59 results about "Robot motion planning" patented technology

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 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 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.

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.

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 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.

The invention provides an action planning method, device, equipment and medium for an anti-fracture and anti-compression experimental robot, and relates to the technical field of robot motion planning, and the method comprises the steps: generating a first action plan based on the initial pose information of a to-be-tested block and a first mark area on an anti-fracture instrument, driving a mechanical arm of the experimental robot to move the to-be-tested block to a first target position preset at the fracture resistance instrument, and driving the mechanical arm of the experimental robot to progressively push the to-be-tested block to a test station of the fracture resistance instrument for fracture resistance test; obtaining pose information of a fault block formed after the fracture resistance test and a second mark area on the pressure resistance instrument; and driving a mechanical arm of the experimental robot to move the fault block to a second target position preset at the pressure resistance instrument based on the pose information of the fault block and a second action plan generated by the second marking area, and driving the mechanical arm of the experimental robot to push the fault block to a test station of the pressure resistance instrument in a preset inclined pose to perform a pressure resistance test, and high-robustness robot automatic operation is realized.

To support an operator so as to enable the operator to select an efficient procedure when a robot performs specific work, more easily than a conventional one.SOLUTION: Pieces of procedure data indicating a plurality of procedures when a robot performs specific work are given to a computer. The pieces of procedure data are given by an operator manually operating the robot, for example. The computer generates a Petri net 7 according to α algorithm on the basis of the pieces of procedure data.SELECTED DRAWING: Figure 7

The invention relates to a method and device for determining an unreachable area of a reachable ball of a robot arm and a medium, and the method comprises the steps: extracting geometric parameters of a connecting rod in an arm kinematics chain and joint limiting parameters of joints, carrying out the random sampling according to the joint limiting parameters and a preset sampling density, and obtaining N groups of joint angle combinations, the sampling efficiency is high, joint angle combinations are uniformly distributed, then N point cloud data of an end effector of an arm kinematics chain are determined according to geometric parameters and N groups of joint angle combinations, so that the point cloud data are also uniformly distributed, the reachable ball determined according to the N point cloud data is high in accuracy, and the accuracy of the reachable ball is improved. According to the method, M verification points are randomly generated in the reachable ball, and inverse kinematics solution is performed on each verification point to obtain the unreachable points which do not meet the preset constraint conditions, so that the unreachable area can be accurately and efficiently determined according to the unreachable points, and data support is provided for robot motion planning, structure optimization and man-machine cooperation safety design.

This invention provides a motion planning method for a robotic arm used in live-line operations based on reinforcement learning. First, a reinforcement learning agent and environment model are constructed. Then, a hierarchical reinforcement learning architecture is implemented and trained using a multi-agent reinforcement learning (MARL) framework, treating each robotic arm as an agent. A centralized training distributed execution (CTDE) algorithm is used for collaborative policy learning. Finally, the trained policy network is deployed to a physical robot system. During online operation, only forward computation of the neural network is required, achieving millisecond-level inference. This invention reduces the motion planning time from seconds in traditional methods to milliseconds. The learned policy possesses the ability to generalize to unseen spatial locations with zero or few samples, effectively improving the overall safety and standardization of the operation.

The invention belongs to the technical field of robots, and discloses a robot operation target state characterization method and system based on a look-ahead diffusion network, and the method comprises the steps: obtaining the initial state information of an operation object and environment; the target pose distribution of the operation object is modeled through a diffusion model, and the target pose comprises a translation component and a rotation component; receiving a natural language instruction, and encoding the natural language instruction into a condition feature to guide a generation process of a diffusion model; a back diffusion process of the diffusion model is executed, candidate target poses conforming to the natural language instruction are generated, physical feasibility optimization is conducted on the candidate target poses, and final target poses meeting physical constraints are screened out; and outputting a final target pose for robot motion planning and control. According to the method, multi-modal target pose distribution is explicitly generated through the Gaussian mixture diffusion model, and high-precision, controllable and collision-free target state representation is realized in combination with language condition guidance and gravity-driven correction mechanisms.

The invention discloses a mechanical arm self-adaptive grabbing method based on geometric constraint and gravity optimization, and relates to the field of robot motion planning, and the mechanical arm self-adaptive grabbing method comprises the steps that three-dimensional point cloud data of a target object is acquired, and a smooth surface area is segmented through geometric feature analysis; candidate grabbing point pairs meeting normal vector parallel constraint and friction cone constraint are screened; generating a grabbing strategy including the target clamping force and the grabbing pose based on the candidate grabbing point pair; taking the grabbing pose as a target, and generating a motion track from the initial pose of the mechanical arm to the grabbing pose and with optimized joint load by introducing a path planning algorithm of a gravitational torque cost function; and the mechanical arm is controlled to move along the movement track, and control modes are automatically switched according to the distance and tail end force feedback till grabbing is completed. The stability, the energy efficiency and the safety of the grabbing operation of the mechanical arm in the unstructured environment are improved through geometric physical constraint screening of grabbing points, gravity optimization planning of a movement track and self-adaptive variable stiffness control.

This invention relates to the field of robotic arm motion planning, specifically a multi-robotic arm motion planning method based on a successful experience derivation and replay mechanism. Addressing the low training efficiency and difficulty in learning more complex strategies in multi-robotic arm systems using deep reinforcement learning methods, this invention proposes a successful experience derivation mechanism. By modifying a successful experience, it simulates the robotic arms completing tasks in a tighter formation, thereby deriving successful experiences for more difficult tasks. These successful experiences are then used for experience replay and training, improving the learning effect of the robotic arms. The method includes the following steps: building a simulation environment; modeling the multi-robotic arm motion planning problem; generating tasks; environmental interaction; experience derivation; experience collection and policy updating. Through this invention, the robotic arm can directly learn successful experiences for more complex tasks from successful experiences of simpler tasks, explore collision boundaries to a greater extent, improve sample diversity and coverage, and shorten learning time.

A method and system for robot motion planning based on recurrent neural networks (RNNs) are disclosed. The method includes: acquiring a basic environmental point cloud during robot operation; adjusting the basic environmental point cloud in a time series based on the current joint angle and the target joint angle, obtaining a series of environmental point clouds with time indices; establishing a recurrent neural network model to output a series of joint angles with time indices; generating a series of joint positions with time indices for each joint in the robot's base coordinate system from the series of joint angles with time indices; calculating the planning error; adjusting the weights of the recurrent neural network model using a backpropagation algorithm, iteratively reducing the planning error until the iteration ends when the planning error is below a set threshold; and the joint angles with time indices output by the recurrent neural network model are the joint trajectories of the robot. This invention improves the robot's motion planning capability.

The application discloses a kind of multi-modal information fusion's robot motion planning method, equipment and medium, it is related to robot motion control technical field, including in reference path and task coordinate system, in each control cycle, the robot body motion state and environment multi-modal forward observation data are collected and aligned time, calculate multi-modal motion process parameter, obtain multi-modal motion process observation;Based on multi-modal motion process observation and the forward speed and angular velocity of last execution, construct motion process state, predict the motion process state of next control cycle by discrete time update relationship, obtain motion process state prediction;According to motion process state prediction, generate candidate forward speed and candidate angular velocity in preset speed range, calculate process performance index and select the candidate forward speed and candidate angular velocity of process performance index minimum and forward obstacle meet safety requirement as motion planning instruction.The predictable description and adjustment of robot motion process are realized.

The invention belongs to the technical field of water surface cleaning robot control, and discloses a water surface cleaning robot motion planning and control system based on ADRC. The system comprises a multi-dimensional parameter acquisition unit, a coupling parameter calculation unit, a self-adaptive ADRC control unit and a propeller driving execution unit, the multi-dimensional parameter acquisition unit acquires related parameters of a water body and a robot, and the coupling parameter calculation unit generates various correction parameters; the self-adaptive ADRC control unit executes control logic according to the correction parameters and generates a driving signal, and the propeller drives the execution unit to execute driving processing; the coupling parameter calculation unit adopts a specific formula to generate fluid resistance correction parameters; the problem that an existing system is poor in control adaptability is solved, and stable motion control over the robot in the complex water area is achieved.

The invention relates to the technical field of TEB trajectory execution, in particular to a trajectory execution success rate-oriented TEB trajectory online predictability evaluation and selection method, which comprises the following steps: S1, TEB candidate trajectory generation: a robot motion planning system receives a target action instruction and environment sensing data, and sends the target action instruction and the environment sensing data to the TEB candidate trajectory; generating a plurality of candidate trajectories meeting kinematics constraints and obstacle avoidance geometric requirements through a TEB trajectory planning algorithm; s2, track execution feature extraction; s3, constructing and training a lightweight trajectory quality prediction model; s4, track execution success rate online evaluation; s5, selecting an optimal track and triggering execution; s6, iteratively optimizing the model; in order to solve the problem of execution uncertainty of the same-action different-quality trajectories, by extracting the steering angle change rate of the trajectories and the obstacle minimum distance time sequence characteristics, constructing a lightweight prediction model and performing predictive evaluation on the execution success rate of TEB candidate trajectories, the high-quality trajectories and the low-quality trajectories which are both target actions such as right turning and the like are effectively distinguished; and the failure risk caused by low-quality trajectory execution is avoided.

The application provides a robot motion planning method and system based on local sub-target guidance learning, comprising: acquiring a current system state of a robot; inputting the current system state into a pre-constructed policy network to output an action vector; determining an execution mode based on a received rescaling factor; if it is determined that an action execution mode of reinforcement learning is executed, obtaining a scaled action based on the action vector and the rescaling factor, and sending the scaled action to a robot execution mechanism; and if it is determined that a motion planner mode is called, generating a local sub-target state based on the current system state and the action vector, taking the current system state as a starting node and the local sub-target state as a target node, performing local collision-free path planning through a motion planner, and sending a successfully planned path to the robot execution mechanism, so that an optimal balance between the efficiency of global exploration and the safety and reliability of local actions in a complex environment is achieved.

The application discloses a robot motion planning method and device based on a velocity field, equipment and a medium. The method comprises the following steps: generating a candidate point set according to an environment grid of a motion area, and determining a high-density sample set of each sensitive area in the environment grid according to the candidate point set; generating a first sample set, and training an initial velocity field estimation network by using the first sample set; generating a second sample set according to the environment grid and a local obstacle transformation condition, and training the initial velocity field estimation network according to the second sample set; generating a velocity field according to a current motion target of the robot and the trained velocity field estimation network, and determining a discrete trajectory and a relative velocity between each trajectory point in the discrete trajectory according to the velocity field. By using the above technical scheme, the velocity field estimation network can be quickly and accurately established, the network has the perception ability of dynamic changes of the environment, can output a reliable velocity field, and the motion planning efficiency is improved.

The invention belongs to the technical field of robot motion planning and navigation, and provides a trajectory planning method and system for an object in any shape based on a spatio-temporal joint star A. According to the technical scheme, the method comprises the steps that bounding boxes corresponding to all discrete angles and mask information of a robot contour are generated based on a constructed robot polygon model; defining a four-dimensional state space, and initializing a starting point state and a target state; in the four-dimensional state space, aiming at the node with the minimum cost value, applying a predefined motion primitive to generate a subsequent state candidate set; for each subsequent state candidate node, a bounding box corresponding to a current node discrete angle and mask information of a robot contour are extracted, bit operation is executed in a corresponding area of an environment grid map, and whether collision occurs or not is judged according to an operation result; and when a target state is searched or a termination condition is satisfied, backtracking to generate a discrete space-time path, performing interpolation smoothing on the discrete space-time path, and outputting a final executable trajectory.

The application is suitable for the field of robot technology, and provides a motion planning method and device of a robot, an electronic device and a program product. The method comprises the following steps: generating a first trajectory, a plurality of second trajectories and a heuristic region; wherein the range of the heuristic region is smaller than that of a target region; generating a sampling point according to the heuristic region; selecting to update the first trajectory or the second trajectory according to the sampling point; if the second trajectory is updated, updating the first trajectory through the updated second trajectory; guiding the robot to move according to the updated first trajectory, and determining the current position of the robot after the movement; updating the heuristic region, and returning to execute the step of generating the sampling point according to the heuristic region and the subsequent steps until the robot moves to the target position. The sampling points of the method are all located in the heuristic region, so that the obtained motion trajectory is generally short, and the motion efficiency of the robot is improved.

The invention discloses a robot motion planning method and device based on a velocity field, equipment and a medium. The method comprises the following steps: generating a candidate point set according to an environment grid of a motion area, and determining a high-density sample set of each sensitive area in the environment grid according to the candidate point set; generating a first sample set, and training by using the first sample set to obtain an initial velocity field estimation network; generating a second sample set according to the environment grid and the local obstacle transformation condition, and training the initial velocity field estimation network according to the second sample set; and according to the current moving target of the robot and the trained speed field estimation network, generating a speed field, and according to the speed field, determining a discrete trajectory and a relative speed between trajectory points in the discrete trajectory. By adopting the technical scheme, the velocity field estimation network can be quickly and accurately established, and the network has the capability of sensing the dynamic change of the environment and can output a reliable velocity field, so that the motion planning efficiency is improved.

The invention provides a motion planning method and device for a series-parallel hybrid redundant robot, a terminal and a medium. The method comprises the steps that a performance space corresponding to a robot driving joint variable is constructed; in the performance space, motion trajectory planning is conducted on robot driving joint sets which are layered in advance through a forward layering planning strategy, and initial trajectories are obtained; a bias sampling strategy guided by a performance gradient is utilized in each layer of planning to guide bias sampling points to be concentrated towards a high-performance area; performing cross-layer rewiring optimization on the initial trajectory, and performing local refined search in a rewiring point neighborhood by using a virtual sampling strategy to obtain an optimized motion trajectory; and generating a target motion track of the robot based on the optimized motion track. According to the method, the joint configuration of the robot is mapped to the performance space, a strategy of combining forward hierarchical planning and reverse optimization is adopted, a virtual sampling point enhancement mechanism is introduced, and comprehensive optimization of multiple performance indexes is achieved.

The invention discloses a rigid-flexible coupling rope traction parallel robot motion planning method and device and a medium, and belongs to the field of rope motion planning. The method comprises the following steps: step 1, establishing a kinematic model and a statics model of a robot; step 2, constructing performance indexes based on the established model; 3, under the known target pose of the end effector, the performance indexes are used, and the target configuration is optimized and solved through a particle swarm optimization algorithm to obtain the optimal target configuration; 4, constructing a low-dimensional agent model based on the established model and performance indexes; 5, taking the optimal target configuration as a whole-body motion planning target, generating a path in a mobile platform space and generating a matching path in a mechanical arm joint space by using a low-dimensional agent model and a hierarchical planning algorithm to obtain a whole-body motion path; and 6, processing the whole-body motion path by using a minimum impact trajectory optimization algorithm to generate a whole-body motion trajectory for driving the robot. Obstacle avoidance safety and mechanical stability can be considered, and quality and execution reliability are improved.

The application discloses a kind of motion planning methods based on convex optimization smooth DWA, first based on DWA algorithm in velocity space collection speed, simulate the robot motion trajectory within future 3 seconds through these speeds;Then the heading angle deviation of motion trajectory, the distance of trajectory end and terminal, the distance between trajectory and obstacle is evaluated in several aspects, select the optimal one group trajectory;Output this group optimal trajectory angular velocity and linear velocity, construct objective function and bring in angular velocity linear velocity and solve by convex optimization, obtain the angular velocity and linear velocity after smoothing;Finally, by calculating the average absolute error and root mean square error of angular velocity linear velocity sequence before and after optimization, verify that the path after optimization does not change obviously, finally realize robot motion planning.