66 results about "Robot path planning" patented technology

The application belongs to the technical field of mechanical arm motion planning, and provides a kind of adaptive sampling and multi-channel expansion mechanical arm path planning method, first, the environment obstacle set is constructed, the point to obstacle distance, the clearance configuration and the segment level minimum clearance are established, and the unified safety criterion is constructed;Subsequently, according to the approximation state of bidirectional search tree, the sampling window is adaptively updated, and the random reference sample is generated in combination with the stagnation detection mechanism;On this basis, the hierarchical multi-channel expansion mechanism of direct pointing expansion, bypass expansion, potential field wall expansion and global bottom expansion is used to generate candidate new nodes;When a feasible new node is generated, it is connected to the current search tree and real meeting detection is carried out, if the double trees are successfully connected, path backtracking, splicing, simplification and full path safety review are further executed, and the final executable path is output.The application can improve the safety, search efficiency and environmental adaptability of the path planning of the mechanical arm in the complex limited environment.

The application belongs to the technical field of path planning of inspection robots, and discloses a cage breeding poultry house autonomous inspection robot path planning method and system. First, a hierarchical cost map including a static map layer, a dynamic obstacle layer, an inflation layer and a risk field layer is established in the global layer, and an orthogonal cumulative occupancy statistical area is used to distinguish a cage passage area and a turning area, and the inspection requirement of "driving along the center line of the passage" is explicitly coded into the risk field. In the search stage, a multi-dimensional constraint adaptive A* algorithm is used, the cumulative cost, a dynamic weighted heuristic item and a risk cost are jointly modeled, and after the initial path is obtained, the collinear redundant nodes are removed. In the local layer, the key nodes are taken as local targets, a 2.5D speed constraint coupled with lifting stability is introduced, an adaptive sampling resolution based on environmental congestion degree and a multi-vertex Jacobian constraint angular velocity correction for dynamic obstacles are introduced, the constant curvature trajectory of the traditional DWA is expanded into a variable curvature trajectory, and efficient obstacle avoidance for dynamic obstacles is realized.

The application relates to the technical field of robot path planning, and discloses a building logistics robot carrying path optimization method and system based on obstacle avoidance, which comprises the following steps: acquiring an environment point cloud to construct a three-dimensional physical attribute grid map containing flatness and friction coefficient; extracting an unknown obstacle bounding box and performing cross-stage matching with a model database to grade a completed building in advance and extract construction process attributes; using an exponential decay formula to radiate outward to correct surrounding grid physical parameters and generate an environment degradation gradient; combining the current total mass and speed of the robot to calculate a maximum safe curvature limit value, cutting off branches exceeding the limit value in path searching to generate a local detour path; reporting the path to the cloud after the vehicle passes, and calculating a safe relaxation degree according to the wheelbase and total mass of the subsequent vehicle to determine whether to issue a detour template. The application solves the problems of obstacle avoidance deadlock caused by construction progress and robot side slip out of control, and reduces the total computing power consumption of multi-machine cooperation.

The application discloses a kind of underwater defect detection robot path planning and tracking control method based on multi-data fusion, to solve the problem of poor positioning accuracy, unstable path tracking and insufficient defect approaching ability in GPS denial environment.The method uses improved artificial bee colony algorithm to optimize BP neural network assisted Kalman filter, fuses INS, DVL, electronic compass and depth gauge data, realizes high-precision integrated navigation;Secondly, combined with global "bow" shape coverage and local pure tracking approaching strategy, generate full coverage scanning path and fine approach to high priority defect points;Finally, based on pre-defined time sliding mode controller, cooperate disturbance observer and adaptive mechanism, realize the fast robust tracking of path.Experiments show that the positioning error is only 0.15m after DVL failure, the local approaching accuracy is 0.05m, the path tracking convergence time is 5s, the steady-state deviation is 0.03m, which is significantly better than the traditional method.The application is suitable for automatic defect detection of underwater structures such as bridge pier, dam, submarine pipeline, etc.

The application discloses a path planning method, device and system of a robot, and a readable storage medium. The path planning method comprises the following steps: performing path planning on a first robot and a second robot to obtain a first planning path of the first robot and a second planning path of the second robot; performing head-on motion conflict detection on the first robot and the second robot according to the first planning path and the second planning path; the head-on motion conflict detection comprises robot size conflict detection; in response to the existence of head-on motion conflict between the first robot and the second robot, seeking an avoidance node for the first robot or the second robot, and performing action constraint on the first robot or the second robot; and performing path planning on the first robot and the second robot again according to the avoidance node and the action constraint until the final planning path of the first robot and the second robot is obtained. In this way, the problem of continuous conflict resolution in a space-limited scene can be reduced.

The application discloses a path planning method, an electronic device and a storage medium. The path planning method is applied to a robot, and the path planning method comprises the following steps: acquiring first point cloud data of a traction device in a to-be-trucked shelf and a first pose of the robot for collecting the first point cloud data, the to-be-trucked shelf further comprises a shelf, the shelf is movably connected with the traction device, and the traction device comprises a traction ring; determining a first target pose of the traction ring relative to the robot according to the first point cloud data; and determining a first target path according to the first pose and the first target pose, the first target path being used for the robot to truck the shelf through the traction ring in the traction device. According to the above scheme, the accuracy of the planned first target path can be improved.

This invention proposes a robotic arm obstacle avoidance path planning method based on an improved ant colony algorithm, relating to the fields of robot path planning and intelligent optimization algorithms. This method endows the joint configuration space with a non-uniform metric structure induced by a metric tensor G(q), where G(q) is derived from the normalized joint inertia matrix W. I The algorithm consists of three parts: the singularity gradient outer product term and the obstacle spacing gradient outer product term. Local geodesic distances are approximated using the mean of the metric tensors at both ends of the node. All edge weights are pre-calculated and cached during the PRM graph construction phase. The ant colony uses the reciprocal of the geodesic distance as a heuristic function, drives non-uniform pheromone evaporation using the normalized value of the metric tensor trace increment, and uses the weighted sum of geodesic length cost and inertia-weighted velocity mutation penalty as the comprehensive path cost. The beetle whisker algorithm performs pre-search and completes non-uniform pheromone initialization under geodesic metrics. This method effectively improves path safety, continuity, and dynamic adaptability.

The present application relates to the technical field of agricultural robot path planning, and particularly relates to a water product breeding feeding robot path planning method and system based on energy consumption and safety joint constraint, the steps of the method comprising: constructing an environment grid map and calculating a terrain gradient vector field; constructing a single-step multi-dimensional comprehensive cost model; establishing a safety constraint judgment mechanism, including longitudinal slope constraint judgment and transverse stability constraint judgment; performing global search on the environment grid map, always preferentially expanding the candidate grid node with the minimum comprehensive evaluation cost in the search process, and searching for a target discrete path of the water product breeding feeding robot under the premise of meeting the safety constraint; performing parameterized smoothing processing and secondary safety verification process on the key points of the target discrete path, calculating the linear velocity and angular velocity reference control instructions of the water product breeding feeding robot at each time, and performing feeding operation along the smoothed path trajectory. The present application can reduce the total energy consumption of operation and improve driving safety.

The application discloses a robot path planning method and device based on improved JPS and Theta, and belongs to the technical field of robot path planning. Firstly, warehouse environment data is acquired; subsequently, an improved JPS-Theta fusion algorithm is used to generate an initial conflict-free discrete path for each robot, the process is optimized by introducing a direction guide factor and an adaptive weight optimization heuristic function, and a line-of-sight checking mechanism is combined to reduce path turning; then, the direction information of the initial path is encoded into a guide factor and injected into the bottom search of an enhanced conflict-based search algorithm (ECBS), a global conflict-free discrete path is generated through high-level conflict tree detection and bottom path re-planning; finally, trajectory optimization is carried out for the discrete path, and a continuous smooth trajectory meeting the dynamic constraint is finally obtained. The application effectively improves the search efficiency and trajectory execution safety in a complex environment.

This invention discloses a state-aware obstacle avoidance path planning method and apparatus for ultrasonic reinforcement robots, belonging to the field of ultrasonic reinforcement robot path planning technology. The method includes: in the exploration phase of new nodes, using the successful states of the most recent W iterations of node expansion, calculating a trapping index to characterize the current search difficulty, and constructing an adaptive control factor using the trapping index to dynamically map the sampling probabilities of different sampling strategies; selecting the sampling strategy with the highest sampling probability, and adaptively expanding the step size using the obstacle's distance field and the trapping index to generate new nodes based on the expansion step size and the selected sampling strategy; and using a parent node selection and reconnection mechanism to iteratively optimize the search tree multiple times to obtain the collision-free optimal path. This invention exhibits good search performance and robustness in complex 3D environments such as multi-obstacle and narrow spaces, and can quickly obtain high-quality collision-free paths.

The application relates to the field of livestock automation technology and discloses a livestock pushing robot path planning method based on AI processing. Environmental data is collected by using a multi-source perception module, attribute features such as physical density and accumulated volume of forage grass are extracted through a semantic analysis model, and the attribute features are mapped as motion constraint conditions of the robot. A real-time coupling control instruction containing a pose and a push plate deflection angle is generated by an on-board computing unit based on physical field constraints, so that the robot travels along a fluid dynamics path and adjusts the push plate posture in linkage. In addition, the method further comprises an anti-slip correction step based on an optical flow, a crab-shaped approach instruction is generated to offset ground skidding, and a global planning step based on a V2X space-time node, an S-shaped buffer or a tunnel acceleration path is generated to realize continuous operation across regions. The application solves technical problems such as pushing blockage, motor overload, skidding on icy and snowy roads and discontinuous operation across regions in a complex pasture environment.

The application relates to a mobile robot path planning method based on an improved bidirectional RRT algorithm, and belongs to the technical field of mobile robot path planning. a b The bidirectional random tree T a b is constructed by taking a starting point and an ending point as root nodes, adaptive sampling is realized by adopting a directional deviation heuristic function containing a bias angle sampling strategy, node distribution is optimized by combining a redundant point removal strategy based on adaptive adjustment of an environmental complexity threshold, and the random tree is directionally expanded by introducing an improved artificial potential field method. The application can quickly generate a safer optimal path which is shorter and smoother, significantly improves the efficiency and robustness of path planning, and is suitable for complex scene path planning tasks of various mobile robots such as service robots and unmanned toy cars.

A method for path planning of a driving robot by using a feature map, includes: a feature map generation step of generating a feature map including multiple nodes and multiple blocks on the basis of a topological map for a random space; a primary block search step of acquiring a departure node and an arrival node from among the multiple nodes, and designating a departure block and an arrival block on the basis of the departure node and the arrival node; a secondary block search step for determining the drivability of each of multiple subsequent blocks placed after the departure block, and performing path designation; and a termination step of terminating path planning if a random subsequent block is the arrival block.

The present application belongs to the field of multi-robot path planning, and specifically relates to a double-layer multi-robot path planning method based on state search and priority. The algorithm uses a deep-first search guided iteration framework in the upper layer to provide full-view planning, and uses a priority-based method in the lower layer to avoid obstacles. Meanwhile, the lower layer search adds a turning penalty factor in the single-robot path cost function to reduce the number of turns; combines priority inheritance and expands the time window considered when avoiding conflicts to reduce the number of conflicts; and pre-processes the map to search out narrow passages, which are regarded as mutual exclusion quantities, and only allow one robot to pass through to avoid the situation of avoiding after entering the passage. The present application has wide application value and can meet various tasks and constraint conditions, and has many practical applications in flexible manufacturing systems such as intelligent workshops, logistics centers, automated warehouses and the like. The present application has important significance for improving system efficiency and reducing system cost.

A device may include an asset intelligence module configured to interpret a physical description of an asset for inspection. A device may include an inspection execution module configured to determine a pathing trajectory for an inspection robot in response to the physical description. A device may include a means for moving the inspection robot on the asset in response to the pathing trajectory.

The application belongs to the technical field of path planning, and discloses a robot path planning method based on combination of particle swarm, ant colony and A-Star algorithm, which comprises the following steps: generating a grid map, obtaining positions of obstacles and feasible areas in the grid map, constructing an accessible distance matrix, determining starting positions and target positions of each robot in the grid map in a robot cluster; and using the particle swarm, ant colony combination algorithm and A-Star algorithm to perform path optimization planning on the robot cluster based on the accessible distance matrix, the starting positions and the target positions of the robots in the grid map, so as to obtain an optimal path. The application combines the ant colony algorithm, the algorithm and the simulated annealing algorithm with the particle swarm algorithm, and performs robot obstacle avoidance path optimization, so that the robot can quickly find a path from the starting point to the end point while completing obstacle avoidance.

The application discloses a multi-agent dynamic real-time path planning method and device based on signal timing logic, and relates to the technical field of control methods. The method comprises the following steps: acquiring multi-robot path planning task information and a preset space-time constraint task demand; constructing a multi-robot task specification based on signal timing logic, and defining a corresponding robustness evaluation function; constructing a comprehensive path cost function based on the robustness evaluation function; introducing the path cost function into a path expansion and reconnection process based on a real-time rapid expansion random tree algorithm, updating a search tree structure, and generating a candidate path meeting environment constraints and timing constraints; planning paths for robots in sequence according to a preset multi-robot cooperative planning strategy, taking the path information of the robots that have been planned as dynamic obstacles and performing progressive cooperative obstacle avoidance, and outputting a conflict-free trajectory adaptive to a dynamic environment. The application can solve the path planning problem in a multi-agent scene.

This invention discloses a multi-robot path planning method in complex environments based on multi-strategy self-learning, belonging to the field of multi-robot path planning technology. This method employs the Spiral Forward Adaptive Multi-Objective Artificial Hummingbird Algorithm (SFA-MOAHA) for path planning, enhancing the algorithm's local exploitation and global exploration capabilities by fusing a spiral approximation strategy and a forward fusion learning strategy. Simultaneously, a deep reinforcement learning mechanism is introduced to adaptively select the optimal search strategy based on population state and environmental feedback during the iteration process. Compared to existing technologies, this invention can efficiently solve multi-robot path planning problems in complex dynamic environments, generating high-quality collision-free cooperative paths. It not only effectively avoids collisions between robots and between robots and static / dynamic obstacles, but also significantly shortens the overall path length and improves task completion efficiency, thereby enhancing the safety and robustness of the multi-robot system.

The application discloses an airport mowing robot path planning method and system, and the method comprises the following steps: preprocessing an airport map to obtain airport lawn data; performing BCD decomposition on the lawn to obtain a plurality of convex polygon units and generate scanning lines; calculating the shortest path between all scanning line endpoints and constructing a distance graph; using the pheromone update and heuristic search mechanism of an ant colony optimization algorithm to solve the optimal scanning line access sequence and identify a turning area; using a Hybrid A * search algorithm to perform grid search, combining a heuristic fusion strategy and a robot kinematics model to generate a smooth turning curve that meets the minimum turning radius constraint; splicing all straight line segments and smooth turning curves to output the final airport mowing robot path. The application realizes reasonable division of the airport lawn, reduces the difficulty of path planning, avoids the problem that the mowing robot cannot turn due to a too small turning radius, and improves the efficiency of path planning.

This application provides a vehicle cleaning method, apparatus, device, storage medium, and program product. The method acquires a 3D model of the vehicle to be cleaned; extracts features from the 3D model to obtain multiple facets and feature data corresponding to each facet; segments the facets based on preset orientation categories and feature data to obtain a set of transition regions and a set of candidate regions for different orientation categories; performs region growing on the transition region set and the candidate region sets for different orientation categories to obtain a set of target regions for different orientation categories; adds labels to the target region sets for different orientation categories based on preset mapping relationships to obtain target segmentation results; and cleans the vehicle based on the target segmentation results. This application, by combining feature data and region growing, ensures that the segmentation results conform to the vehicle's structural logic, meets the continuity requirements of the car wash robot's path planning, and improves the efficiency of vehicle cleaning.

This invention discloses an intelligent puncture path planning method for interventional surgical robots, belonging to the field of interventional robot path planning technology. The method acquires multimodal medical images of the patient's puncture site, completes organ tissue segmentation and three-dimensional reconstruction, generates a digital twin anatomical model, and calibrates basic puncture parameters; constructs a respiratory phase tracking model, combines the basic puncture parameters to lock the respiratory cycle and real-time respiratory phase, and outputs the three-dimensional predicted coordinates of the puncture target point for a pre-set duration; acquires needle insertion force data through a six-dimensional force sensor, calculates tissue compression deformation and needle body flexible bending offset in real time, and generates a feedforward compensation vector; fuses the three-dimensional predicted coordinates and the feedforward compensation vector to generate a dynamic puncture path, performing continuous needle insertion without interruption throughout the entire process. This invention improves upon the shortcomings of traditional static path planning, which cannot adapt to respiratory displacement and tissue deformation, effectively improving puncture positioning accuracy and is suitable for minimally invasive interventional puncture surgery scenarios.

This invention discloses an indoor robot path planning method and system based on navigation feedback closed-loop calibration. The method includes: constructing an initial representation of the local semantic environment based on RGB images, depth images, robot pose information, and local obstacle observation information; generating a local path based on the current local environment representation and driving robot movement; using navigation behavior anomalies generated by the robot during actual operation as feedback signals to perform reverse calibration of the local environment representation; extracting topological information from the local passage space and dynamically adjusting semantic risks to form a total risk field for path planning; and performing gated replanning and adaptive adjustment based on the total risk field. By introducing a navigation execution feedback-driven closed-loop calibration mechanism, this invention not only improves the consistency between the local environment representation and the actual passage state but also, by combining dynamic adjustment of topological semantic risks and gated replanning control, further enhances the stability and safety of path planning in complex indoor environments.

This invention relates to a mobile robot path planning method for optimizing safe distance, belonging to the field of mobile robot path planning technology. The method introduces a soft and hard constraint point judgment method to expand new nodes in the traditional A* path planning method, and adds turning penalty cost and soft and hard constraint point cost during the cost calculation process. After obtaining the path nodes, the path curve is smoothed and optimized using Bézier curves to achieve the optimal planned path while ensuring motion safety.

Embodiments of the present application relate to the field of robot motion control, and particularly to a robot path planning method and device and electronic equipment. The method comprises: for a target joint in each joint of a redundant robot, determining a candidate angle of the target joint; for each candidate angle, determining a first angle of each joint except the target joint in the case of fixing the target joint at the candidate angle, and determining a length of a motion path of the redundant robot according to the candidate angle and each first angle; determining a target length from all the lengths determined, and performing path planning for the redundant robot according to the candidate angle corresponding to the target length and each first angle. The technical scheme of the embodiments of the present application can successfully determine a relatively optimal joint angle for the redundant robot, and then realize path planning.

This invention provides a path planning method for a robotic vacuum cleaner, a robotic vacuum cleaner, and a virtual device, applied to a robotic vacuum cleaner. The method includes: acquiring a regional map image of the area to be cleaned, and determining cleaning reference lines on the regional map image based on the regional information of the area to be cleaned; determining key points on the regional map image, and dividing the regional map image into multiple sub-regional map images based on the key points; determining the inter-regional access order of each sub-region corresponding to each sub-regional map image, and determining the intra-regional access order within each sub-regional map image based on the inter-regional access order and the cleaning reference lines; and planning the cleaning path of the robotic vacuum cleaner regarding the area to be cleaned based on the intra-regional access order and the inter-regional access order. This achieves reasonable and efficient path planning for the robotic vacuum cleaner, avoiding problems such as path duplication, missed cleaning areas, or improper cleaning order.

This invention discloses a vision-based multi-robot path planning system for welding planar sections of ships. The invention relates to the field of shipbuilding technology and includes a multispectral vision inspection module mounted on a gantry, comprising a laser scanning sensor and an infrared thermal imager for real-time acquisition of three-dimensional point cloud and temperature distribution data of the ship's planar sections. This vision-based multi-robot path planning system for welding planar sections of ships fundamentally solves the problem of inaccurate path planning caused by assembly errors, thermal deformation, and complex working conditions in the welding of large ship planar sections through real-time multispectral vision perception and a material-adaptive dynamic correction mechanism. By employing a composite correction vector and spatiotemporal grid collaborative prediction technology, the system effectively improves weld trajectory accuracy, ensuring that weld leg dimensions strictly meet standards in high-difficulty processes such as fillet welding and vertical welding of the largest workpieces, eliminating defects such as porosity and cracks, and significantly reducing rework rates and quality risks.

This disclosure provides a motion optimization obstacle avoidance control method and system for vehicle-like robots, relating to the field of robot path planning and obstacle avoidance technology. The method includes: defining the state space of the vehicle-like robot; planning the global path of the vehicle-like robot; fitting the sparse state sequence using Hermite curves to obtain a time-space continuous trajectory; establishing a collision model between the vehicle-like robot and obstacles; transforming the geometric separation conditions between the robot and obstacles into a set of continuously differentiable nonlinear algebraic constraints based on duality theory; using the time-space continuous trajectory as a reference trajectory; and constructing a trajectory optimization objective function. This function can solve for the motion control quantities of the vehicle-like robot while satisfying its dynamic constraints, input constraints, and acceleration smoothing constraints, thus achieving obstacle avoidance planning and control of polygonal obstacles. This disclosure enables real-time, full-size robot planning and obstacle avoidance control.

This invention relates to a robot path planning method and system based on optical vision, comprising the following steps: acquiring environmental images of the robot's environment and robot motion information; obtaining a dense 3D point cloud map based on the environmental image through contrast enhancement, signal extraction, decoding, and interpolation optimization; generating a semantically labeled 3D point cloud map based on the dense 3D point cloud map and a color RGB visible light environmental image through background culling, image tracking, semantic recognition, and clustering; estimating the robot's motion state based on the semantically labeled 3D point cloud map and robot motion information, performing global and local path planning, and obtaining a local path after trajectory smoothing; and converting the local path into the robot's actual actions by combining the robot's motion state. This invention effectively achieves a balance between cost control and performance improvement through an active optical vision perception architecture and multi-algorithm collaborative design.