor drives a second transmission wheel to rotate, and then a transmission belt in transmission connection with the outer wall of the second transmission wheel is driven; and then a first transmission wheel in transmission connection with a transmission belt rotates, then a rotating rod fixedly connected with the first transmission wheel rotates, then a small sliding block on the inner wall of a first connecting rod slides in a sliding groove formed in the inner wall of the sliding block, meanwhile, the sliding block is driven to slide in the sliding groove, and then the whole device can move on the uneven road surface.

The invention discloses a map construction and path planning method of a four-footed robot dog, and belongs to the technical field of intelligent navigation of four-footed robot dogs. The method comprises the following steps: acquiring environment original data through a preset multi-dimensional sensor, and determining a terrain structure, a terrain material, a terrain bearing capacity and a space dynamic obstacle probability of an environment according to the environment original data; constructing a multi-dimensional topological map according to the terrain structure, the terrain material, the terrain bearing capacity and the space dynamic obstacle probability; the method comprises the following steps: acquiring kinematics parameters of a four-footed robot dog, generating a motion path according to the kinematics parameters and a multi-dimensional topological map, and controlling a moving track of the four-footed robot dog according to the motion path, so that parameters such as terrain materials, terrain bearing capacity and space dynamic obstacle probability are supplemented in dimensions of a traditional grid map; the dimension information of the grid map is improved, and the accurate three-dimensional grid map is provided for the robot, so that the task execution capability of the robot in a dynamic complex environment is ensured.

A legged robot control method performed by a legged robot includes obtaining proprioceptive information and external perception information, the proprioceptive information being configured for characterizing a motion state of the legged robot, and the external perception information being configured for characterizing environment information around the legged robot; inputting the proprioceptive information and the external perception information into a deep neural network, to obtain a first predicted residual outputted by the deep neural network, the first predicted residual being configured for correcting a trajectory generation parameter of a foot trajectory generator; adjusting the trajectory generation parameter of the foot trajectory generator based on the first predicted residual; and controlling the motion state of the legged robot based on a joint motion parameter outputted by the foot trajectory generator after the trajectory generation parameter is adjusted.

Apparatus and methods for mitigating slip conditions and estimating ground friction for a robot having a plurality of feet are provided. In one aspect, a method includes estimating a coefficient of friction for a ground surface supporting the legged robot based on sensor data, odometry data, and a terrain map of an environment. The sensor data includes a set of joint angles and a set of joint torques for a set of joints of the legged robot, and the odometry data indicates a location of the legged robot in the environment. One of the plurality of feet of the robot applies a force on the ground surface based on the estimated coefficient of friction.

The invention discloses a complex soft obstacle environment-oriented quadruped robot crossing advancing method, which comprises the following steps of S1, actively interacting with the terrain under the condition of no prior environment perception, and obtaining terrain mechanical property information; s2, based on the obtained terrain mechanical property information, deeply analyzing the terrain, evaluating the risk of the robot advancing in the terrain, and adjusting the gait according to the risk; and S3, comprehensively evaluating the traveling safety of the robot on the current terrain, and deciding whether to pass safely or take recovery measures according to the risk level. The method gets rid of the dependence of the traditional technology on geometric contour perception of an optical sensor, and can directly obtain key mechanical parameters of subsidence, shearing and the like of soft terrains such as sludge and thick snow on line; meanwhile, in combination with a parameter estimation method for simplifying a Bekker model, the problem of perception deviation caused by large mechanical property difference of visual similar areas is solved, and reliable environmental data support is provided for subsequent planning and control.

The invention relates to the technical field of robots, and provides a walking control method, device and equipment for a foot type robot and a medium. Multi-source ground sensing information of the advancing direction of the foot type robot is obtained; determining the current ground type of the advancing direction according to the multi-source ground sensing information; generating a target gait parameter matched with the current ground type through a gait parameter mapping model corresponding to the current ground type; the target gait parameters are converted into joint control instructions, and joints of the foot type robot are driven to move according to the joint control instructions. The walking control scheme of the foot-type robot is suitable for complex terrains such as mountainous regions, ruins, gullies and muddy zones, the adaptive capacity and walking stability of the foot-type robot in the different complex terrains can be improved, and the foot-type robot can be better applied to on-site investigation and loss assessment of insurance claim settlement in the field of financial science and technology; and life detection, rescue and other tasks in the field of medical science and technology.

The invention relates to a control method for dynamically optimizing contact force distribution of a foot-type robot, belongs to the technical field of foot-type robots, and solves the problem that the foot end is easy to sink when the foot-type robot advances in a soft terrain in the prior art. The method comprises the following steps: acquiring real-time ground counter-acting force of each foot of the foot robot in a current gait cycle support phase and joint control information of each foot in a previous gait cycle support phase; based on the joint control information of each foot in the previous gait cycle support phase and the standard ground reaction force time curve, obtaining an expected ground reaction force time curve of each foot in the current gait cycle support phase; based on the expected ground reaction force time curve of each foot in the current gait cycle supporting phase and the corresponding real-time ground reaction force, a PID controller is used for obtaining the control torque of each foot at each moment, the control torque is applied to each joint motor of each foot, and the contact force distribution of each foot is optimized. The walking stability of the foot type robot in the unstructured terrain is achieved.

The invention relates to a transfer learning motion control application method and device for a legged robot and a storage medium, and the method comprises the steps: training a source domain legged robot model through employing a near-end strategy optimization algorithm, obtaining a walking strategy, and extracting a strategy network of the source domain legged robot; the strategy network parameters of the source domain foot type robot are migrated to the target domain foot type robot model, when the target domain foot type robot model is trained, part of layers of the strategy network of the target domain foot type robot are frozen, parameters of the unfrozen layers are trained, and therefore the motion control strategy of the target domain foot type robot is obtained. Strategy network parameters of a source domain foot type robot are directly applied to a target domain foot type robot model, unfrozen layer parameters are trained, and it is ensured that the model is matched with the target domain foot type robot while the training parameters and time are reduced. A near-end strategy optimization algorithm is adopted to train a source domain foot type robot model, a convergent strategy network is obtained, and reliable prior knowledge is provided for subsequent migration.

The invention belongs to the technical field of three-dimensional space scanning and imaging, and particularly relates to a slam reality three-dimensional space scanning and imaging system based on a quadruped robot, which comprises a self-adaptive attitude compensation module used for acquiring attitude information of the robot in real time, calculating an angle and a position needing to be adjusted of scanning equipment according to the attitude information, and sending the angle and the position to the scanning equipment; then the scanning equipment is adjusted in real time; and the multi-sensor fusion sensing module is used for firstly collecting environment and robot data through various sensors, and then calibrating, fusing and complementing the data collected by different sensors through a multi-sensor data fusion algorithm. According to the invention, the self-adaptive attitude compensation module can counteract the attitude offset caused by the motion of the robot in real time, so that the scanning equipment always keeps a stable position and attitude, and the precision of three-dimensional space scanning is obviously improved.