5524 results about "Obstacle avoidance" patented technology

The invention relates to the technical field of industrial robots, in particular to an industrial robot walking control system based on obstacle recognition. Comprising an environment sensing unit; the obstacle analysis and decision-making unit is used for processing the multi-dimensional data output by the environment sensing unit based on a deep reinforcement learning framework, accurately identifying static obstacle and dynamic obstacle types, motion trails and interaction influences, constructing a two-dimensional decision-making model of static obstacle avoidance and dynamic obstacle avoidance, and carrying out obstacle avoidance and obstacle avoidance on the basis of the two-dimensional decision-making model. A differential obstacle avoidance strategy is triggered; the path planning unit is based on a dynamic game path algorithm under space-time constraint; and an instruction transceiving unit. Through the multi-modal fusion sensing technology and the adaptive parameter adjustment module, space-time alignment and feature fusion of multi-source data such as three-dimensional point cloud, texture features and vibration spectrum are realized, a high-dimensional environment state model is constructed, and the problem of insufficient data fusion depth in the prior art is effectively solved.

The invention provides an unmanned aerial vehicle low-altitude intelligent traffic dynamic airspace management and control method and system, and relates to the technical field of intelligent control, and the method comprises the steps: taking an electronic fence geographic coordinate set as a monitoring reference boundary, fusing ADS-B data, meteorological information and an unmanned aerial vehicle equipment state, generating a real-time risk thermodynamic diagram, and outputting a grading alarm instruction; receiving a real-time risk thermodynamic diagram and a grading alarm instruction, and combining wind speed prediction and dynamic airspace occupation data; when the grading alarm instruction is triggered, executing the following operations: constructing a route feasible solution space by taking a no-fly zone and a high-risk zone in the risk thermodynamic diagram as constraint conditions; and iterating an evolutionary path population through selection, intersection and mutation operations of a genetic algorithm by taking the lowest energy consumption as an optimization target, so as to output a global final obstacle avoidance bypassing path, and issuing a route updating instruction to an unmanned aerial vehicle flight control system. According to the invention, the utilization of airspace resources is maximized on the premise of ensuring safety.

The invention relates to the technical field of intelligent assessment and decision making, in particular to a robot dynamic risk assessment and decision making system and method based on multi-modal perception, and the system comprises a multi-modal sensor module which is used for collecting environment vision, acoustics, mechanics and position data in real time; the edge calculation unit is used for carrying out space-time alignment and feature fusion on the sensor data; the dynamic risk assessment model is used for integrating the environment uncertainty quantification module and the robot state prediction module based on a reinforcement learning framework; the decision execution interface is used for outputting a risk level and obstacle avoidance, speed reduction and shutdown instructions; by integrating visual, acoustic, mechanical and position multi-source sensor data and the like, the system can comprehensively capture various risk factors in a complex dynamic environment, so that the defect that a traditional single sensor system is insufficient in sensing dimension is overcome, and the system is particularly suitable for terrains and weather conditions with variable regions.

The invention discloses a logistics robot path planning method based on multi-modal perception, and relates to the technical field of robot path planning. Laser radar, visual camera and IMU data are fused, and environment state feature vectors are generated through multi-modal data synchronization and space-time alignment; the method comprises the following steps: analyzing environmental semantics by using models such as PointPill and YOLOv8, extracting dynamic characteristics, and identifying obstacles; constructing a space-time risk field, searching a path by a space-time algorithm, converting path points into a continuous trajectory, and optimizing the continuous trajectory; deviation is evaluated in real time, dynamic re-planning is triggered, and multi-robot cooperation and environment semantic understanding are included. Through multi-mode perception fusion, hierarchical planning, multi-target optimization and a cooperation mechanism, the obstacle detection accuracy and the obstacle avoidance success rate are improved, the path planning time is shortened, the energy consumption is reduced, the multi-robot conflict is reduced, the task efficiency is improved, the environment semantic understanding and task adaptive ability is enhanced, and the method is suitable for scenes such as intelligent storage and the like and has wide application prospects. The automation level is improved.

The invention discloses a rescue robot path planning method and system under industrial vision assistance, and relates to the field related to industrial vision, and the method comprises the steps: collecting three-dimensional space data of a rescue environment in real time, generating a dynamic environment point cloud data set, and constructing a three-dimensional semantic map of a rescue area; thermal imaging data updated in real time are called, path analysis is carried out in combination with the three-dimensional semantic map, and a path planning strategy set is obtained; and predicting the motion track of the dynamic obstacle based on the local dynamic obstacle avoidance strategy, optimizing the global path planning strategy according to obstacle prediction track data, and generating a motion control instruction of the rescue robot. The technical problem of poor real-time performance and adaptability of path planning caused by insufficient perception of environment dynamic information in path planning of an existing rescue robot is solved, the strong perception capability depending on industrial vision is achieved, the environment dynamic information is accurately captured in real time, and the real-time performance of path planning is improved. And the real-time response speed of path planning and the adaptability to a dynamic environment are improved.

The invention provides a disaster area unmanned aerial vehicle cluster dynamic task allocation and cooperative control method and system. The method comprises the steps that S1, original data of a disaster area scene are collected in real time through ground IoT equipment; s2, performing dynamic task priority marking on the original data, and integrating multi-dimensional information of the unmanned aerial vehicle; s3, tasks are allocated to the unmanned aerial vehicle based on a hybrid allocation strategy, and an initial obstacle avoidance path is generated; s4, when the unmanned aerial vehicle executes the task, the flight path is updated in real time, temporary obstacles are avoided, path conflicts are coordinated through a conflict detection algorithm, and task redistribution or degradation is carried out if necessary; s5, periodically, globally and dynamically adjusting task priorities or correcting paths; and S6, constructing a self-adaptive closed-loop control mechanism, and tracking multi-dimensional information and task attributes of the unmanned aerial vehicle in real time. According to the method, the global resource utilization rate is maximized while the second-level response of the high-priority task is guaranteed, and finally, the robustness and the adaptive ability of the unmanned aerial vehicle cluster in a complex scene are realized.

The invention relates to the technical field of unmanned aerial vehicle flight path planning, in particular to an unmanned aerial vehicle real-time path planning system and method based on dynamic weight distribution and multi-source data fusion. The system comprises a multi-source data fusion module, an integrated laser radar, a millimeter wave radar, a visual sensor and a Beidou positioning unit. The dynamic weight distribution module dynamically adjusts the weight coefficient of each sensor according to the environmental complexity, the threat level and the state of the unmanned aerial vehicle by adopting a mixed decision-making mechanism combining fuzzy logic and reinforcement learning; an improved RRT * algorithm and a Markov decision process are built in the real-time path planning module, and a global optimal path and a local obstacle avoidance track are generated by adopting a layered planning architecture; the unmanned aerial vehicle cooperative control module comprises a dual-redundancy flight control system and a dynamic obstacle avoidance unit; and the communication relay module supports 5G and low-orbit satellite dual-mode communication, updates an environment cognitive model of each unmanned aerial vehicle through federated learning, and realizes multi-source fusion real-time path planning based on dynamic weight distribution and the unmanned aerial vehicles.

The invention discloses a bionic group intelligent low-altitude logistics unmanned aerial vehicle cluster anti-wind interference cooperation method, and the method comprises the steps: collecting the historical flight data and three-dimensional wind field data of an unmanned aerial vehicle cluster, and generating a bionic formation feature set with a wind field label; inputting the bionic formation feature set into a swarm intelligence model fused with fluid mechanics, and generating a dynamic formation topology instruction; according to the dynamic formation topology instruction, adjusting the relative position and attitude angle of each unmanned aerial vehicle through a distributed cooperative control algorithm, and generating an anti-wind disturbance cooperative flight state; and continuously monitoring the deviation between the three-dimensional wind field change and the cooperative flight state, dynamically correcting the weight of the formation density-anti-wind disturbance intensity mapping relation through a reinforcement learning algorithm, updating a dynamic formation topology instruction, and realizing adaptive control of bionic group anti-wind disturbance cooperation. According to the embodiment of the invention, high-disturbance-rejection cooperative flight of the unmanned aerial vehicle cluster in the dynamic wind field can be realized, the formation energy consumption is reduced, and the obstacle avoidance capability under the sudden wind condition is improved.

The invention relates to the technical field of autonomous navigation and intelligent control of an ocean unmanned system, in particular to a dynamic obstacle avoidance system in unmanned ship path planning, which comprises a multi-source sensing module, a global path planning module, a remote obstacle avoidance decision module, a short-range dynamic obstacle avoidance module and a multi-stage cooperative control unit, and is also provided with a semi-physical verification module. The multi-source sensing module fuses multi-source data to construct a layered map; the global path planning module generates and optimizes a path by adopting an improved algorithm; the long-range and short-range obstacle avoidance modules cope with far and near obstacles based on different algorithms; the multi-stage cooperative control unit coordinates the output of each module; the functions of all the modules are achieved through specific algorithms and formulas, and the semi-physical verification module simulates a real environment to conduct system testing. According to the invention, the environment can be sensed in all directions, intelligent path planning and multi-stage obstacle avoidance decision are realized, an optimal instruction is output through cooperative control, the reliability is improved by combining virtual verification, and safe and efficient navigation of the unmanned ship in a complex water area is effectively ensured.

The invention relates to the technical field of intelligent warehousing and logistics automation, in particular to a forklift dynamic path planning method based on deep reinforcement learning, and the method comprises the steps: deploying multi-view vision, geomagnetism and other multi-source sensors, achieving data calibration and fusion through employing an insect compound eye-imitating vision model, and constructing a high-dimensional state space vector; heuristic models such as migrant bird navigation and biological stress response are adopted, deep reinforcement learning is combined, decision instructions are generated from the three aspects of path planning, dynamic obstacle avoidance and energy efficiency management, actions are executed through a control system, and deviation is fed back; a reward function is used for evaluating the decision effect, rewards are formed by weighting path efficiency, obstacle avoidance success and energy consumption penalty, the weight can be updated in a self-adaptive mode, and therefore the deep reinforcement learning model is optimized. According to the method, the accuracy, safety and efficiency of forklift path planning are effectively improved, the method can adapt to complex dynamic environments, and the requirements of intelligent logistics and industrial automation for forklift intelligent operation are met.

The invention discloses an AR-fused remote driving vehicle virtual-real interaction hardware-in-the-loop test system, particularly relates to the technical field of automatic driving test, and is used for solving the problems of inaccurate coupling between a virtual scene and a real vehicle behavior and lack of AR prompt response evaluation. The method comprises the following steps: firstly, constructing a dynamic obstacle intention-driven prediction model based on time series data of a multi-modal sensor, and generating a trajectory probability distribution and risk thermodynamic diagram; then, space-time alignment of the virtual accident scene and the real environment is achieved through a dynamic binding algorithm, and the virtual-real shielding priority of an AR interface is dynamically adjusted; by simulating abnormal disturbance of a vehicle actuator, synchronously collecting control and watching responses of a driver, and extracting obstacle avoidance path deviation degree and takeover timeliness parameters; and finally, separating and compensating virtual and actual residual errors based on a path deviation index, realizing online correction of a virtual scene attitude and a dynamic trajectory, constructing a closed-loop optimization mechanism, and improving the precision and stability of a test system.

The invention provides an unmanned aerial vehicle autonomous obstacle avoidance and path planning method and system based on deep learning, and relates to the field of unmanned aerial vehicle control, and the method comprises the steps: obtaining position information and environment perception data, constructing a spatial-temporal feature matrix, extracting target motion and background feature vectors, and mapping the target motion and background feature vectors into a target-environment fusion feature field; calculating an accessibility matrix and a cost matrix to construct a track search space, generating a candidate track set and determining an optimal planned track; and performing segmented optimization on the planned track to obtain a continuous attitude sequence, and generating an adaptive control strategy. According to the invention, intelligent obstacle avoidance and efficient path planning of the unmanned aerial vehicle in a complex environment are realized.

The invention provides a new energy photovoltaic dynamic inspection method and system based on artificial intelligence, and relates to the technical field of photovoltaic power station intelligent inspection. Inspection is triggered according to weather early warning, performance warning or timed tasks; initial path planning is carried out by combining terrain, weather and historical data, and the path is updated by dynamic obstacle avoidance through an RRT * algorithm; multi-modal data, including visible light images, infrared thermal imaging, EL detection data and positioning data, are acquired during inspection of the unmanned aerial vehicle; the unmanned aerial vehicle data and the ground sensor data are integrated to generate a unified fault feature matrix; positioning a defect area in real time by using a deep neural network, judging a defect type and dividing a fault level; and finally, the health degree of the photovoltaic system is scored according to the fault level, and the safe operation trend is analyzed. The multi-modal data real-time fusion and dynamic path planning are realized, the fault identification precision and the inspection efficiency are improved, the manual inspection cost and risk are reduced, and powerful support is provided for intelligent operation and maintenance of a photovoltaic system.

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 relates to the technical field of low-altitude aircraft navigation, and discloses a low-altitude aircraft track real-time planning method and system. The system comprises a flight situation awareness module, a track constraint calculation module, a real-time track planning module, a conflict prediction module and a track dynamic correction module. The flight situation sensing module generates a flight situation matrix through multi-source data fusion; a track constraint calculation module extracts static obstacle contours and dynamic obstacle tracks according to the static obstacle contours and the dynamic obstacle tracks, and generates a multi-dimensional track constraint set in combination with aircraft performance parameters; the real-time flight path planning module builds a three-dimensional flight path search domain by using an adaptive space division technology, and iteratively solves an optimal flight path sequence by using an intelligent search algorithm; the conflict prediction module combines the real-time dynamic obstacle trajectory to calculate the space-time proximity, and generates a conflict early warning map; and the track dynamic correction module re-draws an obstacle avoidance constraint area according to the map, and triggers local track correction. The system improves the comprehensiveness, real-time performance and safety of flight path planning, and guarantees the stable operation of the low-altitude aircraft.

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 invention relates to an unmanned aerial vehicle scheduling management and control method and system based on a nest, and the method comprises the following steps: S1, segmenting an inspection region, and searching an optimal distribution scheme of unmanned aerial vehicles and the nest by using improved particle swarm optimization (PSO); s2, the scheduling control center receives the task request, decomposes the task into sub-tasks according to the task requirement, and allocates proper aircraft nests and unmanned aerial vehicles to process tasks according to the real-time states of the aircraft nests and the unmanned aerial vehicles; s3, calling a high-precision map and a path planning algorithm to plan a flight path; s4, the unmanned aerial vehicle receives the task instruction and the flight plan, and navigation and obstacle avoidance are autonomously completed through a GNSS and an airborne sensor; s5, the dispatching center monitors the state of the unmanned aerial vehicle in real time; and S6, after the unmanned aerial vehicle completes the task, selecting an optimal parking point according to the distribution and state of the aircraft nest. The unmanned aerial vehicle cluster efficiency, the dynamic response capability and the task execution reliability are remarkably improved.

The invention discloses a robot dynamic environment adaptive sensing and navigation system based on a three-dimensional laser radar, relates to the technical field of robots, and solves the technical problems that comprehensive environment information is difficult to obtain, and the weight is difficult to adjust by fusing weather types and sensor confidence coefficients. Comprising the following steps: generating original point cloud data by combining a bionic compound eye type laser radar with a silicon photon integrated chip; marking point clouds based on a KITTI data set, performing preprocessing, training a Transform model to output a dynamic obstacle mask, and filtering background point clouds; a weather detection model is constructed, the weight is dynamically adjusted according to the weather type and the sensor confidence coefficient, and position and attitude estimation is fused; laser radar point cloud constructs a geometric map, a camera depth map generates a dense map, and semantic tags are mapped to generate an environmental semantic map; and converting the environmental semantic map into a three-dimensional grid map, and planning an obstacle avoidance path based on the grid map by using an A * algorithm.

The invention relates to the technical field of unmanned aerial vehicle obstacle avoidance, in particular to an AI-assisted unmanned aerial vehicle low-altitude flight obstacle avoidance method and system. The method comprises the following steps: carrying out real-time unmanned aerial vehicle low-altitude flight environment perception based on a multi-modal perception network, and carrying out static obstacle identification and obstacle radiation range analysis to obtain a plurality of static obstacle radiation paths; carrying out dangerous potential energy field modeling based on the plurality of static barrier radiation paths, carrying out dangerous environment distribution fitting, and constructing a three-dimensional dangerous potential energy map; performing multi-path flight rehearsal according to the three-dimensional danger potential energy map, performing optimal flight path evaluation, and extracting an optimal flight path; and performing unmanned aerial vehicle flight processing based on the optimal flight path, performing dynamic obstacle visual identification and operation path prediction, and constructing a plurality of obstacle movement prediction paths. According to the invention, the task execution efficiency and safety of the unmanned aerial vehicle are improved through rapid and accurate obstacle avoidance decision making of the unmanned aerial vehicle.

The invention relates to the technical field of robots, and discloses a multi-robot cooperation control method and system, and the system comprises an environment sensing module, a robot state monitoring module, a task cooperation center, a real-time communication network, a cooperation efficiency evaluation module, and a dynamic optimization execution module. A time and energy consumption dual-target optimization model is constructed through a distributed task allocation mechanism, environment obstacle distribution and robot state parameters are fused in real time, the matching degree of task requirements and robot execution capacity can be verified in the initial planning stage, the risk of task interruption caused by sudden abnormity is reduced, and the task planning efficiency is improved. Meanwhile, the task allocation relation is automatically adjusted based on a dynamic priority strategy, and the system resource scheduling efficiency and the energy consumption balance are improved; when a robot moving path is generated, coupling strength analysis is carried out on a path crossing area through a space-time conflict prediction model, and the dynamic obstacle avoidance capability and response real-time performance of the system are enhanced.

The invention discloses a quadruped robot obstacle avoidance control method and system based on path planning, and relates to the technical field of quadruped robot obstacle avoidance control, and the method comprises the steps: obtaining environment point cloud data, image data and quadruped robot motion speed data, and carrying out the terrain semantic segmentation and semantic pixel back projection of the image data, thereby obtaining a quadruped robot obstacle avoidance result; generating semantic point cloud data, combining the semantic point cloud data with the environment point cloud data subjected to motion compensation, and constructing a semantic annotation grid map; performing global path search on the semantic annotation grid map to generate a global smooth path; when the quadruped robot advances along the global smooth path, path nodes on the global smooth path are extracted at a fixed step pitch, the terrain category and the grid occupation state of each path node are judged according to the semantic annotation grid map, and a local reference path is generated; and executing a multi-stage obstacle avoidance strategy on the local reference path, and generating a foot end track sequence.

The invention discloses a V2X-based global path planning method for a mine unmanned driving cloud control platform, and relates to unmanned driving. The V2X-based global path planning method comprises the following steps: constructing a traffic semantic map data structure # imgabs0 #; according to task issuing or operation plan adjustment, generating path request data R, and performing time constraint, resource constraint and path optimality constraint verification on the path request data R; according to the R and # imgabs1 #, adopting a heuristic search algorithm based on a graph theory to carry out optimal path search on the road topological structure, and generating a global path P containing a node sequence and driving parameters; and acquiring obstacle data detected by the vehicle end through a local sensor, performing obstacle avoidance correction through a local path optimization algorithm according to the obstacle data and the global path P, and generating a local path meeting the safety distance constraint and the path deviation constraint. According to the method, on the premise of meeting multi-dimensional coupling constraints such as time-space, priority-resource, safety-efficiency and the like, the optimal driving path dynamically adapting to the complex environment of the well industry and mining is generated.

The invention relates to a dynamic planning method, device and equipment for a low-altitude route of an unmanned aerial vehicle and a medium. The method comprises the following steps: acquiring real-time dynamic environment change data and obstacle distribution information of a low-altitude flight area, constructing a preliminary data set, and generating an initial flight path; according to the initial flight path, predicting energy consumption data, and if the energy consumption data exceeds a threshold value, performing optimization to obtain an optimized path; performing risk assessment quantification, combining a safety margin adjustment mechanism to obtain a safety index, and if the safety index is lower than a threshold value, performing safety distance adjustment to generate a safety path; monitoring flight conditions, generating a condition monitoring update report, acquiring new obstacle position information, and generating a dynamic obstacle avoidance path; and generating an execution instruction set of the unmanned aerial vehicle according to the dynamic obstacle avoidance path. By adopting the method, the dynamic environment real-time adaptability of the unmanned aerial vehicle can be improved, and the route meeting the task requirement can be planned in time when the unmanned aerial vehicle faces sudden obstacles or environment changes.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle intelligent inspection method for construction site operation safety, which comprises the following steps: step 1, deploying an unmanned aerial vehicle, personnel positioning equipment and a base station, and establishing a time synchronization network and a space coordinate conversion model through the base station; step 2, when the unmanned aerial vehicle executes an inspection task, detecting a shielding area in an image in real time based on a preset route, and generating a dynamic obstacle avoidance path according to the three-dimensional model to control the unmanned aerial vehicle to perform multi-view supplementary shooting; and step 3, carrying out space-time alignment on the multi-view supplementary shooting image and positioning data acquired by the personnel positioning equipment. According to the invention, the technical scheme of dynamic shielding perception and multi-view adaptive supplementary shooting is adopted, a high-precision space coordinate system constructed by a reference station and a real-time shielding detection mechanism are combined with dynamic obstacle avoidance path planning, and comprehensive coverage inspection of a high-altitude and hidden operation area is realized.

The invention relates to the field of navigation, and more particularly discloses a gradient optimization driven unmanned aerial vehicle real-time obstacle avoidance multi-stage trajectory planning method, which comprises the following steps of: in a trajectory initialization stage, firstly, searching a collision-free geometric path considering steering limitation of an unmanned aerial vehicle on an occupied grid map by utilizing an improved algorithm to generate an initial B-spline control point; then, in a trajectory optimization stage, constructing a multi-target trajectory optimization problem, introducing an obstacle avoidance constraint based on an Euclidean distance field map, and optimizing the initial control point set in combination with unmanned aerial vehicle parameters and optimization weights to obtain a trajectory meeting an obstacle avoidance requirement; and finally, for the optimized trajectory, performing dynamic feasibility evaluation based on unmanned aerial vehicle kinematics limitation in a trajectory correction stage, and if the trajectory does not meet the constraint, performing correction based on the minimum curvature constraint on the control point to ensure that the finally generated trajectory is not only obstacle-avoiding but also feasible in dynamics, and finally, determining that the trajectory does not meet the constraint. Therefore, the real-time obstacle avoidance capability of the unmanned aerial vehicle in a complex environment is effectively improved.

The invention relates to the technical field of robot intelligent control, in particular to a humanoid robot multi-modal environment sensing and self-adaptive chassis control method, which comprises the following steps: monitoring a contact force vector and a sliding trend in real time through a multi-dimensional touch sensor, mapping pose drift to a chassis coordinate system based on a Lie group constraint space-time synchronization layer, and performing multi-modal environment sensing and self-adaptive chassis control on the basis of a multi-dimensional touch sensor; a compensation vector is generated, a three-level tactile response layer dynamic switching force / bit mixed impedance mode is adopted, reverse translation of an omnidirectional chassis is combined to counteract drift, compensation parameters are optimized through a depth deterministic strategy gradient algorithm, and the system ensures control instruction time sequence alignment through a time-space stamp synchronization engine. The attitude oscillation in the compensation process is suppressed by using the inertial measurement unit, the obstacle avoidance interference domain is constructed based on the kinematics chain of the operation arm, and the compensation trajectory is smoothed by using the B-spline curve, so that the precision, stability and safety of the humanoid robot in the complex contact operation are improved, and an efficient and reliable solution is provided for a man-machine cooperation scene.

The invention relates to the field of unmanned aerial vehicle monitoring, in particular to a low-altitude unmanned aerial vehicle flight monitoring method and system. A low-altitude unmanned aerial vehicle flight monitoring system comprises an obstacle sensing module, a confidence coefficient calculation module, a risk assessment module, an obstacle avoidance distance adjustment module and a flight mode switching module. According to the invention, by quantifying the rainfall interference index, the visibility index, the illumination intensity index, the obstacle complex index and the electromagnetic interference index, the confidence coefficient weight of the millimeter wave radar, the visual sensor and other multi-element sensing equipment is calculated in real time, so that the limitation of traditional fixed priority fusion is broken through; the optimal sensor data can be automatically selected as an obstacle avoidance decision basis according to actual environmental conditions, misjudgment or delayed response caused by sensor conflicts is avoided, the obstacle avoidance decision precision is improved, and the method is particularly suitable for high-reliability flight in complex environments such as urban canyons.

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 multi-aircraft distributed decision-making swarm route avoidance cooperative control method and system, relates to the technical field of unmanned aerial vehicle formation control, and is used for multi-aircraft flight formation. Each unmanned aerial vehicle is provided with an intelligent decision-making module with environment sensing, communication and path planning capabilities; the method comprises the steps that each unmanned aerial vehicle generates a preliminary avoidance path by adopting an improved genetic algorithm based on acquired local environment information and received state information of neighborhood unmanned aerial vehicles in combination with a preset avoidance rule and a preset multi-objective optimization function; the optimization objectives of the preset multi-objective optimization function comprise an obstacle avoidance safety distance, an energy consumption coefficient, a formation retention degree and task timeliness; space-time conflict detection is executed based on the multiple preliminary avoidance paths, the preliminary avoidance paths are optimized based on conflict detection results, a final conflict-free optimal path is obtained and broadcasted to other unmanned aerial vehicles in the formation, and distributed collaborative decision making is achieved; according to the invention, the control efficiency of multi-unmanned aerial vehicle formation control is improved.

The invention relates to the technical field of mechanical arm obstacle avoidance path planning, in particular to a multi-degree-of-freedom mechanical arm obstacle avoidance path planning method based on three-dimensional reconstruction, which comprises the following steps: step 1, three-dimensional environment perception and dynamic modeling; preferentially offsetting and expanding the near-obstacle nodes towards the concave area or the hole center to generate a candidate node set; and 4, three-dimensional grid collision verification and safe path correction are conducted, specifically, the working space of the mechanical arm is divided into three-dimensional voxel grids, and collision detection is achieved by judging whether path nodes fall into obstacle object elements or not. According to the method, the laser radar and the depth camera are adopted to synchronously collect data through hardware triggering, statistical filtering denoising and three-dimensional grid modeling are combined, geometrical characteristics of static obstacles and motion parameters of dynamic obstacles are restored, and the collision risk caused by environmental perception errors of the mechanical arm is effectively avoided.