1625 results about "Robot locomotion" patented technology

The invention discloses an underwater robot autonomous obstacle avoidance and path planning system based on a multi-modal sensor, particularly relates to the field of underwater robot navigation, solves the problems of multi-modal sensing and path planning in a complex environment, and unifies the feature space of a multi-source heterogeneous sensor based on a physical field correction mode of an acousto-optic fluctuation rule. Fusion deviation caused by physical property difference of sonar and visual data is overcome, and obstacle characterization precision and dynamic environment adaptability are remarkably improved; a dynamic confidence decision-making mechanism is combined with a path curvature-moment constraint model, the environmental perception credibility and the robot motion performance are synchronously fused into a path generation process, the feasibility of motion control is ensured while the geometric collision risk is avoided, and the contradiction between the safety and the performability in path planning is solved; the global path optimization eliminates the hidden danger of local path sudden change through iterative smoothing under double constraints of physical field and dynamics, and forms an optimal navigation scheme considering obstacle avoidance efficiency, energy consumption and motion stability.

The embodiment of the invention relates to robot path planning, in particular to a self-adaptive robot trajectory planning method and system based on deep reinforcement learning, and the method can completely present the motion condition of a robot by obtaining a real-time trajectory data set covering an environment interaction state and a motion attitude sequence; key features can be accurately extracted through dynamic track feature extraction processing; a self-adaptive trajectory optimization strategy is generated based on a pre-trained deep reinforcement learning strategy network, so that the strategy can be flexibly adjusted according to dynamic characteristics; incremental trajectory correction processing is carried out on the robot motion posture sequence, the trajectory can be optimized step by step, and an optimized trajectory sequence conforming to dynamic environment adaptability is generated; and the optimized trajectory sequence is synchronized to the motion control system, so that the robot can quickly and accurately execute the optimized trajectory, and the motion planning capability and execution efficiency of the robot in a complex dynamic environment are remarkably enhanced.

The invention belongs to the technical field of automation control, and discloses an inspection robot control system and method based on inspection information, and the inspection robot control system based on the inspection information comprises a path planning module, an obstacle detection module, a region evaluation module, a path reconstruction module and a state recovery module. By acquiring the coordinate point set of the inspection map and the start-stop position of the robot and analyzing the path steering change and distance accumulation, the navigation precision of the inspection robot in a complex environment is optimized, roundabout and repeated movement caused by inaccurate path planning is reduced, the real-time distance between an obstacle and the path is measured and judged, and the navigation precision of the inspection robot is improved. And the response speed and accuracy of dealing with sudden obstacles are improved, when the path is reconstructed, through analysis of connection curve data, the smoothness of the path and the movement continuity of the inspection robot are ensured, the operation efficiency of the inspection robot is improved, the manpower monitoring demand is reduced, and the resource configuration is optimized.

The invention relates to the field of robot motion, in particular to a multi-modal information fusion robot motion planning method, which comprises the following steps: information acquisition: acquiring multi-modal data of a robot through various sensors; preprocessing: carrying out normalization, denoising and synchronization processing on the multi-modal data; fusion: coding and fusing the preprocessed data by adopting a feature level fusion method, and generating an environment model based on an information fusion algorithm; motion planning: combining an environment modeling and path planning algorithm, and generating a motion path according to the fused multi-modal information; and feedback adjustment: dynamically adjusting the motion track by adopting a feedback control mechanism based on the environment change sensed in real time. The invention provides a multi-modal information fused robot motion planning method and equipment, which are used for improving the perception capability of a robot to a complex environment, enhancing motion planning and precision and improving task execution efficiency and safety by fusing multi-modal information.

The invention provides a robot motion control model training method and device based on deep reinforcement learning, and relates to the technical field of sensors and robots. The method comprises the following steps: performing deep reinforcement learning-based training on a strategy network for outputting an action strategy for controlling the robot to move by utilizing a teacher-student model framework; wherein the training process of the strategy network further comprises the steps of encoding historical linear velocity information of the robot through a linear velocity encoder to generate a first potential vector, and inputting the first potential vector into the strategy network for auxiliary training. According to the method, the motion trend, the state change track and the surrounding terrain structure of the robot are comprehensively considered during action decision making, and strategy output is more stably and accurately controlled.

A method for operating a robot, including: capturing images of a workspace; capturing data indicative of movement of the robot; capturing LIDAR data as the robot moves within the workspace; generating a map of the workspace based on the LIDAR data; actuating the robot to drive; discriminating between an object on a floor surface along a path of the robot and the floor surface based on the captured images; actuating the robot to drive until determining all areas of the workspace are discovered and included in the map; and executing a cleaning function.

The invention discloses a rescue method and system of a rescue robot for exploration, and relates to the technical field of underground space rescue, and the method comprises the following steps: obtaining multi-path acoustic echo data of a karst cave and motion track data of the robot, and constructing a three-dimensional point cloud model according to the multi-path acoustic echo data and the motion track data of the robot; and identifying unmatched data in the multi-path acoustic echo data and the robot motion trail data, and taking an area where the unmatched data is located as an abnormal area. According to the method, the spatial resolution of signal acquisition is enhanced through the multi-microphone array, robust acoustic fingerprints are extracted in combination with a noise reduction algorithm and short-time Fourier transform, high-confidence human body sound source signals are screened out by using a feature template matching mechanism, accurate extraction and recognition of human body acoustic features in a karst cave complex noise environment are realized, and the accuracy of human body acoustic feature recognition is improved. The problem of misjudgment caused by confusion of sound source features and environmental noise in a traditional method is effectively solved.

The invention relates to the technical field of obstacle avoidance early warning, in particular to an obstacle avoidance early warning method and system for a robot. The method comprises the following steps: acquiring motion data and moving target point data of a robot; robot motion mode classification is conducted on the robot motion data, and a robot motion mode and a robot static mode are generated; performing environment detection on the moving target point data by using a built-in laser radar of the robot according to the static mode to obtain standard static environment detection data; and planning the path according to the standard static environment detection data, and generating initial running path planning data of the robot. According to the invention, the obstacle avoidance early warning precision and real-time performance of the robot in a complex environment are improved by accurately distinguishing static and dynamic objects, reconstructing the motion trail and planning and adjusting the path.

The invention discloses a quadruped robot autonomous decision control system based on multi-modal perception, and belongs to the technical field of robot motion, and the system specifically comprises the steps: collecting visual, force and sound information through multiple sensors of a camera, a force sensor and a microphone array, and carrying out the preprocessing; according to the processed information, a diagonal gait, leg track and motion path planning scheme is generated by adopting a motion planning strategy; generating a specific control instruction to drive the robot to move, and monitoring and adjusting in real time; after the task is completed, the motion performance of the robot is evaluated, and a motion planning and control method is optimized according to an evaluation result; the autonomous motion ability and adaptability of the quadruped robot in a complex environment are improved.

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 wheel-foot robot, a robot system, a motion control method and a motion control system, and the motion control system comprises a track publishing module which is used for converting the expected speed and position of a robot trunk into a state track; the predictive control module is used for constructing a model predictive control algorithm which takes the expected trajectory as input and takes the system state and the control input quantity as output, and optimizing the system state and the control input quantity in real time; the state estimation module is used for estimating the motion state of the robot by fusing the sensor data; and the whole body control module is used for constructing a multi-task control algorithm which takes the expected trajectory and the motion state as input and takes the joint torque, the joint speed and the joint position as output and has priorities or weights. According to the method, global prediction and optimization can be realized, the complexity of each optimization calculation can be effectively reduced, and the response speed and the real-time processing capability of a robot system are improved.

The invention relates to the technical field of automatic inspection, in particular to an automatic road surface inspection robot control method and equipment, and the method comprises the steps: collecting environment data and robot motion state data through integrating a plurality of sensors, carrying out the preprocessing, carrying out the data fusion through employing a Kalman filtering algorithm, and generating a comprehensive road surface state map; a disease area is accurately identified and positioned through a convolutional neural network, dynamic path planning and polling strategy optimization are performed through a reinforcement learning algorithm, adaptive control of a complex road surface environment is realized through a fuzzy control algorithm, and meanwhile, a targeted road surface maintenance suggestion and an emergency processing scheme are generated according to real-time polling data, so that the real-time polling of a road surface is realized. According to the method, the scientificity and timeliness of road surface maintenance and traffic management are effectively improved, the road surface diseases can be accurately recognized and the inspection path can be optimized in a complex environment, and the road surface inspection efficiency and the intelligent level of road maintenance are remarkably improved.

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 provides a robot control method, computer equipment and a computer readable storage medium, and the method comprises the steps: obtaining multi-modal data of a user, the multi-modal data comprising attitude data, sound data and force control data; performing feature extraction and fusion processing on the multi-modal data to obtain multi-modal fusion features; according to the environment information of the area where the robot is located and historical interaction data of the robot, context information of man-machine interaction is determined; and according to the multi-modal fusion features and the context information, a response strategy is generated, and the robot is controlled to move according to the response strategy, so that the naturalness and accuracy of man-machine interaction are improved.

The invention provides a spatial heterogeneous vision positioning system and positioning method of a pineapple picking robot, relates to the technical field of picking robot positioning, and realizes high-precision spatial positioning and attitude estimation of the robot in a complex terrain environment by comprehensively utilizing multi-source information such as vision, inertial measurement, multispectrum and structured light. The positioning stability and the real-time performance are obviously improved; by embedding a multi-modal deep learning model, efficient non-destructive detection and intelligent classification of internal defects of pineapple fruits are realized, a mechanical arm is supported to dynamically adjust a picking strategy, fruits with better quality are preferentially selected for picking, and the picking quality and efficiency are improved; and meanwhile, the preset three-dimensional terrain model and the heuristic search algorithm are combined, the motion path of the robot is effectively planned, it is guaranteed that the robot safely and stably moves in the complex environment, and the mechanical damage risk is reduced.

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 discloses a ship plane segmented welding multi-robot task distribution system based on digital twinning, and relates to the technical field of ship intelligent manufacturing, and the system comprises a four-dimensional digital twinning body module which is used for collecting workpiece assembly errors, welding seam positions and robot state data in real time; and constructing a twin model comprising a physical engine layer, a process knowledge base and a dynamic topological graph. According to the ship plane segmented welding multi-robot task allocation system based on digital twinning, workpiece assembly errors, robot motion states and welding process rules are fused in real time through the digital twinning body, a weld joint task topological graph is dynamically constructed, and multi-robot task accurate allocation is achieved in combination with an anti-conflict auction algorithm. By means of a space-time gridding cooperation mechanism, motion conflicts between a large gantry mechanism and a small gantry mechanism are effectively solved, and zero interference of a welding path is guaranteed; the local path optimization module adaptively adjusts the posture and the dry extension length of a welding gun for a narrow space, and eliminates the physical collision risk.

A machining head with active correction of the type used in association with a robot to carry out fast high-precision machining tasks especially on parts for the aeronautical production industry that has localised position and angular sensors, and a machining motor or spindle provided with localised movement with respect to the head casing, independent of the robot's movement, this movement being preferably both displacement and rotation with respect to both, allowing active correction of the machining position is disclosed. The invention provides the main advantage of allowing errors by the robot or deformation of the part to be machined, in positioning for machining, to be corrected in a localised very fast and accurate way, without the need to re-position the robot.

The invention provides a whole-body control method and device of a robot, a storage medium and the robot, and belongs to the technical field of humanoid robot control. The method comprises the following steps: training a basic whole body control model by using an AMASS data set; training a sub-control model corresponding to each motion type on the basis of the basic whole body control model by using the plurality of clustering sub-data sets; after the trained sub-control model is deployed to a control module of the robot, an execution track sequence of the robot is collected by controlling the robot to move; utilizing the collected execution track sequence to train an action increment model corresponding to the sub-control model so as to adjust the corresponding sub-control model; and fusing the adjusted sub-control models by using a knowledge distillation technology to obtain an optimized whole-body control model. According to the embodiment of the invention, a'general-expert-general 'training framework is provided and is used for learning a general whole-body control strategy, and the problem of interference among different motion types is effectively relieved.

The invention discloses a gas extraction pipeline tiny leakage positioning method based on multi-sensor fusion, which belongs to the technical field of positioning, is used for gas extraction pipeline leakage positioning, and comprises the following steps: establishing a state equation and an observation equation of a motion process of an inspection robot, and realizing inspection robot positioning under a navigation system based on Kalman filtering. And realizing coal mine gas extraction pipeline leakage source positioning under a microphone array coordinate system based on a beam forming algorithm, and obtaining coal mine gas extraction pipeline leakage source positioning under a navigation system through coordinate conversion. According to the method, the leakage source position of the gas extraction pipeline under the navigation system is directly given, maintainers can conveniently and quickly reach the leakage source position, and the first-aid repair time is greatly shortened; the positioning error of the inspection robot can be effectively reduced, and the phenomenon that the positioning error is accumulated along with time when the inspection robot only uses an accelerometer and a gyroscope for positioning is avoided; the influence of background noise on a positioning result can be greatly reduced, and the spatial resolution of a leakage positioning result is improved.

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 provides a robot cable fault classification method and system based on deep tree learning, and relates to the technical field of robots, and the method comprises the steps: collecting cable signals and joint position information, and calculating bending degree features; voltage, current, power and impedance features are extracted according to the motion state of the robot, and a state sensitive feature set is formed; constructing a deep tree learning classifier, and performing feature transfer through an attention connection mechanism; and carrying out local optimization and global optimization training by adopting a multi-scale splitting criterion. According to the method, cable faults in different motion states can be effectively identified, and the classification accuracy and robustness are improved.

The invention provides a touch and visual perception integrated micro-nano robot magnetic field control system and method, and the system comprises a camera which collects the image of a micro-nano robot in a pipeline in real time; the magnetic field generating device generates a gradient magnetic field for driving the micro-nano robot to move through a permanent magnet; the tail end of the mechanical arm is connected with the permanent magnet and adjusts the three-dimensional pose to change the magnetic field distribution; the force feedback equipment is used for providing a touch signal for the micro-nano robot to be in contact with the pipeline boundary; and the computer is configured to identify the aggregation state of the micro-nano robot according to the camera image, increase the resistance of the force feedback equipment and reduce the rotation speed of the permanent magnet when the dispersion state is detected, and reduce the resistance and recover the conventional magnetic field control after the aggregation state is recovered. According to the system, accurate control and abnormal state self-adaptive response of the micro-nano robot in a complex experiment scene are realized through touch-vision collaborative perception and dynamic magnetic field regulation and control.

The invention relates to the technical field of robots, and discloses a robot dynamic inspection control system based on anti-shake performance. The visual detection module is used for collecting image data of an inspection target; the motion compensation and anti-jitter module is used for monitoring the motion state of the robot in real time and compensating jitter generated by the robot; the environment sensing module is used for detecting and acquiring change data of external environment factors in real time; the data fusion and optimization control module is used for fusing data from the visual detection module, the motion compensation and anti-jitter module and the environment perception module; and the intelligent control module is used for fusing and optimizing the control instruction generated by the control module according to the data. By introducing the motion compensation and anti-jitter technology, the motion state of the robot is monitored and optimized in real time, interference caused by vibration is eliminated, the precision and stability of images and sensor data in the dynamic inspection process of the robot can be greatly improved, and the influence of external interference on a measurement result is reduced.

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

The invention 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 provides a robot production line article grabbing method based on visual localization, which comprises the following steps: preprocessing a multi-modal image to obtain an original image; performing grid mapping on the original image to obtain a multi-level feature descriptor; based on the multi-level feature descriptors, a dynamic mapping relation among the three coordinate systems is established through a non-rigid coordinate system alignment algorithm, and coordinate offset of movement of the conveyor belt is compensated in real time; performing hierarchical feature matching on the multi-level feature descriptors and an article template library, identifying article categories and extracting contour geometric features; based on the contour geometric features and the surface curvature distribution, the three-dimensional pose and candidate grabbing points of the object are obtained through a geometric constraint optimization algorithm; and generating a robot obstacle avoidance track according to the candidate grabbing points and the robot motion model. According to the method, accurate coordinate compensation is realized through multi-modal data fusion and dynamic adaptive grid mapping, and the article positioning accuracy is improved in combination with hierarchical feature matching.