126 results about "Robot motion planning" patented technology

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 an improved robot motion planning method based on Jump Point Search (JPS), and relates to a robot motion planning method based on JPS. According to the method, the search direction and the path planning process of the JPS algorithm are optimized, the path direction guidance of BFS (breadth-First Search) is combined, and the search direction priorities are sorted, so that the overall search efficiency and the path security are remarkably improved. Specifically, according to the method, a cosine value is used for measuring the similarity degree of each search direction and a BFS path, and the directions are divided into high priorities and secondary priorities. In the hop point searching process, the heuristic function value only evaluates the extended hop points in the high priority direction, and selects the hop point corresponding to the minimum value, so that the extension in the redundant direction is reduced, and the searching efficiency is improved. In addition, the invention also designs a set of path optimization strategy which mainly comprises two parts, namely a'security node updating strategy 'and a'redundant node eliminating strategy'. Aiming at the dangerous boundary path points, a security node updating strategy is utilized, and adjacent nodes are replaced by common security neighbor points, so that paths with potential safety hazards are prevented from being formed; according to the redundant node elimination strategy, the number of control nodes is reduced and the overall path length is shortened by detecting the direct connectivity between continuous nodes.

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 discloses a two-arm robot motion planning method, system and device for a strong coupling scene, and the main content is as follows: constructing a strong coupling two-arm robot closed chain system kinematic model, giving a group of mechanical arm # imgabs0 # joint configuration, and outputting a joint configuration set corresponding to another mechanical arm # imgabs1 # through the kinematic model, therefore, the planning space dimension is greatly reduced; according to a master-slave batch self-adaptive planning strategy, a mechanical arm # imgabs2 # carries out active batch self-adaptive path planning, and then a mechanical arm # imgabs3 # carries out passive batch self-adaptive path planning based on the strong-coupling double-arm closed-chain system kinematic model; and carrying out pre-motion safety check on the double-arm robot to ensure absolute safety of double-arm cooperative motion in the closed chain operation process. The problems of real-time performance and safety in a strong coupling scene are effectively solved, and reliable technical support is provided for industrial-grade double-arm cooperation.

The invention discloses a double-arm robot motion planning method and system for complex operation tasks, and the method comprises the steps: decoupling the collision detection of a double-arm robot into double-arm self-collision detection and environment obstacles, and constructing a collision distance learning frame for a high-degree-of-freedom double-arm robot; high-precision self-collision detection of the double-arm robot is realized by using a paired collision distance learning model, collision detection of the double-arm robot and environmental obstacles is realized by using a collision distance reasoning learning model, and the double-arm robot has the capability of processing dynamic obstacles on the basis of keeping the high-precision self-collision detection precision of the double-arm robot; a batch parallel expansion mechanism based on an artificial potential field method is designed, and efficient path searching of the double-arm robot in a complex environment is achieved; a path re-planning module based on a collision distance learning framework is constructed, environment obstacle change information is monitored and updated in real time, the two-arm robot can adaptively adjust the motion path, and it is ensured that the two-arm robot can adapt to complex operation tasks.

The invention relates to the technical field of robot motion path planning, in particular to a beam end spraying and scabbling robot motion planning system based on vision. According to the method, multi-angle images collected by taking a robot as a carrier are subjected to space fusion to generate a three-dimensional space map, position coordinates and posture information of a beam end in the three-dimensional space map are identified and positioned according to standard contour features of the beam end, and an initial arrival path of a robot base is planned; a base movement path is planned according to all the chiseling position coordinates, and a chiseling operation movement path of the mechanical arm is planned based on the defect area and forbidden area positions of the operation coverage area corresponding to all the chiseling position coordinates, so that single-stop coverage maximization and operation safety are both considered; and meanwhile, by combining the actual positions of the base and the tail end of the mechanical arm in the scabbling process of the robot, the positions of the base and the mechanical arm are dynamically verified, the position precision and operation safety in the whole scabbling process are ensured, and the scabbling quality and consistency are improved.

The invention discloses a robot motion planning method and device based on a dynamic parameter return-to-zero neural network and a medium. The method comprises the steps that 1, an expected track and an expected attitude vector are set; step 2, giving an initial joint angle and an initial attitude vector; 3, reasonable physical limit constraint conditions of the redundant robot joints are set; 4, the redundant robot motion planning considering the joint physical limit constraint and the posture keeping is described as a quadratic planning scheme; and 5, providing a dynamic parameter return-to-zero neural network for the quadratic programming scheme in the step 4, and applying the dynamic parameter return-to-zero neural network to motion programming of the redundant robot. Redundant robot motion planning tasks of joint physical limit constraint and posture keeping are considered, and it can be guaranteed that model errors converge to 0 within finite time.

The invention discloses a robot motion planning method fusing GRU-Attention and TD3-SAC, and aims to solve the problem that motion planning based on a traditional reinforcement learning method is relatively weak in strategy diversity, stability and exploration capability balance. Specifically, it is proposed that (1) a gated loop unit (GRU) and an attention module (Attention Module) are introduced into an actor-commentator network to enhance the representation ability of time sequence features; and (2) on the basis of an Actor-Critic network in which GRU-Attention is introduced, a dual-delay depth deterministic strategy gradient (TD3) and a soft actor-commentator (SAC) algorithm are fused, and through fusion of a promotion exploration mechanism of the TD3 and the SAC algorithm, the exploration ability of the robot to a complex unknown environment is enhanced while the training stability and robustness are kept.

The invention discloses a robot motion planning method and system based on a sampling dictionary, and belongs to the robot motion planning technology, the architecture comprises a quantification stage and a prediction stage, in the quantification stage, through a pre-training VQ model, under the condition that re-training or fine tuning is not carried out at the same time, the prediction can be carried out at the same time; the method is directly applied to other constraint planning problems, a gradient projection operator is used for projecting sampling points to a constraint manifold in a prediction stage, and finally, a path from a starting point to a target point is generated by using the improved sampling points and optimized distribution under the condition of considering task space region constraints through a bidirectional planning algorithm. The planning search space is reduced through the pre-trained VQ-MPT model, the sampling area is made to be close to the constraint manifold through the gradient optimization technology, the planning efficiency is improved, and the planning time and path cost are remarkably reduced while it is guaranteed that the trajectory meets the constraint.

The invention discloses a humanoid robot control method and system, the system comprises a sensor module, a sensing module, a state representation module, a decision module, a motion control module, an actuator module and a feedback module, and the method comprises the following steps: step 1, data acquisition; step 2, environment perception; step 3, state representation; step 4, action selection; step 5, motion control; step 6, performing feedback and adjustment; according to the method, the hybrid model combining the ViT and the CNN is adopted for environment perception, the environment perception precision is improved, the error recognition rate is reduced, motion planning is carried out through reinforcement learning, the robot can autonomously learn the optimal motion strategy, decision making and adjustment can be autonomously carried out according to the environment change and the task target, and the robot experience is improved. The problem that a traditional motion planning algorithm is high in calculation complexity is solved, the robot motion planning efficiency, the autonomous decision-making ability and the environment adaptability are improved, and safe, efficient and autonomous motion of the humanoid robot in a complex dynamic environment is achieved.

The invention provides a multi-mobile-robot layered reinforcement learning motion planning method and system, and belongs to the technical field of robots and swarm intelligence. According to the method, a double-layer planner is constructed; the upper planner and the lower planner perform action planning by adopting a neural network model, and are trained through reinforcement learning. The upper layer planner generates the speed of the robot in a continuous time space according to the local map information and the physical information of the robot, and the speed is used as the global guiding speed and transmitted to the lower layer planner. And the lower layer planner takes optimal reciprocal obstacle avoidance ORCA observation and robot self observation as lower layer observation, and generates a robot action planning result by taking the global guidance speed provided by the upper layer planner as a target speed. By using the method, the problem of multi-robot motion planning in a real indoor unstructured environment can be solved.

The invention discloses a lithium battery recycling and disassembling method based on AI + 3D vision, and relates to the technical field of battery recycling, and the method comprises the steps of S1, multispectral polarization image acquisition, S2, polarization parameter dynamic calibration, S3, image fusion, S4, deep learning denoising optimization, S5, 3D point cloud reconstruction and feature extraction, and S6, robot motion planning and execution. According to the method, a dynamic situation field model is combined with an impedance control principle, so that the robot can sense obstacle distribution in real time in a dynamic environment and autonomously adjust a motion strategy, and meanwhile, the contact force between an end effector and a target object is accurately controlled; a rejection and attraction field is modeled in real time, so that the sudden mechanical interference risk in the disassembling process is effectively avoided; the impedance coupling mechanism ensures that the robot maintains stable interaction force in contact operation through force feedback closed-loop adjustment, and battery damage or tool failure caused by sudden change of external force is avoided.

The invention belongs to the technical field of path planning and robot motion planning, and particularly relates to a multi-strategy fusion improved RRT * sampling planning method and device and a storage medium, and the method comprises the steps: constructing a dual-search tree through a starting point and a target point in a three-dimensional working space, and selecting a to-be-expanded search tree through a random mode; halton-Bridge mixed sampling is adopted to generate sampling points, so that the global coverage uniformity is improved, and the narrow channel sampling capability is enhanced; calculating the dynamic step length of the exponential decay based on the local obstacle proportion; adopting a hierarchical guidance expansion strategy, preferentially expanding towards a target, introducing an improved artificial potential field to guide expansion when the expansion fails, and taking the bottom with random expansion; performing RRT * neighborhood father node reselection and reconnection optimization on the new node; greedy pruning, self-adaptive interpolation and cubic B-spline smoothing are carried out on a path after the double trees are connected, and an executable smoothing track is output; according to the scheme, planning efficiency, success rate and path quality are improved.

This invention relates to a rapid robot motion planning method. First, it models example actions using a sequence of trajectory points that characterizes task features. Then, it extracts key points from the example trajectory using a trajectory segmentation method based on a first-order difference metric. Finally, it uses PCA to obtain the motion pattern features of the key points of the example trajectory, determines prediction constraints, and achieves rapid motion prediction under a given robot state by solving a quadratic programming problem under these constraints, thus completing the planning. This invention also provides a rapid robot motion planning system. This invention improves the efficiency of robot motion generation in industrial environments and enhances the robot's adaptability to real-time customized tasks.

The present invention discloses a dual-arm robot motion planning method and system for complex operation tasks. The method decouples the dual-arm robot collision detection into dual-arm self-collision detection and environmental obstacle detection, constructs a collision distance learning framework for high-degree-of-freedom dual-arm robots, uses a paired collision distance learning model to achieve high-precision self-collision detection of the dual-arm robot, and uses a collision distance inference learning model to achieve collision detection between the dual-arm robot and environmental obstacles. On the basis of maintaining the high-precision self-collision detection accuracy of the dual-arm robot, the system is enabled to have the ability to handle dynamic obstacles; designs a batch parallel expansion mechanism based on the artificial potential field method to achieve efficient path search of the dual-arm robot in complex environments; constructs a path replanning module based on the collision distance learning framework to monitor and update environmental obstacle change information in real time. The dual-arm robot can adaptively adjust the motion path, ensuring that the dual-arm robot can adapt to complex operation tasks.

This invention relates to the field of robot motion planning technology, and proposes a method and system for obstacle avoidance path planning for a multi-degree-of-freedom robotic arm. The method includes: constructing a resultant force guiding field composed of an attractive potential field and a repulsive potential field; iteratively searching from the starting point to the target point along the gradient direction of the resultant force guiding field to generate a pre-path point sequence; mapping the generated pre-path point sequence to the joint space through inverse kinematics to obtain a candidate configuration sequence; sequentially detecting whether the local path segments between adjacent configurations in the candidate configuration sequence satisfy collision constraints, and determining that adjacent configurations are gaps to be repaired if they do not satisfy the constraints; establishing a bidirectional random expansion tree and adjusting the expansion step size in real time according to the potential energy intensity of the repulsive potential field in the current region; repairing the gaps to be repaired through the interconnection of the bidirectional random expansion tree, and splicing the repaired path segments with the path segments that satisfy the collision constraints to generate the final obstacle avoidance motion path.

The invention discloses a robot motion planning method, device and equipment for a constrained trajectory and a medium, and belongs to the field of industrial robot trajectory planning, and the method comprises the steps: converting the motion constraint of a robot at the current moment into a path parameter space; solving a first movement speed of the robot through a third-order sliding mode controller; performing look-ahead simulation on each second constraint boundary set of the robot in the remaining paths; if the simulation result is that the second movement speed exists, the smaller value of the first movement speed and the second movement speed serves as the movement speed of the robot; if the simulation result is that the second movement speed does not exist, the second movement speed of the last period serves as the movement speed of the robot; and controlling the robot to move for a period duration, and if the robot does not walk to the path end point after the motion is finished, circularly executing all the steps until the robot walks to the path end point. Therefore, by implementing the method, real-time planning of the constrained trajectory can be realized.

The invention discloses a robot path planning method based on an adaptive fuzzy and hybrid strategy, and belongs to the technical field of robot motion planning, and the method comprises the steps: carrying out environment perception, and obtaining environment information; the method comprises the following steps: on the basis of an RRT-Connect algorithm framework, fusing a fuzzy control theory, a hierarchical hybrid expansion mechanism and a dynamic path optimization thought to obtain an improved RRT-Connect algorithm; and an improved RRT-Connect algorithm is utilized, and robot path planning is realized based on environment information. According to the method, the fuzzy control theory is utilized to endow the path planning process with a macroscopic intelligent decision-making capability, and a microscopic accurate execution and fault-tolerant capability is provided through a hierarchical mixed strategy, so that the method is an efficient and robust universal solution; the success rate, efficiency and path quality of path planning of the robot in a complex high-dimensional space are remarkably improved, and more efficient and better collision-free path generation can be achieved.

The invention discloses a flying robot motion planning method and system in a microgravity environment and a storage medium, relates to the field of robot automation, and aims to solve the problem that an efficient, accurate and lightweight flying robot motion planning method is lacked for a dynamically changing microgravity environment. Comprising the following steps: step S100, planning an initial optimal path from a starting point to an end point in an ideal environment assuming that no obstacle exists, and recording key positions interacting with the obstacle in the path to form a crossing point sequence; step S200, performing ray tracing along a vertical direction of a crossing point connecting line, searching a barrier-free area in a path, marking the barrier-free area as a candidate node, dynamically adjusting the node by introducing a feedback mechanism, and constructing a topological graph through iterative optimization; and S300, performing path optimization in combination with the search A and kinematics constraint, and introducing a heuristic function to improve the path search efficiency, thereby obtaining the optimal path of the motion of the flying robot in the microgravity environment.

The application belongs to the field of compound robot motion planning and control, and discloses a compound robot dynamic obstacle avoidance control method based on partition adaptive potential field and acceleration MPC fusion. According to the minimum clearance between the robot and the obstacle, the collision, obstacle avoidance and safety area are divided, a continuous partition scheduling function is designed to construct a weighted artificial potential field, a potential field hybrid force is outputted and an MPC initial value sequence is constructed. A discrete prediction model is established by taking acceleration as the control input, a constraint optimization problem in a prediction time domain N=3 is solved online, input constraints, change rate constraints and obstacle avoidance soft constraints with a relaxation variable are applied, the potential field initial value is used for guiding the solution and triggering the initial value updating, and PID weighted terminal guidance and potential field hybrid force fusion are introduced in the target near field. The application considers the safety of dynamic obstacle avoidance, motion stability and target arrival accuracy in a narrow environment.

The application belongs to the field of robots, in order to solve the problems of motion planning scalability, navigation flexibility and system safety of large-scale robot cluster, the application provides a collision risk awareness and high scalability large-scale cluster robot motion planning method and device, comprising: in the macro stage, using a Gaussian mixture model to represent the macro state of the cluster, and planning the transport trajectory of the Gaussian mixture model through model predictive control; in the micro stage, using an artificial potential field method to assign target positions to the robots, and combining a distributed model predictive control to realize the tracking and dynamic obstacle avoidance of the individual to the transport trajectory of the Gaussian mixture model. The application can effectively improve the motion planning performance of the large-scale cluster robot system in a complex environment. In the application, by adjusting the risk acceptance degree, the distance between the robot cluster and the obstacle can be flexibly adjusted, and the balance between safety and motion efficiency is realized.

The application provides a UAV recovery path adaptive planning method and system based on air-ground cooperation, relates to the technical field of robot motion planning, and comprises the following steps: constructing a three-dimensional grid map of a task environment, acquiring the positions of a UAV and an unmanned vehicle and the maximum flight distance of the UAV; determining the responsibility distribution of the UAV and the unmanned vehicle in a recovery task based on the positions of the UAV and the unmanned vehicle and the maximum flight distance of the UAV, and obtaining an optimal UAV recovery scheme; selecting a corresponding path planning mode according to the optimal UAV recovery scheme, and determining the starting point, the ending point and the dimension of the search space of path planning based on the positions of the UAV and the unmanned vehicle; executing a path planning algorithm according to the selected path planning mode, and generating a final recovery path; and through air-ground cooperative decision and cross-dimension bidirectional joint path planning algorithm, the application realizes safe and efficient recovery of the UAV under the constraint of electric quantity, and significantly improves the task success rate and the system cooperation efficiency.

The invention provides an improved CILQR-based intelligent tracked robot motion planning control method and system, and constructs a motion planning control framework which comprises four core modules including an A * algorithm, an improved discrete point quadratic smoothing algorithm, an improved CILQR algorithm and an LQR controller. According to the framework, a motion planning task from a starting point to a target point can be realized based on an environment map fusing static prior information and dynamic real-time information, continuous curvature, safety and no collision of a planned trajectory are ensured, kinematics constraints of an intelligent tracked robot are met, and a finally generated trajectory can be accurately controlled through an LQR controller.

This disclosure provides a motion planning method, apparatus, computer device, and storage medium for robots, applied to each robot in a robot swarm; each robot maintains its own local factor graph; the local factor graphs maintained by the multiple robots constitute a global factor graph; the method includes: in response to a state update event corresponding to the current robot being triggered, determining a trigger domain, and determining a replanning subnet from the current global factor graph based on the trigger domain; performing message passing processing based on Gaussian belief propagation in the replanning subnet with the goal of reducing the energy change of the local factor graph corresponding to the current robot, to obtain an updated local factor graph corresponding to the current robot; determining the confidence distribution information of the variables corresponding to multiple variable nodes in the local factor graph of the current robot based on the updated local factor graph of the current robot; and determining the motion replanning result of the current robot based on the confidence distribution information.

The application relates to a robot motion planning method based on a greedy algorithm and an improved hybrid A* algorithm, and the method comprises the following steps: determining a traversal order of a wall-climbing robot for traversing each detection area by using the greedy algorithm, specifically comprising the following steps: selecting a detection area as a starting point; selecting a detection area closest to the current detection area and not traversed as a next detection area; adding the selected next detection area to a path and marking the detection area as visited; continuously selecting a detection area closest to the current detection area and not traversed as a next detection area until all detection areas are marked as visited; returning to the starting point; and determining the traversal order; and planning a path between adjacent detection areas by using the improved hybrid A* algorithm according to the traversal order. The method has the beneficial effect that the greedy algorithm and the improved hybrid A* algorithm are used to provide a shortest safe path that meets kinematic constraints and can be tracked for the wall-climbing robot.

This application discloses a robot motion planning method, chip, and robot applied to the bottom of furniture. The robot motion planning method includes: when the robot performs a bow-shaped movement, when the robot scans two target support parts on the bottom of the target furniture using a lidar, a scene trigger area corresponding to the target furniture is set based on the two target support parts. Then, the robot enters the scene trigger area at least twice and walks along the line connecting the two target support parts to prevent getting trapped in the hollow area between the two target support parts. During the process of the robot entering the scene trigger area at least twice, it collides with the two target support parts or moves away from the minimum obstacle avoidance distance. Whenever the robot collides with a target support part or moves away from the minimum obstacle avoidance distance, the robot adjusts its movement direction to walk along the line connecting the two target support parts and maintain the bow-shaped movement.

This invention provides a robot motion planning method, apparatus, device, and medium based on a diffusion model. Addressing the shortcomings of existing motion planning methods, such as high dependence on initial solutions, difficulty in balancing planning efficiency and smoothness, and poor generalization ability of learning models, this method proposes an end-to-end framework integrating prior learning and posterior guidance. After acquiring state data and distributing it according to function, the method uses a diffusion model for multi-step iterative denoising starting from random noise. The core of this invention lies in the following: in each denoising time step of trajectory diffusion, the control points of the current intermediate noisy trajectory are extracted and represented by B-spline low-dimensional parameterization; multi-objective comprehensive cost and gradient are constructed in real time based on multi-source states; and the cost gradient is incorporated as a guiding signal into the posterior sampling to guide the denoising update direction of the network. This invention deeply nests dimensionality reduction and cost guidance within a single iteration, enabling the rapid generation of multimodal smooth motion trajectories for adaptive dynamic obstacles without the need for post-processing smoothing.

The precise control method and device for the end of a robot based on a segmented motion planning strategy belong to the technical field of hyper-redundant robot motion planning and control. To solve the problem of low motion accuracy at the end of existing hyper-redundant robots, the hyper-redundant robot is divided into a base, a neck, and a head on the kinematic chain in the present invention. The base is kinematically designed using a backbone curve, and the joint angles of the base are calculated through discretization. Then, the workspace of the head of the hyper-redundant robot is calculated, and the center of the flexible workspace is determined. The desired position and orientation of the end reference coordinate system for the neck are obtained from the desired pose of the end link of the head and the flexible workspace of the head, and the joint angles of the neck and the head are calculated using an optimization algorithm. It is applicable to the end control of hyper-redundant robots.

The application discloses a rigid-flexible coupling rope traction parallel robot motion planning method, equipment and medium, and belongs to the field of rope motion planning. The method comprises the following steps: step 1, a kinematics model and a statics model of the robot are established; step 2, a performance index is constructed based on the established model; step 3, under the known target pose of an end effector, the optimal target pose is obtained by optimizing and solving the target pose through a particle swarm algorithm using the performance index; step 4, a low-dimensional proxy model is constructed based on the established model and the performance index; step 5, the optimal target pose is used as a whole body motion planning target, a path in a mobile platform space and a matching path in a mechanical arm joint space are generated by using the low-dimensional proxy model and a hierarchical planning algorithm, and a whole body motion path is obtained; and step 6, the whole body motion path is processed by using a minimum impact trajectory optimization algorithm, and a whole body motion trajectory for driving the robot is generated. The application can consider obstacle avoidance safety and mechanical stability, and improve quality and execution reliability.

This invention provides a path planning method for industrial robotic arms based on potential field perception and experience-based adaptation, relating to the fields of robot motion planning and intelligent manufacturing technology. Based on the path planning framework of the RRT-Connect algorithm, this method constructs a bidirectional search tree within the search space. It uses the calculated safety margin of the direction-aware space as the initial environmental safety margin for nodes, employs an artificial potential field lateral deflection direction guidance strategy to determine the expansion direction, and uses a parameter adaptive adjustment method with a clearly bounded mapping relationship to adaptively determine the node expansion parameters based on the environmental safety margin of the parent node. This achieves efficient and stable collision-free path planning in complex assembly environments. This method is applicable to path planning problems for multi-degree-of-freedom industrial robotic arms and can significantly improve path planning efficiency and path quality in complex assembly environments.