393 results about "Legged robot" patented technology

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.

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.

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 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 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 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.

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 quadruped robot weld crack detection method based on knowledge distillation and transfer learning. The quadruped robot weld crack detection method comprises the steps that weld images are collected to construct a weld image data set; constructing and training a teacher model at the cloud based on an RT-DETR architecture; constructing a lightweight student model at the edge end based on the teacher model; introducing a channel projection operator, and respectively mapping the multi-scale intermediate features and the multi-scale fusion intermediate features extracted by the teacher model to the same dimension as the multi-scale features and the multi-scale fusion output feature map extracted by the lightweight student model; establishing feature alignment loss and comprehensive distillation loss of the student model based on the distance between the features, and training a lightweight student model in combination with student detection loss; designing transfer learning based on feature alignment and adversarial training for the student model; and carrying out model deployment and increment optimization. According to the method, lightweight deployment and continuous optimization of the high-precision detection model are realized, and the detection reliability of the quadruped robot in a complex industrial environment is improved.

The invention discloses a robust self-adaptive multi-skill learning method for a quadruped robot based on key frame guidance. The robust self-adaptive multi-skill learning method comprises the following steps: determining a target key frame set of multi-skill learning; in a flat terrain environment, probability sampling is carried out on the key frames according to skill task values to generate skill instructions, the quadruped robot is controlled to execute actions, and training tracks are collected and stored in a strategy learning experience playback pool and a self-simulation learning experience playback pool respectively. Training the strategy network and the value function network through strategy learning circulation; a self-mimicking learning discriminator network is trained through a self-mimicking learning loop to extract a high quality trajectory. And after the flat terrain training is completed, entering a complex terrain environment, and carrying out adaptive training under the guidance of the trained strategy network and the self-simulation learning discriminator network so as to realize the migration of the skill primitives to the complex environment. According to the method, multi-skill learning and terrain adaptive capacity can be effectively considered, and the robustness, flexibility and multi-task execution performance of the quadruped robot are improved.

The invention discloses a quadruped robot navigation method and system based on three-dimensional environment mapping and navigation, and belongs to the technical field of mobile robot navigation. The method comprises the following steps: acquiring a non-repeated scanning three-dimensional point cloud and a high-frequency pose to carry out pre-integration distortion removal; extracting features for joint optimization, and constructing a global three-dimensional point cloud topological map; stripping ground point cloud by using a double-track map mapping mechanism, and performing dimensionality reduction to generate a two-dimensional multi-dimensional cost navigation map; column cycle matching is carried out by using two-dimensional polar coordinate context features in a positioning degradation environment, and continuous poses are forcibly corrected; and forward sampling and evaluation are carried out based on the navigation map and the multi-dimensional trajectory evaluation cost function model, and closed-loop motion of the robot is controlled. According to the method, the problems of high computing power consumption of high-dimensional space calculation and easy failure of positioning in a degraded environment are effectively solved, and the safety navigation capability of the quadruped robot in a complex environment is remarkably improved.

The invention provides a global path planning method and system for a wheel-foot robot in multiple motion modes, and relates to the technical field of robot path planning, and the method comprises the steps: obtaining multi-source data of the wheel-foot robot; calculating actual motion cost values of the wheel-foot robot in various motion modes in different terrains; constructing a deep learning network model; outputting a motion cost prediction value through a deep learning network model; constructing a loss function, and optimizing the deep learning network model by taking the minimum function value of the loss function as a target; sampling the global map through a path planning algorithm to generate an undirected graph; through the optimized deep learning network model, motion cost prediction values of all sampling points in the undirected graph in different motion modes are output; determining a plurality of middle sampling points through a heuristic search algorithm; and connecting the initial sampling point, each middle sampling point and the target sampling point in sequence to obtain a global path of the wheel-foot robot in various motion modes.

The invention discloses a quadruped robot advancing path selection method and system based on Kalman filtering, and belongs to the technical field of robot movement control. The method is used for solving the technical problems that sudden change of foot end contact force of a quadruped robot or terrain slope change can cause model mismatch, and then filtering divergence or estimation deviation is caused. A kinematic model is corrected in real time through a slope angle, so that system state prediction is more fit with an actual terrain, noise covariance is dynamically enhanced when contact stiffness is suddenly changed, and the situation that small-probability large sudden change is excessively suppressed by filtering is avoided; the method can dynamically adapt to changes of terrain gradient and contact rigidity, according to the output filtering optimal state estimation and in combination with a preset foot end landing point stability evaluation function, an advancing landing point meeting force-pose constraint is generated, the robot is driven to execute, and the quadruped robot can autonomously select a stable landing point in an unknown complex terrain.

The application discloses a kind of application four-legged robot's inspection method and system, its method when receiving current time's to be analyzed inspection data, obtain inspection database and the equipment parameter of four-legged robot, to be analyzed with the standard inspection data of inspection database preset one by one matching inspection data, if not matched standard inspection data, then the inspection image in to-be-analyzed inspection data is input into the geometric analytic model that is well trained in advance, generate obstacle data, judge whether the passing height value in obstacle data is greater than the height threshold value of preset, if passing height value is greater than height threshold value, then according to to-be-analyzed inspection data and equipment parameter construction passing analysis function, generate passing analysis value, according to the comparison result of passing analysis value and preset passing threshold, adjust the inspection path of four-legged robot. Solve the existing in the process of inspection, due to the influence of maintenance and construction and other human activities, leading to the technical problem of low accuracy of four-legged robot to obtain inspection information.

The invention relates to the technical field of robot control, and discloses a wheeled humanoid robot movement and operation integrated control strategy, which is characterized in that a twin hierarchical control framework is constructed, real-time data is acquired through a sensing layer, and a twin data layer is used for constructing a double-arm kinematics model fused with chassis incomplete constraint; the task planning layer dynamically allocates master-slave roles of the two arms and optimizes a time sequence scheme; the chassis and double-arm coordination layer realizes balance adjustment through dynamic sliding mode control driven by twin correction, and the control layer generates a force-position coordination instruction to drive the execution layer to work; closed-loop iteration is formed through comparison and calibration of virtual and real data of the twin data layer. According to the method, the double-arm cooperation precision and the anti-interference capability are improved, the system robustness is enhanced, the operation continuity can be guaranteed when the single arm fails, and the comprehensive performance of the cooperation operation is optimized.

The invention relates to the technical field of robots, in particular to a visual perception-based real-time abnormal event recognition method for a quadruped robot, which comprises the following steps of: acquiring body motion data and an environment image sequence of the robot in real time through an inertial measurement unit and a visual sensor; predicting and generating a short-term expected movement track based on the data and the control instruction; meanwhile, estimating an actual motion track of the robot by utilizing a visual odometer technology; fusing the deviation value of the expected trajectory and the actual trajectory with the optical flow statistical features extracted from the dense optical flow field to form a multi-modal fusion feature vector; and calculating a real-time abnormal confidence coefficient score by adopting a pre-trained long-short-term memory network model. Multi-mode sensing information is effectively fused, limitation of a single sensor is overcome, high-precision and real-time recognition and classification of abnormities such as foot slipping, collision and terrain abrupt change are achieved, corresponding emergency control strategies can be triggered, and autonomy and reliability of the quadruped robot in a complex environment are improved.

The application discloses a quadruped robot control method and system based on a Transformer reinforcement learning architecture. The method is related to the technical field of robot intelligent control. The method realizes real-time acquisition of the body state and environmental information of a quadruped robot through a multi-modal sensor. The information at the current and historical time is constructed into a time sequence state sequence. Feature embedding and time position coding are performed on the sequence to form an input token sequence. The token sequence is input into a policy network pre-trained through reinforcement learning, with a Transformer encoder as the core. The network utilizes a self-attention mechanism to capture long-range global dependency relationships between states and outputs continuous action instructions at the joint level. Finally, the corresponding movements of the robot are executed through a bottom controller to drive each joint of the robot. The system comprises corresponding sensing, computing and executing modules. The application effectively solves the gradient disappearance and memory decay problems of traditional recurrent neural networks in long sequence decision-making, and significantly improves the motion intelligence, robustness and energy efficiency of the quadruped robot in complex and unstructured environments.

The application provides a terrain recognition method, device and equipment for a legged robot and a medium. The method comprises the following steps: acquiring pose data collected by a body sensor of a target legged robot, and calculating state parameters of the target legged robot; inputting the pose data and the state parameters into a foot-ground contact force model to determine three-dimensional foot-ground contact force; generating a contact mask sequence representing a foot-end ground contact state of the target legged robot based on the three-dimensional foot-ground contact force, and segmenting the pose data to obtain a segmented pose data set; inputting the pose data set into a pre-trained terrain classification model to determine a confidence vector of the current ground under multiple terrain attributes, and determining a terrain category of the current ground according to multiple confidence vectors. Through the method and device, the autonomous adaptation capability and environmental perception reliability of the legged robot in complex terrain are significantly improved.

The present application relates to a kind of four-legged robot single leg sliding mode control method based on interference observer, belong to robot control technical field.First, the trajectory of four-legged robot single leg in swing phase is established;Then, the kinematic model of four-legged robot single leg is established, to realize the conversion of foot target position and corresponding joint angle;On this basis, non-singular terminal sliding mode controller is designed to deal with the dynamics control under the condition of no interference;Finally, combined with sliding mode controller and interference estimation, the final composite control strategy is designed, the stable tracking of robot joint angle is realized, so as to improve the anti-interference performance of single leg system.The present application utilizes the combination of finite time interference observer and non-singular terminal sliding mode controller, designs a kind of composite controller, to improve the foot trajectory tracking accuracy and system robustness of four-legged robot, eliminate the uncertainty caused by external disturbance and modeling error suffered by robot.

1. The name of the design product: four-legged robot dog. 2. The use of the design product: used for replacing people to perform tasks such as patrol, exploration, monitoring, and dangerous disposal in specific environments. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: Design 1 perspective view 1. 5. Design 1 is designated as the basic design.

The application belongs to the technical field of robots, and discloses a three-dimensional force sensing-based detection and balance control method and system for a legged robot. The method comprises the following steps: determining the number of the leg of the multi-legged robot that slips according to a three-dimensional force signal of the multi-legged robot in contact with the ground; and adjusting the multi-legged robot in the following manner when slipping occurs: (1) adjusting the height of the center of mass of the multi-legged robot; (2) for the leg that slips, actively adjusting the joint stiffness and joint damping of the leg to relieve instantaneous impact; applying foot end acceleration and adjusting normal support force to suppress slipping; and adjusting the target foot landing point at the next time. Through the application, the balance of the robot can be quickly restored after slipping occurs, the risk of instability is reduced, and the stability of the robot in a complex environment is ensured.

The invention relates to the technical field of robot control, in particular to a quadruped robot adaptive path optimization and obstacle avoidance control method and system combined with reinforcement learning and a computer readable storage medium. By constructing a comprehensive state space, comprehensive environment perception is provided for reinforcement learning, and the adaptability of the robot in a complex environment is enhanced; a reinforcement learning method is adopted to realize autonomous path planning and decision making, so that the robot can learn online and continuously optimize the strategy; through multi-factor reward function design, multi-objective optimization of path efficiency, safety and energy consumption is realized; and an online adaptive optimization mechanism is introduced, so that the robot can adapt to environmental changes in real time, and the task execution efficiency is improved.

The invention relates to a foot-type robot cooperative control method based on environment and cluster interaction force, belongs to the technical field of foot-type robot control, and solves one of the problems that an existing foot-type robot cluster is poor in complex terrain adaptability, unstable in dynamic motion control and weak in multi-robot cluster cooperative capability. The method comprises the following steps: obtaining individual state information and neighbor state information of each foot type robot in a cluster at the current moment, and constructing a state space of each foot type robot; based on the state space of each foot type robot, the trained control model of each foot type robot is used to generate each joint torque instruction of each foot type robot at the next moment so as to control each foot type robot to move; wherein the foot type robot control model is a strategy network obtained through reinforcement learning training; during training, a global state space is constructed by aggregating the state spaces of all the foot type robots in the cluster, and the value of action strategies of all the foot type robots in the cluster is evaluated and optimized.

The utility model relates to four -legged robot technical field, concretely is a kind of handle structure and carrying mechanism for four -legged robot carrying, including rubber coating, flexible band inlayed in rubber coating, and with the quick-release type fixing assembly and four groups of leg unit connected to leg unit, handle unit, four groups of leg unit are symmetrically arranged in the head and tail two sides of four -legged robot by pivot. Handle structure and carrying mechanism of the application, in the carrying process, handle can realize load self-adapting adjustment and reduce the structural volume of four -legged robot carrying state with four -legged robot leg structure form, convenient for operator to carry, and realize long-distance transfer.