17 results about "Dynamic window approach" patented technology

The application relates to an unmanned vehicle adaptive path planning method based on a dynamic window method and a proximal strategy. First, an agent-environment interaction model for an unmanned vehicle is constructed, a proximal strategy optimization learning (PPO) model based on an actor-critic framework is established, a reward function is defined according to the principle of a dynamic window method algorithm (DWA) and main evaluation elements, model parameters in the input layer, the output layer size, the number of hidden layers and the number of neurons are determined, and a DWA-PPO deep reinforcement learning model is constructed. Then, the established DWA-PPO deep reinforcement learning model is used for iterative training, and finally, a network model capable of representing the potential relationship between surrounding environment information and evaluation function weight parameters is converged, and the construction of the adaptive PPO-ADWA algorithm is completed. Finally, the feasibility and effectiveness of the unmanned vehicle adaptive path planning strategy based on the PPO-ADWA are verified through simulation comparison experiments.

This invention relates to the field of obstacle avoidance technology for mobile robots, specifically a reinforcement learning-based obstacle avoidance method for mobile robots guided by a large language model (LLM). The method includes: using a large language model (LLM) to guide the generation of scene vectors reflecting scene features; and constructing a low-dimensional matrix group conforming to robot dynamics constraints using the Dynamic Window (DWA) method, which together with the scene vectors constitutes the state space for reinforcement learning. This structured, low-dimensional state representation significantly reduces the learning burden of the policy network, enabling the policy to quickly distinguish key states and reduce ineffective exploration in the early stages, thereby reducing training costs. Compared to pure reinforcement learning, which relies on a simulation environment, generating different scene vectors through LLM allows for transfer between different scenes, improving the generalization ability of the method.

The application provides a hoisting path intelligent planning method, comprising the following steps: S1, crane motion state and environment modeling; S2, construction of a space-time sequence of a historical trajectory of a work equipment; S3, position recommendation based on a space-time attention network (STAN); S4, neural network prediction dynamic window method weight; S5, local path planning based on a weighted DWA algorithm; S6, real-time obstacle avoidance and closed-loop updating. The application mines the time sequence and spatial correlation characteristics in the historical trajectory of the crane through a space-time attention mechanism, and realizes real-time path planning and obstacle avoidance in combination with a dynamic window method, and simultaneously uses a neural network to adaptively adjust the weight of a dynamic window evaluation function, thereby improving the path planning safety, stability and operating efficiency of the crane in a complex outdoor work environment.

The application discloses a kind of based on patrol trolley robot police training intelligent tracking evaluation method and system, it is related to intelligent monitoring technical field.The method includes: demarcate training area and robot activity area in training site, and patrol trolley robot is based on laser radar and dynamic window method (DWA) in activity area autonomous movement and real-time obstacle avoidance;Through the AI vision module and laser radar carried, the preset training team is accurately identified and locked, and the pan-tilt and the moving chassis are controlled to realize stable follow-up shooting, and training video is collected in real time.Further, based on the multi-angle video shot, using the action recognition algorithm based on skeleton key point detection, the team action specification of training team and the action specification of individual student are intelligently evaluated, and the evaluation report containing quantitative score, deviation positioning and rectification suggestion is automatically output.The application solves the problem that traditional police training evaluation relies on manual, strong subjectivity and low efficiency, realizes the automation tracking of training process, multi-angle recording and intelligent quantitative evaluation.

The application discloses a kind of unmanned plane omnidirectional obstacle avoidance methods based on millimeter wave radar and binocular laser module, forward binocular laser obstacle avoidance module is set below unmanned plane center disc, and obstacle detection within 40m range is realized by double camera image processing plus laser ranging;Millimeter wave radar module is set in upper, lower, rear, left and right 5 directions below unmanned plane center disc, and obstacle detection within 40m detection distance can be realized.The omnidirectional obstacle avoidance method of unmanned plane of the application adopts omnidirectional detection of obstacle avoidance detection module, whether unmanned plane and obstacle will collide is judged using collision detection algorithm, if collision will be generated or dangerous approach will be generated, unmanned plane route is re-planned using velocity field method and dynamic window method, and omnidirectional obstacle avoidance function is realized.The unmanned plane obstacle avoidance mode of the application can be upgraded, the optimization of unmanned plane omnidirectional obstacle avoidance is realized, the effect of obstacle avoidance is good, safety is high, and omnidirectional obstacle avoidance detection has the advantages of long detection distance, stable detection performance and the like.

The application discloses a path planning method and device based on federated learning and a medium, and relates to the technical field of intelligent transportation and automatic driving. The method comprises the following steps: performing self-checking on a sensor module and a communication module of a vehicle and loading dynamic parameters; extracting a structured data set from local historical driving data and uploading the structured data set to a cloud; receiving a lightweight global path planning model issued by the cloud; based on real-time traffic data, constructing a lightweight digital twin, and in the lightweight digital twin, decoupling verification and dynamic conflict detection are performed on a global path generated by the lightweight global path planning model; loading a path point sequence after verification, combining real-time sensor data, generating a local path through a dynamic window method, and performing smoothing processing when a path curvature mutation is detected; and recording this decision data and uploading the decision data to the cloud. In this way, the problems of poor dynamic adaptability, difficulty in lightweight deployment of a model, and excessive consumption of communication and computing resources of a traditional path optimization method can be solved.

The application relates to a dynamic path planning method and system for cooperative work of double industrial mechanical arms, which comprises the following steps: firstly, an initial feasible path connecting starting points and target endpoints of the mechanical arms is generated by using a global path search algorithm; secondly, key nodes of the initial path are extracted and estimated occupation time windows are calculated to form space-time occupation information which is published to a shared state pool; then, each mechanical arm retrieves space-time occupation information of other mechanical arms from the shared state pool and takes the information as a dynamic constraint to generate a candidate trajectory segment set meeting cooperative avoidance requirements; finally, a dynamic window method is used to sample and evaluate the candidate trajectory segments in a speed-angle velocity space, and an optimal trajectory segment is selected as a current execution path. The application can improve path planning efficiency in a dynamic scene, eliminate collision risks in cooperative work and realize beat synchronization, thereby effectively solving the problems of low search efficiency, high collision risk and asynchronous beats in double-arm cooperative work.

The application provides an indoor mobile robot hierarchical dynamic obstacle avoidance method based on a human-like decision mechanism, which comprises the following steps: 1, the indoor mobile robot obtains observation and positioning information of obstacles through a sensor, and constructs a multi-model predictor to perform prediction; 2, hierarchical decision based on distance driving is performed, and step 3 or step 4 is executed; 3, global path planning, a global path is generated according to an improved APF method, and the prediction information of the motion state of the obstacle is fused to control the movement of the robot, and step 1 is re-executed; 4, local obstacle avoidance control, based on the latest global path, an improved dynamic window method (DWA) is used to determine the optimal speed, the movement of the robot is controlled, and step 1 is re-executed. The application can effectively improve the path planning efficiency of the robot in a complex dynamic indoor environment and the response ability of the robot to sudden dynamic obstacles, and has the advantages of low calculation amount, high planning efficiency, automatic response and the like.

This invention discloses a cleaning control method and system for a cleaning robot, belonging to the field of robot control technology. The method includes: initialization and environmental perception, collecting and preprocessing environmental data through a multi-source heterogeneous sensing module; cleaning area division and priority setting, using an improved K-means clustering algorithm to divide sub-regions and evaluate priorities; dynamic path planning, generating optimal paths by combining an improved A* algorithm, the DWA dynamic window method, and an inner spiral algorithm; adaptive cleaning parameter adjustment, adjusting cleaning parameters based on environmental and feedback data; and cleaning process monitoring and closed-loop optimization. This invention solves the problems of low cleaning efficiency, poor quality, and weak environmental adaptability of existing cleaning robots, and has the advantages of accurate environmental perception, efficient path planning, controllable cleaning quality, optimized energy consumption, and low maintenance costs. It can be widely applied in various scenarios such as homes and shopping malls.

The application relates to the technical field of autonomous navigation of mobile robots, and particularly relates to a mobile robot path planning method fusing an artificial potential field and an improved ant colony, which adopts a hierarchical modular architecture, constructs a multi-dimensional cost map, generates a global reference path through an improved ant colony algorithm fusing multi-dimensional cost and direction perception heuristic guidance and dynamic pheromone adjustment, realizes local real-time obstacle avoidance in combination with an improved artificial potential field method and a dynamic window method, establishes a global and local two-way feedback replanning mechanism, and realizes multi-robot distributed path coordination through a dynamic path occupation layer; the mobile robot path planning method fusing the artificial potential field and the improved ant colony significantly improves the convergence speed and path quality of path planning, solves the defects of a traditional method that a target is not reachable and is prone to falling into local optimization, realizes global optimization and local real-time collaborative planning in a complex dynamic environment, and greatly improves the robustness of mobile robot navigation and multi-robot collaborative operation efficiency.

The application relates to the technical field of intelligent control and remote operation and maintenance of warehouse robots, and discloses an intelligent carrying navigation control and remote monitoring system based on 3D laser sensing, which comprises a 3D laser sensing module, a real-time positioning and map construction module, a path planning and dynamic obstacle avoidance module, a hierarchical cooperative motion control module, a remote monitoring and operation and maintenance management module and a cloud interaction module. Three-dimensional point cloud data of a complex warehouse environment is collected by a 3D laser radar, centimeter-level real-time positioning and three-dimensional mapping are realized by combining a laser SLAM and an AMCL algorithm, global path planning and dynamic obstacle avoidance are completed by adopting an A algorithm and a dynamic window method, cooperative closed-loop control of navigation and execution is realized by using a built-in three-closed-loop PID adaptive control algorithm, and multi-machine cluster scheduling and remote operation and maintenance are realized through the cloud module.

This invention discloses an intelligent wheelchair autonomous navigation and obstacle avoidance mechanism and its usage method based on multi-sensor fusion. The device includes a wheelchair frame and integrated environmental perception module, core control module, drive execution module, and human-machine interaction module. The environmental perception module includes front and rear radars; the core control module includes a host, a hierarchical power supply management system, and a network switch; the drive execution module includes two wheelchair motors and corresponding motor drive boards; and the human-machine interaction module includes a touchscreen. This invention constructs an omnidirectional horizontal perception field through front and rear dual radars, eliminating blind spots; the host runs synchronous positioning and map building algorithms to achieve autonomous mapping and positioning; and combines global path planning (A / B) and dynamic window method for local obstacle avoidance to achieve full-process autonomous navigation; the hierarchical power supply ensures stable system operation. This invention enables intelligent wheelchairs to achieve autonomous navigation and safe obstacle avoidance in complex environments, improving the independence and safety of user movement.

The application belongs to the technical field of water rescue equipment control, and provides a water rescue robot master control system.The system comprises: a master CPU module; a communication module, integrated with a LoRa command control link and a Mesh self-organizing network data transmission link, forming a dual-means communication system; a navigation positioning module, integrated with Beidou and inertial navigation positioning and fusion vision and hearing positioning; a path planning module, based on a grid map for global path planning; a path tracking module, using an improved integral line-of-sight method for accurate path tracking; a collision avoidance module, fusing a dynamic window method and a speed obstacle method to realize dynamic obstacle avoidance; a propeller drive control module, using a double closed loop control structure to coordinate control of the heading and the speed; and a life buoy throwing control module, used for autonomous decision and execution of life buoy throwing.The application, through multi-module cooperation and advanced algorithm fusion, significantly improves the autonomy, accuracy, collaborative operation ability and reliability of the rescue robot in complex sea conditions.

This invention discloses a big data-based robot management system control method, relating to the fields of artificial intelligence and automated control technology. The method includes: constructing a unified data representation model that integrates four types of heterogeneous data: task semantics, environmental graph, robot dynamics, and communication state; deploying a distributed reinforcement learning agent based on a dual-delay deep deterministic policy gradient algorithm for multi-robot collaborative decision-making; implementing an improved dynamic window method that integrates communication connectivity and social compliance constraints for local path replanning; utilizing a long short-term memory network to predict the state in the next 12 seconds to intervene in conflicts in advance; and designing a standardized control command conversion layer to adapt to various heterogeneous robots. This invention achieves stable collaborative control with a task completion rate ≥97% and a response latency <3 seconds in high-density complex environments, supports hot-swappable access and self-recovery scheduling, and significantly improves system robustness, versatility, and operational efficiency.

The application relates to a high-dynamic-avoidance-obstacle fixed-wing unmanned aerial vehicle (UAV) route planning method and belongs to the field of UAV route planning. In view of the problem that traditional algorithms cannot realize real-time dynamic obstacle avoidance caused by mobile obstacles in part of a proven three-dimensional region and unproven obstacles, the application fuses an improved rapidly-exploring random tree (RRT) algorithm and an improved dynamic window approach (DWA) algorithm; the improved RRT adopts heuristic growth point selection and safety boundary ring enhancement sampling to quickly generate a global static flight path; the improved DWA is based on a UAV kinematics model, introduces overload-velocity space sampling, improves a trajectory evaluation function and a normalization method, and realizes local dynamic obstacle avoidance; and a Dubins path is used to guide the UAV to return to the global flight path. The application enables the UAV to simultaneously cope with dynamic and unknown obstacles, generates a safe, flyable and smooth obstacle avoidance route, and is suitable for complex environments such as emergency rescue and underground exploration.

This invention relates to the field of path planning for autonomous vehicles, specifically to a path planning method for autonomous vehicles based on a fusion of the RRT* algorithm and the Dynamic Window Method (DWA). This method first extracts sampling points based on global planning using the RRT* algorithm to obtain a coarsely optimal path, and then segments the route using these sampling points as target points. Next, the Dynamic Window Method is introduced, using each segmented path as the planning object for local planning. Finally, the planned paths are concatenated again to obtain a smoother global path. This invention optimizes the insufficient smoothness of paths planned by the RRT* algorithm, addresses the shortcomings of dynamic obstacle avoidance capabilities and the uncertainty of target points during the Dynamic Window Method's planning process, and significantly improves both the smoothness of the planned path and the obstacle avoidance capabilities of the autonomous vehicle.

The application discloses a kind of based on improved dynamic window method unmanned plane local path planning method, S1: initialization unmanned plane state, target point and environment map, create the grid map of environment;S2: improvement is carried out to dynamic window optimization algorithm, and the improvement point is: (1) according to the degree of obstacle density, introduce dynamic window parameter adaptive adjustment strategy, dynamically adjust the speed and acceleration range of unmanned plane, simultaneously, the distance of unmanned plane from the end point is as influencing factor, so that unmanned plane slows down automatically when approaching the end point;(2) set dynamic adjustment sampling resolution, optimize the number of candidate paths according to the complexity of environment;(3) introduce sparrow search algorithm, for optimizing the index weight of trajectory evaluation function;S3: the created path grid map is input into improved dynamic window optimization algorithm, and the optimal path is output.The application can be more effectively applied to unmanned plane local path planning, improve the safety and effectiveness of autonomous navigation of unmanned plane in complex environment.