2959 results about "Obstacle avoidance" patented technology

The invention discloses an unmanned ship multi-agent collaborative obstacle avoidance method and system for complex sea conditions, and relates to the field of unmanned ship multi-agent collaborative obstacle avoidance, and the method comprises the steps: obtaining the real-time data of each unmanned ship and the surrounding environment; generating a candidate obstacle target point cloud cluster list based on a density clustering algorithm; on the basis of a Kalman filter, real-time absolute motion state estimation of the candidate obstacles is obtained, and an obstacle feature list is output; determining a safety radius compensation amount required by autonomous obstacle avoidance of each unmanned ship; obtaining the safe sailing space of each unmanned ship at the current moment; constructing a global synthetic potential field, and generating a group of optimal alternative paths of the unmanned ship from the current position to the target point; on the basis of adopting a consensus binding algorithm, an unmanned ship multi-agent collaborative obstacle avoidance path reaching a consensus is obtained. The method has the advantages that safe, efficient, cooperative and consistent intelligent obstacle avoidance of a multi-unmanned-ship cluster in a real complex marine environment is realized.

The invention discloses a power transmission line inspection unmanned aerial vehicle obstacle intelligent detection and obstacle avoidance method. The method comprises the steps that multi-source sensing data is acquired, and alignment is completed through calibration and timestamp matching; heterogeneous data preprocessing and feature enhancement; constructing a high-precision environment fusing a geometric structure and a semantic tag, mapping a two-dimensional target detection result output by the recognition network to a three-dimensional coordinate system through spatial transformation, and fusing the two-dimensional target detection result with a point cloud structure to construct a semantic occupation grid map; performing preliminary route planning according to a preset power grid topological structure and task coverage requirements, and generating a barrier-free flight path covering the whole inspection area; reinforcing learning of a dynamic obstacle avoidance strategy; track dynamic reconstruction and energy consumption optimization scheduling are carried out; the technical problems that an existing technical system has defects in the aspects of obstacle recognition accuracy, complex environment adaptability, data fusion capacity and obstacle avoidance strategy intelligence, and the requirements for high-reliability, low-energy-consumption and high-efficiency unmanned aerial vehicle power transmission line inspection are difficult to meet are solved.

The present disclosure belongs to a field of UAVs exploration technology, discloses a high-efficient autonomous exploration method, system, and terminal for UAVs, comprising: S1, heuristic waypoint generation: setting an exploration scope and waypoint spacing, and generating waypoints through waypoint generation algorithms; S2, global path planning: after generating heuristic waypoints, an A * algorithm is used to generate the global planning path; S3, real-time positioning and mapping: using point clouds for real-time positioning and mapping; S4, local B-spline trajectory generation: using B-spline parameterization method to generate local trajectories; S5, real-time obstacle avoidance and dynamic feasibility constraints: optimizing the trajectories to achieve fast convergence, generating smooth, collision-free, and dynamically feasible trajectories; S6, local real-time replanning: using a time sliding window for local replanning; S7, flight control: Using UAV control algorithms for controlling of UAVs robustly.

The invention discloses a robot obstacle avoidance method and system based on millimeter wave radar sparse point cloud, and relates to the technical field of obstacle avoidance recognition. A robot obstacle avoidance system based on millimeter wave radar sparse point cloud comprises a point cloud acquisition module, a negative obstacle identification module, a weak obstacle identification module, a point cluster identification module, a risk map module, a tentative verification module and an obstacle avoidance decision module. According to the invention, suspected obstacle point clusters are extracted based on a reflection intensity threshold and a spatial proximity relation in an enhanced point cloud, a theoretical parallax model of a real static obstacle is constructed under the constraint of a robot motion trajectory, and Doppler velocity distribution of each frame is combined with a static obstacle Doppler physical law for comparison. And classifying the point clusters which do not meet the multi-view geometric consistency or Doppler physical law, and distinguishing multipath false point clusters from dynamic point clusters.

The invention discloses a double-layer and double-stage heterogeneous unmanned aerial vehicle task allocation and flight path planning method, and relates to the technical field of unmanned aerial vehicles. The method comprises a dual-stage task allocation method and a dual-stage path planning method. The beneficial effects of the invention are that the dual-stage task allocation method and the dual-stage path planning method are provided for improving the efficiency of the multi-unmanned aerial vehicle cooperative execution of the search rescue task; according to the dual-stage task allocation method, a joint optimization framework combining mixed integer linear programming and an improved ant colony algorithm is provided, so that the task load balance and the total flight distance can be optimized, and a better task allocation effect is realized; the dual-stage path planning method provides a dual-stage path planning scheme in which global task sequence optimization and local obstacle avoidance planning are coordinated, and a better path planning effect is obtained by combining the global and local dual-stage optimization scheme.

The invention relates to a distributed trajectory planning method and system for an unmanned aerial vehicle cluster in an obstacle environment. The method comprises the steps of initializing a system, constructing a local Euclidean symbol distance site map, and realizing real-time interaction of state information of the unmanned aerial vehicle. An initial collision-free path is generated using jump point search. And solving an optimal control point through an L-BFGS algorithm through multi-constraint trajectory optimization in combination with a load swing dynamics model, four-rotor dynamics limitation, cluster collision avoidance and environment obstacle avoidance requirements. And a dynamic time redistribution strategy is adopted, and the track time interval is adjusted according to speed and acceleration overrun conditions. The method further comprises an adaptive re-planning mechanism, and local target points are updated in real time and adjacent aircraft collaborative optimization is triggered based on local map boundary detection and quadrotor track safety detection. According to the method, efficient, safe and stable trajectory planning of the hanging load quad-rotor unmanned aerial vehicle cluster in a complex environment is realized, the requirement of autonomously and efficiently completing tasks is met, and the task execution efficiency and safety are improved.

The invention relates to the technical field of robot control, particularly discloses a path planning and dynamic obstacle avoidance control method for multi-task collaborative operation of an industrial robot, and aims to solve the problems of task scheduling conflict, dynamic obstacle response lag and low collaborative efficiency in a multi-robot system. The method comprises the following steps: constructing a task-resource joint scheduling model and generating initial task allocation; planning a conflict-free collaborative path based on an improved space-time A star algorithm; predicting a dynamic obstacle trajectory by using an LSTM network and generating a space-time envelope; constructing a second-order safety barrier function fusing task priorities; local obstacle avoidance re-planning is realized through rolling horizon model predictive control; and the global rescheduling is triggered when the task delay exceeds the limit or the deadlock risk occurs. According to the technical scheme, closed-loop linkage of global task collaboration and local dynamic obstacle avoidance is realized, and the system collaboration efficiency, the obstacle avoidance success rate and the operation robustness are remarkably improved.

The invention discloses an industrial robot intelligent obstacle avoidance control method and system based on visual identification, and relates to the technical field of robot obstacle avoidance control, and the method comprises the steps: carrying out the calibration of a multi-mode visual sensor, obtaining an initial depth map and point cloud information, and combining the visual identification and depth compensation technology; recognizing and positioning obstacles in the operation area, and constructing a dynamic environment map layer; and the current robot state is collected, kinematics calculation and collision distance analysis are carried out, whether obstacle avoidance operation needs to be executed or not is judged, if yes, an obstacle avoidance path is generated in combination with the dynamic environment map layer and the target point location, feasibility verification is carried out after the path is generated, and the path passing the verification serves as an execution track to be issued to the control module. According to the invention, the recognition precision and depth perception integrity of the industrial robot on obstacles in a complex environment are improved, high feasibility of path planning and high-reliability obstacle avoidance capability in a dynamic environment are realized, and the intelligent decision-making level and operation safety of the system are remarkably enhanced.

The invention belongs to the technical field of ship navigation and port automation, and particularly discloses a dock ship guiding path planning and dynamic obstacle avoidance system, which is characterized in that a data acquisition module acquires ship parameters, environmental data and obstacle information through a ship end sensing unit and a dock end monitoring unit and standardizes the ship parameters, the environmental data and the obstacle information; the initial path planning module generates an optimal initial ship guiding path based on an improved A * algorithm in combination with ship physical constraints, environmental constraints and operation rules; the dynamic obstacle avoidance module monitors the movement of an obstacle in real time, predicts the trajectory of a ship and the obstacle, and triggers local path re-planning and smoothing processing when a collision risk occurs; and the control output module converts the path into a control instruction, issues and receives ship feedback through two-way communication, and forms closed-loop control according to a deviation fine adjustment instruction or re-planning. The system realizes full-process automation of ship guiding, can dynamically adapt to environmental changes, improve path precision and operation safety, adapt to different docks and ship types, and ensure that ships are accurately and safely parked to target berths.

The invention relates to the technical field of service robot navigation and motion control, in particular to a service robot dynamic obstacle pre-judgment obstacle avoidance system, which comprises an environment sensing unit for outputting sensing data; the target state estimation unit outputs a target state sequence; the future evolution prediction unit is used for outputting a target future evolution result; the risk quantification unit outputs the current collision risk degree; operating the strategy management unit, and outputting strategy parameters; the evasion decision and path generation unit is used for outputting candidate evasion tracks and speed curves; the execution and control interface unit is used for outputting a chassis control instruction and implementing trajectory tracking; and the device communication and calculation platform is used for outputting a time sequence synchronization and data channel management result according to operation requirements and providing fault monitoring. According to the method, target state estimation, future evolution prediction, collision risk quantification and strategy linkage are completed within real-time constraints, consistent input and constraints are provided for planning and execution on a unified data channel, and safety and traceability are improved.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle dynamic obstacle avoidance method based on multi-sensor fusion. Comprising the steps of receiving an image sequence, a depth point cloud set, a pose state parameter and a flight speed parameter; calculating an illumination distortion gradient value and a speckle noise entropy value based on the image sequence and the depth point cloud set; performing coordinate registration and feature extraction on the image sequence, the depth point cloud set and the pose state parameter, and outputting a visual feature set and a radar feature set; generating a weighted visual feature set and a weighted radar feature set through a weight adjustment function, and generating a joint heterogeneous feature tensor through alignment splicing; and generating a body attitude and thrust control instruction by using the combined heterogeneous feature tensor through a probability prediction model and a model prediction control algorithm device. According to the method, through cross-domain multiplexing and deep coupling of the body physical parameters in the data stream, error accumulation and decision delay caused by multi-stage series calculation are avoided, and global performability of an obstacle avoidance task is facilitated.

The invention discloses an intelligent low-altitude unmanned aerial vehicle obstacle avoidance path planning method and system. The method comprises the steps of multi-modal data acquisition, space-time alignment fusion, three-dimensional dynamic trajectory reconstruction, obstacle avoidance path planning and system verification. The invention belongs to the technical field of path planning, and particularly relates to an intelligent low-altitude unmanned aerial vehicle obstacle avoidance path planning method and system.According to the scheme, an incremental dynamic updating mechanism is adopted, and the three-dimensional trajectory reconstruction precision is improved through a double-correction mechanism of hidden space normal estimation and global discretization constraint; bEV feature optimization fusion is adopted to generate a double-enhanced feature map; constructing a mixed bounding box for the trajectory prediction points, calculating a dynamic minimum distance, designing a dynamic safety threshold fusing the real-time speed of the unmanned aerial vehicle and the priority, constructing a space-time coupling risk index in combination with the potential collision time and the priority to complete refined risk grading, and constructing a reinforcement learning model of priority weighted rewards; the problem that the passing right distribution is not uniform in the low-altitude multi-priority unmanned aerial vehicle obstacle avoidance process is solved.

The invention discloses a visual perception and dynamic anti-collision intelligent closed-loop control method for a central control mechanical arm in a vehicle, and the method comprises the steps: S1, loading an improved YOLOv11 network detection model, and reading a video stream of a camera; s2, preprocessing each frame of image in the video stream, inputting the preprocessed image into the model, and performing target identification; s3, inputting an identification result into a dynamic safe area collision model, and performing coordinate conversion through a hand-eye calibration algorithm; calculating a relative position and a motion trend in combination with depth sensor data, and judging a risk level to generate a corresponding protection instruction; and S4, executing a hierarchical control strategy according to the risk level, updating the action track of the mechanical arm, and returning to the step S1 through a closed-loop feedback mechanism to adjust the safety threshold and the control parameters to form a complete'perception-decision-execution-optimization 'autonomous evolution closed loop. According to the method, the visual perception robustness and the target detection precision under the complex illumination condition are improved, and real-time dynamic anti-collision response and millisecond-level rapid obstacle avoidance are realized.

The invention belongs to the technical field of inspection robot systems, and particularly relates to an inspection robot path planning method and system.The visual semantic perception module collects an industrial environment image through a top industrial camera, after graying and Gaussian filtering preprocessing, feature points are detected and matched through an ORB algorithm, and a path planning result is obtained; in combination with an illumination self-adaptive threshold screening mechanism, mismatching points are eliminated, semantics are marked, and a semantic feature map is constructed; the initial path planning module generates an initial path through a semantic cost-containing A * algorithm based on the map; the dynamic obstacle avoidance module captures a moving obstacle by using a visual sensor, and predicts a trajectory through Kalman filtering; the path optimization module combines an initial path and an obstacle track, optimizes the path by using quadratic programming of a fusion curvature constraint and an energy consumption model, and corrects positioning by fusing vision and IMU data through a dynamic weight fusion algorithm; and the execution feedback module generates an instruction according to the optimized path, re-triggers path optimization, forms a closed loop, and ensures the inspection stability.

The invention discloses a distributed intelligent control method for unmanned aerial vehicle cluster cooperative path planning, and relates to the technical field of unmanned aerial vehicle cluster cooperative control, and the method comprises the steps: collecting a dynamic feature set of an unmanned aerial vehicle in real time, and carrying out the time synchronization, noise filtering and formatting integration, and forming a local state data set; performing neighborhood collaborative analysis and temporary link planning on the local state data set to obtain relay chain layout data; performing environmental risk assessment and path feasibility analysis on the relay chain layout data to generate a candidate path set; adjusting the candidate path set through local obstacle avoidance and dynamic constraint to generate an executable path sequence, and generating a collaborative execution path through neighborhood communication and collaborative adjustment; performing task execution result integration and state updating on the collaborative execution path to generate group state data; and performing path feedback and loop optimization on the group state data to obtain a group collaboration result. And the task completion rate, the cooperation efficiency and the environment adaptability are improved.

The invention relates to the technical field of autonomous navigation of inspection robots, and provides a field inspection robot autonomous navigation and path planning method based on multi-modal sensing. The method comprises the following steps: collecting a laser radar point cloud, a camera image, inertial measurement and positioning data, obtaining an environment semantic feature set through spatio-temporal feature fusion, and generating a semantic occupation map; inputting the current position of the robot, the target point and the semantic occupation map into a trajectory generation network to obtain candidate trajectories meeting obstacle avoidance and path smoothness constraints, and completing task sorting and trajectory splicing in combination with inspection task points to form a global path; in the operation process, the reinforcement learning control model adjusts the linear speed and the angular speed in real time, and path tracking and dynamic obstacle avoidance are achieved. The navigation precision and the operation safety of the inspection robot in the complex field area are improved.

The invention belongs to the technical field of artificial intelligence and intelligent control, and particularly relates to an obstacle avoidance control method based on dynamic obstacle trajectory prediction. The method aims at solving the problems of obstacle avoidance decision lag and path oscillation caused by nonlinearity and strong interactivity of obstacle movement in a complex dynamic environment. A historical pose sequence of an obstacle is obtained through multi-source sensor fusion, a probabilistic trajectory prediction model is constructed by combining Gaussian mixture process regression with scene topology constraints, an attention mechanism is introduced to enhance inter-individual interactive intention modeling, and a socially compatible joint prediction trajectory is generated. The method also supports the dynamic adjustment of the prediction time domain and the re-prediction frequency, and gives consideration to the calculation efficiency and the response capability. The method is deployed in an embedded platform, the cycle period is less than 80 milliseconds, more than 32 obstacle trajectories can be tracked and predicted in real time, and the obstacle avoidance safety and smoothness of the system in a dense and high-speed dynamic scene are remarkably improved.

The invention discloses an automatic hoisting and positioning system and method for prefabricated parts, and relates to the technical field of building construction. The system comprises a tower crane, a frequency conversion trolley, a three-axis pan-tilt stabilizer, a receiver positioning device and an infrared emitter which are coaxial, an intelligent terminal control system with a pre-stored BIM model, and an integrated inertial sensing unit and a dome camera monitoring system. The intelligent terminal generates target coordinates through the BIM positioning reference module, the real-time deviation calculation module quantifies three-dimensional positioning and axis perpendicularity deviation, and the compensation instruction generation module drives targeted adjustment; through combination of infrared dual-mode switching, frame line deviation correction and inertia swing dynamic suppression, precise positioning and obstacle avoidance cooperation is realized. According to the method, through the closed-loop process of benchmark setting, obstacle avoidance lifting, fine compensation and standard confirmation, it is guaranteed that the positioning deviation is not larger than 0.8 mm, the axis perpendicularity is not larger than 0.3 degree, and the inertia swing is not larger than 10 cm, the problems that traditional hoisting is poor in precision and large in inertia interference are solved, and the installation quality and the operation efficiency are improved.

The invention discloses an automatic guided vehicle path optimization method and system based on fusion planning, and relates to the field of robot technology, computer science and automation control, and the method comprises the steps: carrying out the global path optimization based on an improved A star algorithm according to a two-dimensional grid map, and obtaining a global optimal path and a key path point set; performing local path planning based on an improved dynamic window algorithm according to the global optimal path and the key path point set, and obtaining a local obstacle avoidance track and an optimal speed control instruction; and based on the optimal speed control instruction and in combination with the local obstacle avoidance trajectory, obtaining an automatic guided vehicle path optimization result. According to the method, on the global planning level, the path length and the number of redundant nodes are remarkably reduced, the steering frequency is effectively reduced, and the path smoothness and the performability are improved; in a local planning level, efficient obstacle avoidance and real-time trajectory optimization in a dynamic obstacle environment are realized.

The invention discloses a flight vision obstacle avoidance method for a low-altitude inspection unmanned aerial vehicle, particularly relates to the technical field of image processing and computer vision, and is used for solving the problem of how to stably detect and reliably estimate the distance and contact time of such obstacles on the premise of only depending on airborne vision and being limited in calculation power and time delay. A space-time skeleton of a linear obstacle is constructed and continuously tracked in the strip-shaped candidates, and a grading distance and contact time are given in combination with an imaging time sequence difference and projection drift; uncertainties generated by the detection side and the self-motion are unified to the same coordinate and time mark, and risk density and safety envelope are formed through recursion; and then a path is updated according to a fixed priority of'deceleration-bypassing-still hovering 'by taking the risk as a hard constraint, so that high recall and stable distance evaluation and executable real-time avoidance can still be realized for a slender and swinging real obstacle under the conditions of only dependence on airborne vision and limited computing power and time delay. And the near loss event occurrence rate and the meaningless braking ratio are obviously reduced.

The invention provides a multi-unmanned aerial vehicle adaptive migration method for a sudden no-fly zone, and relates to the technical field of unmanned aerial vehicles. And acquiring fusion environment data through cooperative sensing of multiple unmanned aerial vehicles, processing the data by adopting a semantic boundary extraction algorithm based on deep learning, and outputting a sudden no-fly zone map and unmanned aerial vehicle safety state codes. On the basis, the unmanned aerial vehicles are shunted according to the safety state codes and the sudden no-fly zone map, real-time obstacle avoidance tracks are generated for the non-intruding unmanned aerial vehicles, and cluster collaborative separation is executed for the intruding unmanned aerial vehicles. For unmanned aerial vehicles whose tasks are interrupted due to obstacle avoidance or separation, task decomposition is realized based on the sudden no-fly zone map and space-time isolation, and continuous execution of cluster tasks is realized through an energy consumption constraint auction mechanism, so that a plurality of unmanned aerial vehicles can better cope with a complex and changeable sudden no-fly zone environment.

The invention discloses a robot image-free visual target docking method and system based on multi-modal perception, and the method comprises the steps: carrying out the recognition calculation of a preset target in the image data of the environment in front of a robot through a target detection model YOLOv4-tiny, and constructing a robot control feedback quantity; radial distance information of an object in the environment is obtained, a scanning range is divided through a regional strategy, straight line fitting calculation is conducted, and a deviation evaluation value of the robot relative to the yaw angle is constructed; and the robot accumulative mileage is obtained, finite-state machine hierarchical decision making and mistaken stop prevention judgment are conducted in combination with the robot control feedback quantity and the robot relative yaw angle deviation evaluation value, and robot graph-free visual target docking is achieved. According to the invention, full-process autonomous control of the robot from target discovery, navigation on the way, obstacle avoidance to end fine alignment can be realized. The robot graph-free visual target docking method and system based on multi-modal perception can be widely applied to the technical field of graph-free navigation.

The invention discloses a multi-agent intelligent way-finding and real-time decision-making method based on MADDPG, and the method achieves the autonomous collaborative decision-making of multi-target optimization through the technical links of environment perception, dynamic obstacle intention prediction, single-agent pre-training, strategy distillation and multi-agent collaborative training. The method comprises the following core steps: predicting a dynamic obstacle intention based on a long short-term memory network of an attention mechanism; constructing a threat field model to quantify the environmental risk; forming basic obstacle avoidance and path optimization experience through single agent pre-training; empirical migration is realized by utilizing strategy distillation so as to shorten a multi-agent training period; and multi-agent cooperative training is completed based on a centralized training-distributed execution architecture and an MADDPG algorithm. According to the method, the path length, the energy consumption, the safety and the task completion rate can be considered, the survivability, the cooperation efficiency and the task adaptability of the unmanned aerial vehicle formation in the complex dynamic environment are remarkably improved, and the method can be widely applied to different-scale formation and multiple task scenes such as reconnaissance, communication relay and situation awareness.

The invention discloses an obstacle avoidance motion planning method and system for redundant drive of an intelligent process of a welding robot and computer equipment. According to the method, firstly, a motion path of a robot is planned based on welding process redundancy, then a sampling strategy that the position and posture of a welding gun are separated is adopted, then the sampled posture is mapped to a planar two-dimensional space, and the relevance between an obstacle collision boundary and the obstacle avoidance posture of the welding gun is constructed; an incremental extreme gradient lifting model is adopted to carry out probability modeling on the collision boundary and obstacle avoidance attitude relevance, nearest neighbor search and new planning node expansion are carried out based on target cost to obtain new planning nodes, collision detection is carried out on the new planning nodes, and the new planning nodes passing the collision detection are used for incremental learning of an obstacle avoidance model; and finally, an optimization strategy of spherical linear interpolation and cosine slow motion time mapping is adopted to ensure continuity and smoothness of a motion planning path trace. The problem that in the prior art, it is difficult to quickly calculate and generate a collision-free welding track is solved.

The invention relates to the technical field of computer vision, in particular to a method for generating a blind person navigation obstacle avoidance map. The method comprises the following steps: driving a blind guiding robot to a preset inspection point, and collecting a surrounding environment image in real time; generating a three-dimensional point cloud according to the environment image, and constructing an environment map; extracting feature points based on the surrounding environment image, performing feature matching, and outputting a feature matching result; evaluating an inter-frame motion state by using a feature matching result; determining pose features of the blind guiding robot according to the inter-frame motion state; semantic segmentation is carried out by using the pose features, and an obstacle area is determined; acquiring an obstacle image of the obstacle area, and fusing the obstacle image with the surrounding environment image to generate an obstacle map; and constructing a dynamic occupancy grid based on the obstacle map. According to the invention, real-time updating and intelligent obstacle avoidance functions of the blind navigation map are realized based on the computer vision technology, and the navigation safety and the path planning accuracy are improved.

The invention discloses an automatic driving path planning method based on multi-sensor fusion, and relates to the technical field of automatic driving, and the method comprises the steps: collecting environment, vehicle and positioning data through multi-source sensors, such as a laser radar and a camera; mapping a vehicle body coordinate system through coordinate conversion, and carrying out downsampling, correction enhancement and denoising preprocessing on data; extracting multi-sensor features, and distributing weights by means of a cross-modal attention mechanism to complete multi-modal fusion; recognizing obstacles and passable areas, and predicting a 5s track of a dynamic obstacle; 5-8 initial paths are generated based on the coordinate system and dynamic constraints; constructing a multi-objective function to screen an optimal path; and finally, a steering angle and an acceleration instruction are calculated through model prediction control, and accurate path tracking is realized. The sensing robustness is improved through multi-sensor fusion, the dynamic weight adjustment adapts to the complex environment, the emergency obstacle avoidance mechanism is quick in response, and the automatic driving reliability and the scene adaptability are integrally enhanced.

The invention discloses a sensing optical cable laying robot path planning and obstacle avoidance system and method, and belongs to the technical field of robot path planning. The system comprises an environment sensing module, a data preprocessing module, a path planning module, an obstacle avoidance control module and a post-processing module. According to the method, environment information is acquired and preprocessed through a laser radar and a camera, path planning is performed based on an improved dynamic RRT algorithm, a self-adaptive step length adjustment mechanism is introduced, the step length is dynamically adjusted according to environment complexity, obstacle distance and path curvature, and a smooth path is generated by combining real-time obstacle detection and dynamic re-planning functions. A PID control algorithm is adopted to adjust the speed and the steering angle of the robot to achieve obstacle avoidance, and a post-processing module optimizes an evaluation path. The real-time performance, the path quality and the environment adaptability of the robot in a complex dynamic environment are improved, and efficient, safe and accurate laying of optical cables is ensured.

The invention provides an unmanned aerial vehicle formation conflict avoidance control method and system and a medium, and the method comprises the steps: introducing a virtual leader-follower-based Stackelberg game architecture, enabling a virtual leader unmanned aerial vehicle to adaptively adjust the trajectory according to the formation state, and enabling the follower unmanned aerial vehicle to maintain the stability of the formation while completing a set tracking task; constructing an obstacle avoidance penalty term based on a speed obstacle method, and embedding the obstacle avoidance penalty term into an optimization objective function of the follower unmanned aerial vehicle to ensure that the follower unmanned aerial vehicle realizes collision avoidance in an obstacle environment; and respectively solving Hamiltonian-Jacobian-Bellman equations corresponding to the leader unmanned aerial vehicle and the follower unmanned aerial vehicle by adopting a single-network ADP algorithm, and generating a controller in real time. According to the invention, the problems of safe obstacle avoidance and tracking control of a multi-unmanned aerial vehicle system with formation stability requirements are effectively solved.

The invention provides a robot trajectory tracking and obstacle avoidance control method, system and device and a medium, and belongs to the field of intelligent robot control, and the method comprises the steps: obtaining motion state information, obstacle information and a reference trajectory; calculating a look-ahead distance based on preset prediction time, a safety coefficient and a current maximum speed in the motion state information; selecting a locally planned target point from the reference trajectory according to the look-ahead distance; based on a dynamic window algorithm, generating a plurality of candidate tracks according to the motion state information and the obstacle information; based on the multi-target cost function and the target point, evaluating each candidate track to obtain a comprehensive cost value; and determining the candidate trajectory with the minimum comprehensive cost value as an optimal trajectory, obtaining an expected linear velocity and an expected angular velocity based on the optimal trajectory, and generating a control instruction to drive the robot to move along the optimal trajectory. According to the invention, the motion stability, path continuity and overall robustness of the robot in a continuous multi-section inspection task are improved.

The invention relates to the technical field of mechanical arms, in particular to a chemical emergency disposal-oriented manipulator cross-modal sensing control system which comprises an environment scanning module, a visual positioning module, an obstacle avoidance planning module, a pose compensation module, a decision mapping module and a grabbing updating module. Obtaining environment state information; an operation view angle image of the manipulator is collected, visual features of the operation view angle image are solved, and pose information is obtained; obstacle avoidance correction is conducted on the motion trail of the mechanical arm, and a collision-free approaching path is obtained; the operation view angle image and the multi-mode micro-touch signal are fused, error compensation is carried out on the pose information, and an accurate grabbing pose is obtained; mapping object surface physical attributes sensed by the multi-mode micro-tactile signals to obtain a self-adaptive grabbing instruction; the manipulator is controlled to conduct grabbing through the self-adaptive grabbing instruction, and environment state information is updated; according to the invention, the accuracy of cross-modal sensing control of the manipulator can be improved.