21 results about "Obstacle avoidance algorithm" patented technology

This invention discloses a control system for an intelligent inspection robot for stormwater and sewage pipelines, comprising a microcontroller unit, a multi-sensor module, an image acquisition module, a communication module, and a power module. Through multi-sensor collaborative perception and intelligent decision-making mechanisms, it integrates multi-dimensional data such as images, distance, and environmental data, and employs adaptive path planning and obstacle avoidance algorithms, making it suitable for autonomous inspection tasks in complex pipeline environments. The image acquisition module integrates a high-definition camera and a WiFi module; the distance detection unit combines ultrasonic and infrared ranging sensors to achieve complementary ranging and improve detection accuracy; the environmental monitoring unit uses gas sensors to detect the concentration of toxic and flammable gases such as ammonia and sulfides in the pipeline in real time, and automatically triggers an early warning when the concentration exceeds the standard. This effectively solves the problems of existing pipeline robots, such as single sensor detection methods, poor environmental adaptability, and insufficient intelligence, improving inspection efficiency and system reliability, and reducing manual maintenance costs.

The application discloses a robot obstacle avoidance method based on model-based and model-free reinforcement learning, belongs to the field of robot navigation, and adds model-based reinforcement learning as a forward-looking module into the framework of model-free reinforcement learning, uses the model-based reinforcement learning to improve the sample data utilization efficiency of the model-free reinforcement learning, and avoids the robot from selecting actions that may cause the robot to be in a dangerous situation by deducing the interaction between the robot and the surrounding environment in a future period of time, uses the model-free reinforcement learning as a main decision algorithm, and reduces the influence caused by an inaccurate environment model; the obstacle avoidance strategy is trained in a two-dimensional simulation environment, the perception module is designed and built offline on a data set, the perception module with the output information in the form of pseudo laser radar data is obtained, and then the obstacle avoidance strategy network obtained in the two-dimensional simulation environment can be combined to realize obstacle avoidance in a real environment. The application improves the performance of the obstacle avoidance algorithm.

The invention belongs to the technical field of aircraft control, and particularly relates to a simulation test system and method for an aircraft obstacle avoidance algorithm. The invention discloses a simulation test system and method for an aircraft obstacle avoidance algorithm. The system comprises a virtual environment modeling device, a dynamics resolving device, an obstacle avoidance logic processing device, a physiological feature modeling device, a stress parameter mapping device, a comprehensive evaluation feedback device and a data monitoring device. According to the method, kinematic parameters are mapped into physiological stress parameters such as gravity overload and motion sickness, and an obstacle avoidance track is fed back and corrected according to a physiological comfort threshold, so that an obstacle avoidance path conforms to the tolerance limit of a human body on the premise of ensuring safety. According to the method, deep fusion of control engineering and aeronautical medicine is realized, obstacle avoidance assessment extends to the field of physiological tolerance from physical safety, the scientificity of simulation assessment of the manned aircraft is improved, and the risk of personnel disability caused by violent maneuver in real flight is reduced.

The application discloses a five-axis mechanical arm obstacle avoidance planning and cross-platform control system based on A* and APF algorithms and relates to the technical field of mechanical arm motion control. In view of the problems that the existing A* algorithm is easy to generate a virtual path exceeding the limit, motion step is stuck, and the traditional potential field method does not cover the anti-collision of the connecting rod and is easy to fall into local optimization, the application builds an improved D-H kinematics model and a standardized simulation environment, proposes an improved A* global planning algorithm with joint physical constraints and quintic polynomial smoothing, and ensures the continuous and smooth trajectory. Meanwhile, the application proposes an improved APF local obstacle avoidance algorithm by introducing a velocity Jacobian matrix to map the repulsive force of the whole arm connecting rod. The system adopts cross-platform communication between the upper computer and the ROS2 bottom layer node, and the smooth trajectory is losslessly issued to the entity mechanical arm for execution. The application takes into account the path feasibility and safety, greatly reduces the hardware impact, and provides a quantitative algorithm comparison and verification mechanism.

The application belongs to the technical field of aircraft control, and particularly relates to a simulation test system and method of an aircraft obstacle avoidance algorithm. The system discloses a simulation test system and method of an aircraft obstacle avoidance algorithm, and the system comprises a virtual environment modeling, a dynamics solving, an obstacle avoidance logic processing, a physiological characteristic modeling, a stress parameter mapping, a comprehensive evaluation feedback and a data monitoring device. The system maps kinematic parameters into physiological stress parameters such as gravity overload and motion sickness, and corrects the obstacle avoidance trajectory according to the physiological comfort threshold feedback, so that the obstacle avoidance path meets the human tolerance limit under the premise of safety. The application realizes the deep integration of control engineering and aviation medicine, extends the obstacle avoidance evaluation from physical safety to the physiological tolerance field, improves the scientificity of manned aircraft simulation evaluation, and reduces the risk of personnel disability caused by violent maneuvering in real flight.

This application discloses a target following method, device, and storage medium based on a mobile device. The target following method includes: in response to receiving a follow instruction for a target object in the current following scene, obtaining the scene complexity of the current following scene; obtaining a follow control quantity for the mobile device to follow the target object according to a preset follow algorithm, and obtaining an obstacle avoidance control quantity for the mobile device to avoid obstacles in the current following scene according to a preset obstacle avoidance algorithm; performing weighted fusion processing on the follow control quantity and the obstacle avoidance control quantity according to the scene complexity to obtain a target control quantity; and performing movement processing based on the target control quantity. This scheme can optimize the following effect of the mobile device on the target object.

This invention discloses an intelligent grain silo sampling method and robot based on multi-point automatic planning, belonging to the field of grain silo sampling technology. The method involves a main control unit calculating and planning a compliant sampling point sequence and a global navigation path, supporting dynamic adjustment of the point sequence and replanning of the remaining path. Multimodal perception and obstacle avoidance algorithms enable autonomous robot navigation, obstacle detour, and path regression. Subsequently, grain surface perception, main control parameter adjustment, and resistance feedback complete adaptive sampling of the grain surface, and a unique identifier is generated for each grain sample. Finally, multi-dimensional data from the equipment and environment are collected throughout the process, and the main control unit determines anomalies, issues graded warnings, and responds to remote emergency commands, achieving full-process safety monitoring and emergency intervention. Using the above method and structure, precise sampling point planning, autonomous robot navigation and obstacle avoidance, adaptive grain surface sampling, traceable grain sample information, and full-process safety monitoring, early warning, and emergency intervention are achieved.

This invention discloses a multi-agent beyond-line-of-sight networked collaborative perception dynamic decision-making method and related devices, relating to the fields of robot perception and multi-agent collaboration. The point cloud-based multi-agent beyond-line-of-sight networked collaborative perception dynamic decision-making method of this invention combines collaborative perception with path planning by maintaining obstacle lists and global abnormal path lists for the agents, enabling multi-agent collaborative perception and multi-agent collaborative planning and scheduling tasks. A replanning mechanism is introduced, allowing the agent system to make dynamic decisions based on perception information, improving its adaptability to dynamic and complex environments. The path following algorithm, which incorporates time-window path planning, can dynamically adjust its running speed according to time conditions, providing control support for the collaborative path planning algorithm. Furthermore, by adding an improved obstacle avoidance algorithm, when encountering an obstacle, it first determines whether the agent can autonomously avoid it; if it cannot, replanning is performed, reducing the frequency of replanning and improving the agent's operating efficiency.

This invention provides a swarm autonomous obstacle avoidance simulation method and apparatus based on urban terrain environment. The method includes: S1: constructing a multi-level urban terrain simulation environment model; S2: establishing a single-machine intelligent agent and constructing a swarm simulation model based on the single-machine intelligent agent; S3: setting swarm areas and tasks in the urban terrain simulation environment model and completing preset tasks; S4: collecting data information during the simulation process and evaluating the performance of the data information. This simulation method can efficiently and realistically simulate urban terrain environment and is specifically used to verify and evaluate the performance of swarm autonomous obstacle avoidance algorithms.

This invention discloses a multimodal fusion crack detection and 3D modeling method and system for complex space unmanned aerial vehicles (UAVs), belonging to the field of intelligent inspection and structural health monitoring technology. The key technical points are: simultaneously acquiring RGB images, depth data, LiDAR point clouds, and GPS / IMU data using a multimodal sensor array mounted on the UAV; identifying and segmenting multiple types of damage, including cracks, based on an improved YOLOv11s-DySnake model; fusing multi-source data to calculate the 3D coordinates of the damage; constructing a structural model using an incremental 3D modeling algorithm and visually annotating the damage; and combining a hybrid A* algorithm with a dynamic obstacle avoidance algorithm to achieve autonomous inspection path planning. This invention achieves full automation from data acquisition to 3D modeling, improving the accuracy, robustness, and positioning accuracy of damage detection.

This invention belongs to the field of cadastral surveying technology and is a system for dynamically adjusting cadastral surveying routes. The system includes a cadastral database, a field interaction module, a feature change confirmation module, and a route planning and optimization module. The field interaction module is used to collect multimodal data on feature changes in real time. The feature change confirmation module is used to perform rapid AI verification on the collected multimodal change data and combine it with historical time-series tracing to achieve accurate confirmation of feature changes. The route planning and optimization module is used to calculate the surveying priority based on the confirmed feature change data, and combine an improved obstacle avoidance algorithm with real-time fine-tuning of the cadastral spatial model to dynamically and intelligently adjust the surveying route.

The present invention provides a multi-modal obstacle avoidance system for multi-sensor fusion of an unmanned ship based on ROS, which is characterized by including three parts: sensors, a controller, and an actuator. In this system, a lidar, an industrial camera, an inertial measurement unit, and a GPS module are used to sense the surrounding environment and obtain attitude and position information, belonging to the sensors of the system; an artificial intelligence development board and a single-chip microcomputer are the main control modules, which make decisions according to the input through built-in algorithms, belonging to the controller of the system; a drive controller and a drive mechanical device are used to execute the decisions of the controller, belonging to the actuator of the system; the present invention uses a multi-sensor distributed modular architecture, enabling various functions to be coupled together, allowing tasks to be executed simultaneously in multiple threads, greatly reducing computing power, making the control more flexible, and equipped with a high-efficiency and high-accuracy vision-lidar fusion obstacle avoidance algorithm, enabling the unmanned ship to flexibly handle various complex situations that occur on the water surface and having the capabilities of autonomous cruising and autonomous obstacle avoidance.

This invention proposes a hierarchical obstacle avoidance control method and device for UAV swarms in complex environments. The method includes: dividing the UAVs into two levels, leader and followers; implementing differentiated control between levels through a hierarchical dominance matrix; the leader guiding the swarm's movement direction; and followers maintaining formation through local interactions. An adaptive obstacle avoidance algorithm is designed, combining edge distance detection and tangential force assistance to achieve safe and efficient obstacle avoidance. A gap-guiding force is incorporated into the attraction force, and a collaborative control model is constructed using attraction, repulsion, alignment, obstacle avoidance, and leader pairing forces. The alignment coefficient and leader spacing are dynamically adjusted based on environmental characteristics (proximity to obstacles) to maintain a tight formation in open areas and increase turning flexibility in narrow passages. This invention enables UAV swarms to maintain a clear direction of travel in complex environments, adaptively select paths, and avoid collisions with obstacles.

The application discloses a kind of link obstacle avoidance topologies of cross-level multi-senior node based on G6, comprising: creating at least one container, for storing topological graph;In the container, an object representing the graph of satellite station and its relationship is created, the attribute of the object is set, the edge shape of the object is called obstacle avoidance algorithm module, and obstacle avoidance is carried out;The edge shape is set, the default edge shape is added to the object representing the graph of satellite station and its relationship, and the graph is generated;The graph is rendered, and interactive function is added.The application optimizes the readability of layout, makes the distance between nodes more uniform, the relationship layout between cross-levels more reasonable, and reduces the complexity of algorithm.

The application discloses a kind of multi-agent group confrontation decision method and system based on weighted average field, the method is by constructing three-dimensional multi-agent group confrontation environment, define agent state and action space, design WMF-MAPOCA algorithm, fusion multi-hot encoding and multi-head attention mechanism realize neighbor information adaptive aggregation, while designing the reward function including target keeping, target approaching, attack success and survival four dimensions, combined with artificial potential field method realizes motion control and obstacle avoidance WMF-MAPOCA algorithm is updated by experience replay buffer and the training of three core networks, effectively alleviate dimension disaster, reduce strategy non-stationarity in large-scale asymmetric group confrontation scene.

This application provides a method, system, device, and medium for multi-aircraft cooperative search mission planning, belonging to the field of aircraft technology. The method includes: acquiring aircraft cluster information, no-fly zone information, and patrol target information; using a greedy algorithm or auction algorithm for initial task allocation, assigning patrol targets to each aircraft and generating an initial task sequence; using a zigzag path planning algorithm to generate a scanning path for the target area, generating a coverage path by intersecting horizontal scan lines with polygonal areas; detecting collision risks between aircraft paths and obstacles, and when a collision risk is detected, using an obstacle avoidance algorithm based on safe distance expansion to generate a detour path; when an aircraft is detected to have completed a task or is in an idle state, triggering dynamic task reassignment, reassigning unfinished tasks to idle aircraft; and outputting the final multi-aircraft cooperative mission planning scheme. This achieves efficient dynamic mission planning for multi-aircraft cooperative operations.

This invention discloses a method, system, equipment, and medium for cable tunnel inspection based on a quadruped robot. The method includes: real-time acquisition of multi-source data; time synchronization and anomaly processing of the multi-source data, extraction of environmental feature points and parameters required for particle filtering, and construction of a structured fusion dataset supporting localization and obstacle avoidance algorithms; estimation of the robot's real-time pose using a fusion localization algorithm based on the structured fusion dataset and tunnel environmental reference data, and generation of a coordinate sequence for the future inspection path; prediction of the future trajectory of obstacles based on obstacle motion state data, spatiotemporal overlap analysis of the obstacle trajectory and the coordinate sequence of the robot's future inspection path, identification of potential collision risks, adjustment of the robot's movement speed according to the risk level, and triggering of corresponding obstacle avoidance responses. This invention improves localization accuracy, achieves active obstacle avoidance, reduces collision risks, and adapts to the complex environment of tunnels through multi-source data fusion and trajectory prediction.

The application belongs to the field of air-ground cooperative control and obstacle avoidance, and discloses an air-ground cooperative control and obstacle avoidance algorithm, according to different environment data, the obstacle avoidance algorithm is divided into two-dimensional and three-dimensional algorithms, the algorithm is suitable for an environment lacking height data, and includes a static ellipse tangent method (SETG-TG) and a dynamic ellipse tangent method (DETG-TG), which are respectively suitable for static and dynamic environments; the three-dimensional obstacle avoidance algorithm adopts a three-dimensional tangent method (3D STG) for path planning and obstacle avoidance in a three-dimensional space. The application aims to ensure that a UAV and a ground vehicle can effectively avoid obstacles in real time in a complex and dynamic environment, and guarantee the safety, stability and path planning accuracy of the UAV and the ground vehicle.

This invention provides a sand table chart visualization system and method based on two-dimensional dynamic process mapping, belonging to the field of data visualization and process management technology. The sand table chart visualization system based on two-dimensional dynamic process mapping includes a data interface module, a sand table view generation module, a dynamic node layout engine, an intelligent path drawing module, and a bidirectional interactive state management module, all connected sequentially. This invention achieves adaptive node layout by constructing a two-dimensional dynamic grid and combines it with an intelligent obstacle avoidance algorithm to draw node-related paths, realizing collaborative visualization of business process and project process data. It can clearly present the cross-mapping relationship of two-dimensional processes, improving the readability and interactivity of complex process visualization, and is suitable for various business process management and data visualization analysis scenarios.

The application discloses an obstacle avoidance verification method and device, the method comprises the following steps: collecting simulation binocular images through simulation test by mounting a motion model of a camera component, and determining a simulation depth map; constructing a depth map model, training an actual scene depth map collected from a real environment to obtain an actual depth map model; inputting the simulation depth map into the actual depth map model to fuse actual scene features and obtain a fused depth map; converting the fused depth map into a video stream and pushing the video stream to a streaming media server; and an obstacle avoidance algorithm accesses the streaming media server to obtain the fused depth map for verification. The method solves the problem that the training and verification method of the existing binocular camera obstacle avoidance algorithm has limitations in efficiency, cost, cycle and environment coverage, and cannot meet the demand of intelligent agents for high-performance and high-robustness obstacle avoidance capability, reduces the cost and cycle of obstacle avoidance algorithm verification, and improves the generalization ability and robustness of obstacle avoidance algorithm verification.