979 results about "Robot locomotion" patented technology

The invention discloses a robot obstacle avoidance method and system based on millimeter wave radar sparse point cloud, and relates to the technical field of obstacle avoidance recognition. A robot obstacle avoidance system based on millimeter wave radar sparse point cloud comprises a point cloud acquisition module, a negative obstacle identification module, a weak obstacle identification module, a point cluster identification module, a risk map module, a tentative verification module and an obstacle avoidance decision module. According to the invention, suspected obstacle point clusters are extracted based on a reflection intensity threshold and a spatial proximity relation in an enhanced point cloud, a theoretical parallax model of a real static obstacle is constructed under the constraint of a robot motion trajectory, and Doppler velocity distribution of each frame is combined with a static obstacle Doppler physical law for comparison. And classifying the point clusters which do not meet the multi-view geometric consistency or Doppler physical law, and distinguishing multipath false point clusters from dynamic point clusters.

The invention discloses a visual prediction method for space-time manifold and implicit state deduction of a robot, and relates to the technical field of robot space visual perception. The motion state and the visual image of the robot are collected; calculating carrier pose offset and generating a reverse compensation matrix; modeling an image target into a visual cone probability manifold, and extracting an explicit manifold observation vector; monitoring the confidence coefficient in real time through an observation quality evaluation network, and activating an implicit deduction mode during observation degradation; reading historical time sequence characteristics by using an improved Transform model, and performing prediction and deduction on a future nonlinear motion state of the target; performing coordinate correction and probability field decoding on a prediction result in combination with the reverse compensation matrix, and outputting a three-dimensional prediction trajectory and a spatial covariance ellipsoid; according to the method, the problem that traditional visual tracking fails under the conditions of violent shaking of a carrier and target shielding is solved, and continuous and foresight physical scale positioning and safety decision making of a robot on a dynamic target are achieved.

The invention relates to the technical field of industrial robot path planning, in particular to an industrial mobile robot path planning method based on an artificial potential field method and dynamic prediction. The method comprises the following steps: firstly, acquiring environment point cloud data, and distinguishing static and dynamic obstacles based on time sequence difference and Euclidean distance; kalman filtering is used to predict the future position of a dynamic obstacle, and a density clustering algorithm is used to simplify a static obstacle; combining a target point, a static obstacle set and a dynamic obstacle prediction result to construct an improved artificial potential field function to calculate gravitational force and repulsive force; the potential collision risk is judged based on the motion state of the robot, the dynamic obstacle repulsive force with the risk is brought into potential synthesis in advance, and the final resultant force for guiding motion is generated; and when it is detected that the system is caught in a local optimal or oscillation state, executing a virtual gravity point escape strategy until the system reaches a target point. According to the method, the fusion of static clustering, dynamic prediction and potential field improvement is realized, and the obstacle avoidance and path planning efficiency in a complex dynamic environment is improved.

The invention relates to the field of mobile robot motion planning, and discloses a quadruped robot motion planning method and system based on height self-adaption. The method comprises the following steps: constructing 3D and 2D local obstacle maps according to coordinate transformation, and calculating a grid vertical distance through ray casting to generate a 2.5 D local obstacle map; determining an initial control sampling space by combining speed, acceleration and height constraints; discretizing a sampling space and simulation time, deducing a control value sequence and recursively calculating a local trajectory; then calculating a shaft alignment bounding box in a directed bounding box of the robot, screening grid units in the shaft alignment bounding box, comparing the height of a machine body with the allowable height of grids, and rejecting collision trajectories to obtain a feasible trajectory set; and finally selecting an optimal track by the evaluation function, extracting a control value and sending the control value to hardware for execution. According to the method, the 2.5 D local obstacle map containing height information is constructed, the four-dimensional control sampling space is designed, and the robot height self-adaptive adjustment is realized in combination with the collision detection and evaluation function.

The invention relates to the technical field of industrial robot control, and discloses an industrial robot trajectory optimization control method based on an intelligent algorithm. The method comprises the steps that joint position information, tool center point coordinates and a motion time sequence when a robot executes multiple tasks are collected and stored in a track database; after cleaning and screening data, extracting a feature set containing a path point sequence, speed distribution and an acceleration contour; constructing an intelligent optimization algorithm model of a neural network structure, and training by using the feature set to learn a trajectory optimization strategy; analyzing a target position coordinate and a motion constraint condition of the current task to obtain an initial track parameter; and inputting the initial parameters into the trained model, outputting an optimized track sequence containing a path point list and a speed curve, and generating a control instruction to drive the robot to move. The method can adapt to different tasks, improves track rationality and motion stability, and fits industrial production practice.

The embodiment of the invention provides a robot control method and related equipment. The robot control method specifically comprises the steps that target action information used for controlling robot movement is generated based on a robot control task; performing security verification on the target action information based on the motion security constraint information of the robot; wherein the motion safety constraint information is used for constraining the motion range of the robot in the space from at least one spatial dimension; under the condition that the security verification is not passed, optimizing the target action information until the optimized target action information passes the security verification; and driving the robot to move by using the target action information passing the safety verification. According to the invention, unsafe robot behaviors can be reduced.

The invention discloses a decoupling control method for motion coupling between a track and a propeller, relates to the technical field of robot control, and realizes accurate and intelligent regulation and control of motion of a sea-land amphibious robot by constructing a complete closed-loop strategy from sensing, recognition, prediction to control. And the motion stability of the robot in a complex mixed environment is obviously improved. According to the method, the undesirable coupling effect between the crawler belt and the propeller can be anticipated and quantified in a prospective mode, suppression is conducted actively through self-adaptive control, impact, vibration and attitude instability caused by power conflicts in land and water transition or rugged terrain are effectively avoided, and stability and safety of a robot platform and loads of the robot platform are guaranteed. The energy utilization efficiency of the robot is greatly improved. And it is ensured that the two power systems work in the optimal cooperative state at any moment, and invalid energy internal consumption and redundant output are avoided.

The invention discloses a robot automatic navigation method and system based on depth vision fusion, and relates to the technical field of intelligent navigation, and the method comprises the steps: carrying out the registration of a point cloud subset of continuous frames through employing an ICP algorithm, distributing weights for a static obstacle and a dynamic obstacle, and carrying out the fusion, and obtaining a fused environment perception data set. The method comprises the following steps: updating a dynamic environment in real time, generating a dynamic environment map, carrying out trajectory tracking on moving targets by using binocular vision equipment, calculating a real-time motion trajectory of each moving target, obtaining an environment hotspot map, generating a global navigation path for a robot by using an A * algorithm, planning a detouring path at the periphery of a hotspot area, and outputting a navigation path sequence. According to the method, intelligent optimization of the motion path of the robot is realized through path planning and an obstacle avoidance strategy, the safety is ensured, the path efficiency is maximized, and unnecessary detour and pause time is reduced.

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 relates to the technical field of large-scale structure measurement, and discloses a robot laser vision three-dimensional scanning measurement system for a large-scale structural member. The system comprises a robot motion platform, a laser vision sensing module, an ambient light sensing module, a point cloud processing module and a motion control module, wherein the laser vision sensing module is arranged at the tail end of the robot motion platform. The ambient light sensing module collects ambient light intensity distribution data in real time, and the motion control module combines the data with three-dimensional model data of the large-scale structural member to be measured to generate a multi-view dynamic scanning path instruction. The robot motion platform executes an instruction to drive the laser vision sensing module to move, the module projects laser stripes to the surface of a to-be-measured structural member and synchronously collects an image, the point cloud processing module performs adaptive filtering processing on the image and then generates three-dimensional point cloud data, and the requirement for efficient and accurate measurement of a large structural member in a complex industrial environment can be met.

The invention provides a robot motion path planning method and device based on machine learning, and relates to the technical field of robot path planning, and the method comprises the steps: obtaining and storing the position information of an obstacle and a target point in an environment; constructing an artificial potential field according to the current position of the robot, the speed of the robot, the target point position, the target speed, the obstacle information and the motion state of the robot; the artificial potential field is optimized through a variable neighborhood search algorithm, and a reinforcement learning strategy is introduced into the variable neighborhood search algorithm; nodes on the basic path generated by the artificial potential field method serve as initial starting points of ants in the ant colony algorithm, and a final path is obtained through multi-round iterative optimization through an ant release pheromone mechanism, a transition probability selection mechanism and a pheromone volatilization updating mechanism; and evaluating the optimized path and dynamically adjusting the path. The robot motion path planning method can cope with complex environment changes, and effectively improves the safety and efficiency of path planning.

The invention discloses a humanoid robot walking and running unified control method and system based on a biological rhythm, and belongs to the technical field of humanoid robots. Capturing human walking and running action data, and extracting rhythm features from frequency and phase time domains; constructing a rhythm generator based on the rhythm features extracted in the step 1; according to a strategy gradient method based on a constraint reinforcement learning algorithm, a walking and running unified control strategy is constructed, rhythm information generated based on a rhythm generator is adopted in the walking and running unified control strategy, then action-critic is used for training, a bionic motion control system Walk2Run under speed driving is jointly constructed, natural gait transition and frequency adjustment are achieved, and the walking and running unified control strategy is obtained. And meanwhile, the physical capacity limitation of the robot is met, and motion fluency and energy efficiency are improved. The problems that an existing humanoid robot does not achieve energy optimization of human walking during walking, and mobility of an existing data set has high requirements for the motion ability of the robot and is poor in generalization ability are solved.

The invention discloses an assemblage multi-robot collaborative welding system in ship sections, and relates to the technical field of industrial robot collaborative control, the assemblage multi-robot collaborative welding system comprises an industrial internet platform, the industrial internet platform is in communication connection with the following modules: a visual inspection modeling module, which is used for scanning the surface of a workpiece through a 3D camera, constructing a point cloud model, and establishing a point cloud model; matching the point cloud model with a design drawing, and identifying a deviation between an actual workpiece and the drawing; and the path planning and correcting module is used for planning a collision-free welding path in combination with the visual detection data and the motion range of the robot. Through high-precision visual inspection scanning and an SLAM matching algorithm, a workpiece point cloud model is constructed in real time and is accurately aligned with a design drawing, the welding seam position and the workpiece geometric deviation can be accurately recognized, a collision-free welding path is generated in combination with robot kinematics constraint and a safety boundary, it is ensured that the pose of the tail end of a welding gun is attached to the surface of the workpiece, and the welding quality is improved. Welding deviation is reduced, and process stability is ensured.

The invention discloses a robot automatic mapping method and related equipment in an unknown open environment, and the method comprises the steps: obtaining sensor data, and constructing a local map according to the sensor data; topologizing the local map to obtain a collision-free topological graph of the local map; performing sparse processing on the collision-free topological graph to obtain a sparse topological graph; and inputting the obtained sparse topological graph into a strategy network model based on deep reinforcement learning, and outputting a strategy for controlling the robot to move so as to move the robot. According to the method, the environment mapping problem is modeled into a graph structure, spatial dependency in the environment is modeled through the relation between the nodes, and effective learning of the robot on the relation between different areas in the complex environment is achieved. In addition, the attention mechanism helps the robot to preferentially select the area with the high potential return in the autonomous exploration process by dynamically adjusting the attention weight of each area, and therefore the exploration efficiency is improved. The robot can be widely applied to the technical field of robots.

The invention discloses an industrial robot motion state intelligent control method based on a twinborn model, and the method comprises the steps: collecting the motion state parameters, such as the joint angular velocity, the joint angular displacement, the position and posture of an end effector, of a robot through a multi-domain coupling twinborn modeling engine; transmitting to an industrial internet twinborn intelligent control center for classified storage, and establishing a parameter association mapping table; constructing a multi-domain coupling twinborn model based on the parameters and the mapping table, inputting motion state representation data output by the model into a deep enhanced evolutionary control algorithm with spatio-temporal attention enhancement, and generating multiple groups of control instruction candidate sets after spatio-temporal dimension features are extracted; and the center evaluates and screens the candidate set, selects an instruction meeting the requirement and transmits the instruction to a robot execution mechanism. The method depends on a space-time attention enhancement algorithm to improve the control precision so as to complete a control link to avoid a process fault, the intelligence and reliability of robot motion control are effectively improved, and the method is suitable for robot motion regulation and control under complex industrial working conditions.

The invention discloses a robot trajectory planning method for a spraying production line, a robot spraying control method and a robot trajectory planning device.The trajectory planning method comprises the steps that model surface information is extracted, an extracted surface geometric figure is processed, and a surface spraying path and a connection transition path between surfaces are generated; and finally, generating a collision-free and full-coverage complete spraying motion path. According to the robot spraying control method, an optimal solution is selected by using an optimization algorithm to plan and control robot movement, so that the robot spraying quality is improved while the stability of a robot movement track is ensured; the robot trajectory planning device provides a workpiece conveying module and a robot and is used for applying the robot trajectory planning method to real spraying production line hardware.

The invention discloses a robot hand-eye calibration method, control equipment and a robot system, and relates to the technical field of robots. The robot hand-eye calibration method comprises the following steps: uniformly correcting a plurality of pixel coordinates obtained by dynamic shooting to a reference pixel coordinate system which is consistent with a camera coordinate system at a reference position in direction; and meanwhile, the world coordinates of the end effector corresponding to each sampling position are localized relative to the world coordinates of the end effector at the reference position, so that interference caused by global coordinate offset and camera orientation difference is eliminated, and the application range of the hand-eye calibration method is expanded. Besides, a singular value decomposition algorithm is adopted to solve a mapping relation between pixel coordinates and local world coordinates under a reference pixel coordinate system, so that a solved rotation matrix can meet orthogonality constraints, the hand-eye transformation process strictly conforms to a robot motion model with translation and rotation motion, and the calibration precision is improved. The calibration process can be automatically calculated and completed, and the calibration efficiency is high.

The invention discloses a position and posture compensation method for guiding installation of a contact network cantilever based on 3D vision and an automatic installation system. The position and posture compensation method comprises the steps that the spatial transformation relation between a 3D vision sensor coordinate system and a robot coordinate system is determined; the robot is controlled to move, and the three-dimensional point cloud data of the to-be-installed cantilever component and the three-dimensional point cloud data of the field installation foundation are collected; the collected three-dimensional point cloud data is registered with a corresponding preset CAD model, and a first real-time pose of the cantilever component in a robot coordinate system and a second real-time pose of the installation foundation in the robot coordinate system are obtained through calculation; calculating a pose compensation transformation quantity based on a pre-stored theoretical mounting pose of the cantilever, the first real-time pose and the second real-time pose; and according to the pose compensation transformation amount, planning and executing a motion track of the robot, and completing butt joint installation. The method is high in compensation precision, robustness and adaptability and high in construction efficiency, and the problem of uncertainty in automatic installation is fundamentally solved.

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 robot track synchronization control method. According to the technical scheme, efficient collection and over-distance transmission of neural signals are achieved through the quantum entanglement technology, the brain movement intention is analyzed in combination with a high-sensitivity sensor and an intelligent algorithm, an instruction is transmitted to a robot power driving system through a low-delay communication architecture, and real-time synchronization of man-machine tracks is achieved through a two-way feedback system. Meanwhile, the movement flexibility of the robot is improved by adopting a lightweight design, the problems of high delay, low precision, poor interaction experience and the like in traditional man-machine synchronous control are solved, and the method can be widely applied to the fields of exoskeleton robots, industrial collaborative robots, rehabilitation medical robots and the like. The method has the advantages of being low in delay, high in precision, capable of improving interaction experience and wide in application range.

A robotic pool cleaning vacuum, includes a chassis, a suction duct topping the frame and opening into a filter bag placed above, and an impeller placed inside the duct to suck up debris via a suction port and push it into the filter bag, the chassis including a motor axle drawing the robot, the axle includes two drive wheels driven by a single motor, and an axis connecting the drive wheels transversely to the movement of the robot, the motor being configured to invert its direction of rotation in contact with a wall, the robot further includes a third free wheel for greater stability and maneuverability.

The invention provides an artificial intelligence-assisted intelligent control decision method, which comprises the steps of obtaining real-time environment perception data, constructing an obstacle group motion intention prediction model by adopting a deep reinforcement learning algorithm, determining intention probability distribution and a prediction time window of an obstacle group, and obtaining a short-term motion trend of the obstacle group; according to prediction results of the trajectory deviation angle deviation and the deviation acceleration peak value, a robot path priority order is determined, and a candidate path set is generated; acquiring a navigation instruction of an optimal path, and adjusting motion parameters of the robot to obtain new environment sensing data; and according to the updated short-term movement trend of the obstacle group, circularly adjusting the path priority order, generating a new navigation instruction, and obtaining a continuously optimized robot movement track.

The invention discloses a motion planning method for a heterogeneous robot, and relates to the technical field of robots. The method comprises the following steps: step 1, subspace definition and differential homeomorphic mapping establishment; step 2, dynamic situation field construction; step 3, geometric motion generation; and 4, controlling system energy. According to the method, universal adaptation of robot systems with different configurations can be realized, the algorithm reuse rate is greatly improved, the development cost is remarkably reduced, efficient obstacle avoidance can be realized in a dynamic environment, and the method has excellent real-time performance and wide applicability and robustness.

The invention provides a movement control method, system and device for adaptively following a target object and a medium, and belongs to the field of robot automatic control, and the method comprises the steps: obtaining the motion state information of a robot, the obstacle information of the surrounding environment and the position information of the target object; generating a prediction track of the dynamic obstacle based on the dynamic obstacle information, and constructing a risk area according to the static obstacle information and the prediction track; sampling by adopting a dynamic window algorithm to obtain a plurality of groups of candidate speed pairs and generating a plurality of candidate tracks; on the basis of candidate trajectories, the risk area and the predicted trajectory of the dynamic obstacle, a trajectory risk evaluation value is obtained through calculation; and based on a preset evaluation index, scoring the candidate trajectory, selecting an optimal speed pair according to a scoring result, and based on the optimal speed pair and the current actual speed of the robot, generating a control instruction to drive the robot to move. The autonomous following performance and the user experience of the robot in the dynamic environment are improved.

The invention discloses a dual-drive self-switching strong-adaptability quadruped robot motion control method, which comprises the following steps of: estimating current topographic features by using sensing signals such as body state feedback, IMU (Inertial Measurement Unit) and sole force, and predicting a falling probability according to the topographic features, so that a quadruped robot is intelligently switched between two driving modes of MPC (Moving Picture Control) independent control and MPC-DRL (Moving Picture Control-Digital Regulation Language) cooperative control; dynamic behavior parameters are introduced to influence the gait and step height of robot movement, the optimization efficiency and smoothness of MPC and the disturbance adaptive capacity of DRL are efficiently fused, and the high-robustness, high-efficiency and self-adaptive movement capacity of the quadruped robot in various terrains under the condition that external sensing information is not relied on is enhanced.

The invention discloses an integral adaptive admittance control method and system, a computing control device and a storage medium. The method comprises the steps that 1, a target admittance model is established and initialized; step 2, establishing an adaptive admittance controller according to an adaptive control strategy based on environmental information identification, and deriving a reference trajectory corresponding to the target admittance model; step 3, establishing an integral admittance controller, performing steady-state error optimization processing on the reference trajectory, and generating an optimized reference trajectory; and step 4, generating a robot motion instruction according to the optimized reference trajectory. According to the method, firstly, an integral controller and online environment parameter identification are deeply fused, steady-state errors are eliminated through integral terms, dynamic environment changes are compensated through parameter identification, and stability collaborative optimization is carried out. And secondly, a reference trajectory is dynamically adjusted by introducing a force error integration link, so that the stability contradiction between integration control and an adaptive law is solved, and the unification of high-frequency response and lightweight control is realized.

The invention provides a mobile robot exploration trajectory generation method based on motion primitive preferential selection, and belongs to the field of autonomous exploration of a mobile unmanned vehicle in an unknown environment. According to the method, the motion primitives serve as basic units of exploration gain calculation, the generated motion primitives conform to Ackerman kinematics constraints, and it is ensured that the exploration trajectory conforms to the actual motion characteristics of the robot. In the exploration gain calculation of the motion primitive, the occupation state information of the two-dimensional probability grid map is considered, and the trafficability of the track is ensured; and meanwhile, punishment on change of the movement direction of the robot is introduced, and frequent steering in the exploration process is reduced. Besides, exploration gains are calculated according to sampling points on the track, observation of the robot on the environment in the track advancing process is expressed, and information obtained at a target point is not considered only. According to the method, the local track formed by the motion elements is preferentially selected, the exploration track is directly generated, the algorithm process is simplified, and the operation efficiency of the exploration algorithm is improved.

The invention provides an automatic robot and a method. The method comprises the steps of generating an initial motion track of a target robot through task execution environment information; calculating the trajectory deviation value of the target robot in the operation process based on the pose data collected in real time and the initial motion trajectory; performing segmented extraction on the trajectory deviation value to obtain a trajectory deviation sequence of the target robot, predicting a predicted trajectory error of the target robot in a dynamic environment through a trajectory error prediction model and the trajectory deviation sequence, and constructing an error correction factor of the motion trajectory of the target robot according to the predicted trajectory error; obtaining fusion correction track characteristics of the target robot in a dynamic environment according to the motion track corrected by the error correction factor; and driving the target robot to perform adaptive trajectory control in a dynamic environment based on the fused corrected trajectory features. By adopting the scheme, the adaptive trajectory control capability of the automatic robot can be improved based on accurate prediction of the trajectory deviation of the automatic robot in a dynamic environment.

The invention discloses a robot motion path real-time deviation rectifying method based on digital twinning, which comprises the following steps that an off-line programming system generates an initial welding track according to a welding requirement, converts the initial welding track into a code file, transmits the code file to a digital twinning system, and generates a preset track Q; the virtual model layer synchronously maps a motion path of a welding robot entity to obtain a real-time mapping track P; track points of the two tracks are matched, and wrong matching is eliminated; a robot track model is constructed, a predicted track T of entity motion of the welding robot is obtained, and track point matching is conducted on the predicted track T and a preset track Q; and the deviation value of the matched track points of the two tracks is calculated, the robot electric control system corrects the welding track of the welding robot entity according to the deviation value, and the welding track is corrected in real time. According to the correction method, track points are matched only by means of a digital twin system for real-time correction; the adopted track point matching algorithm is small in error and high in matching precision.

The invention discloses a robot vision system construction and control method, and the method comprises the following steps: collecting environment image data and robot motion state data, carrying out the real-time preprocessing of the environment image data, generating standardized image data, and carrying out the real-time monitoring of the environment through setting a dynamic environment sensing module. When real-time grabbing operation guided by robot vision is carried out, an environment light interference real-time judgment mechanism is established, differential compensation strategies are set for different working condition scenes, the stability of visual data collection under the complex illumination condition is guaranteed, and meanwhile compensated light parameters are synchronized to an image processing unit; the problem of feature recognition distortion caused by abnormal environmental reflection can be reduced in real time, the accuracy of robot target positioning is ensured, the track error of visual guidance is reduced, and the completeness of a high-precision assembly task is ensured when it is detected that the in-place posture offset exceeds a safety threshold value.