359 results about "Collision free" patented technology

The invention discloses a mechanical arm path planning method based on Transform and a diffusion model, and belongs to the technical field of robots, and the method comprises the following steps: collecting environment information to generate a reference path, and constructing a training data set; a conditional diffusion Transform prediction network is constructed, features are extracted, and multi-modal path prediction is realized; gaussian noise is applied to the reference path, and denoising training is carried out on the conditional diffusion Transform prediction network; in combination with a diffusion model and a cost guidance mechanism, optimizing a noise path sampled from Gaussian distribution until a smooth collision-free path is generated; the candidate paths are evaluated, and a mechanical arm joint control instruction is generated; and the mechanical arm executes the planning track and carries out real-time sensing and online re-planning. According to the method, environment perception, Transform coding and diffusion generation are organically combined, a plurality of feasible tracks which are smooth and capable of avoiding obstacles are rapidly generated in a complex obstacle scene, and the method has good generalization ability and can adapt to different scenes and dimension changes.

The invention provides a robot skill learning and control method and device oriented to rich contact operation tasks. The method provided by the invention comprises the following steps: collecting human demonstration data; the human demonstration data at least comprises end effector pose data and stiffness matrix data; on the basis of the end effector pose data and the stiffness matrix data, a generalization motion track and a generalization stiffness track are obtained through time alignment, probability distribution modeling and new state constraint adaptation; constructing an obstacle model through environmental perception, taking the generalized motion trajectory and the generalized rigidity trajectory as reference sampling candidate trajectories, screening a collision-free trajectory in combination with motion consistency, rigidity consistency and smoothness cost, and performing iterative optimization to obtain an optimized motion trajectory and an optimized rigidity trajectory; and converting the optimized motion track and the optimized rigidity track into a robot joint torque instruction based on variable impedance control, and controlling the robot to execute a rich contact operation task based on the robot joint torque instruction.

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 a robot dynamic grabbing control method based on visual sense and tactile sense depth fusion. The method comprises the following steps that firstly, based on visual information, surface geometric features of a target object are extracted, grabbing adaptability scores are calculated, an optimal grabbing point is selected, and a collision-free approaching track is planned; secondly, in a contact establishment stage, detecting a contact event through a touch sensor, fusing vision and touch data to unify a coordinate system, calculating a vision-touch consistency score, and applying an initial grabbing force; and thirdly, in the stable holding stage, slip detection is conducted through wavelet packet energy entropy and pressure gradient, and the grabbing force and impedance parameters are dynamically adjusted in combination with self-adaptive impedance control. According to the method, intelligent control over the whole process from approaching to contact to stable holding is achieved, and the adaptability, stability and safety of robot grabbing in the dynamic environment are improved.

The invention discloses a non-standard weldment-oriented robot efficient collision-free path planning method and system, and belongs to the technical field of robot welding, and the method comprises the following steps: S1, obtaining three-dimensional model data of a non-standard weldment and an operation environment, and extracting welding seam point cloud data; s2, carrying out feature extraction and downsampling on the welding seam point cloud data to generate a group of key path points; s3, global optimization is conducted on the welding path through an improved genetic algorithm, candidate paths are generated, and the welding sequence of the multiple sections of welding seams and the welding gun postures at the key path points are optimized at the same time through the improved genetic algorithm; s4, evaluating the candidate paths generated by the improved genetic algorithm through a multi-target fitness function including path length, attitude stability and collision risk; and S5, outputting an optimal collision-free welding path according to an evaluation result, and converting the optimal collision-free welding path into a smooth track which can be executed by the robot. According to the invention, the high-efficiency, high-quality and collision-free welding gun motion trail can be automatically generated.

The invention discloses a truck-mounted crane group operation safety monitoring system and method based on multi-source perception and collaborative decision, and particularly relates to the field of safety monitoring, the truck-mounted crane group operation safety monitoring system comprises a distributed perception module, an edge calculation module, a graded early warning and execution module, a digital twin platform module and a man-machine interaction module; collected multi-source data are fused through Kalman filtering and then transmitted to an edge computing node; generating a dynamic safety envelope region based on an adaptive kinematics model, predicting a group conflict risk through a graph theory algorithm, and calculating collision time; the digital twin platform fuses a BIM model and real-time data, hoisting path rehearsal, load swing prediction and foundation bearing capacity visualization are carried out, an improved RRT * algorithm is adopted to plan a collision-free path, and a Lagrange dynamics model is adopted to predict a swing track; the grading early warning module triggers differential response according to the risk grade; and risk warning and path guidance are provided through the AR-HUD and the three-dimensional situation billboard.

The invention relates to an unmanned aerial vehicle cluster safety path planning reinforcement learning method and device. The method comprises the following steps: inputting unmanned aerial vehicle group states, task targets and environment observation information into a deep reinforcement learning strategy of a continuous action space, and generating a global guidance quantity; an explicit security constraint set between the unmanned aerial vehicles and between the unmanned aerial vehicles and obstacles is constructed, through graph structure modeling and a GNN adaptive mechanism, the unmanned aerial vehicles / obstacles are represented by dynamic graphs, and neighborhood and relative position speed information is utilized to predict CBF parameters; and performing safety projection correction on the guide quantity based on the constraint and the CBF parameter to obtain an actual control instruction corresponding to the safety path. After the instruction is executed, feedback information such as state deviation and constraint tensity is collected and used for adjusting a reinforcement learning strategy, loop iteration is carried out to continuously output an optimization path, and whole-course collision-free planning of the cluster is achieved. By adopting the method, stable and reliable safety protection in a complex dynamic environment can be realized.

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.

The invention discloses an assemblage multi-robot collaborative welding system in ship sections, and relates to the technical field of industrial robot collaborative control, the assemblage multi-robot collaborative welding system comprises an industrial internet platform, the industrial internet platform is in communication connection with the following modules: a visual inspection modeling module, which is used for scanning the surface of a workpiece through a 3D camera, constructing a point cloud model, and establishing a point cloud model; matching the point cloud model with a design drawing, and identifying a deviation between an actual workpiece and the drawing; and the path planning and correcting module is used for planning a collision-free welding path in combination with the visual detection data and the motion range of the robot. Through high-precision visual inspection scanning and an SLAM matching algorithm, a workpiece point cloud model is constructed in real time and is accurately aligned with a design drawing, the welding seam position and the workpiece geometric deviation can be accurately recognized, a collision-free welding path is generated in combination with robot kinematics constraint and a safety boundary, it is ensured that the pose of the tail end of a welding gun is attached to the surface of the workpiece, and the welding quality is improved. Welding deviation is reduced, and process stability is ensured.

The invention relates to the field of intelligent traffic control, discloses an intelligent control method and system for a bidirectional driving wheel type sightseeing train, and aims to solve the problems of low efficiency, poor safety, weak adaptability and the like caused by dependence on a fixed track, path solidification and control lag of a traditional sightseeing train. According to the method, a dynamic environment map is constructed by deploying a front-back symmetric multi-mode sensor array and fusing laser radar, vision, millimeter wave radar and inertial data; an improved mixed A star algorithm is adopted to plan a global collision-free trajectory, high-precision tracking is realized in combination with model prediction control, bidirectional autonomous operation, multi-vehicle intelligent cooperation and full-scene adaptive control under a trackless condition are realized, and the traffic efficiency, safety and intelligent level of a scenic spot are remarkably improved.

The invention provides a motion control method of a surgical robot and a surgical robot system. In accordance with an example of the present application, the surgical robotic system includes a manipulator assembly that is movable under actuation of an actuator; the method comprises the following steps: determining whether the manipulator assembly collides or not, wherein the collision comprises an imminent collision or an already-collided collision; in response to the collision of the manipulator assembly, adjusting a control parameter of the actuator to reduce a current output of the actuator; the manipulator assembly is actuated by the actuator to move. The motion state of the manipulator can be adjusted in time, and damage caused by collision is effectively reduced; meanwhile, adjustment can be carried out when it is detected that the manipulator assembly collides, the motion tracking performance of the manipulator assembly is effectively maintained without the collision risk, and the accuracy of the operation is improved.

The invention discloses a mechanical arm autonomous grabbing system and method based on visual guidance and visual touch fusion. The mechanical arm autonomous grabbing system at least comprises a visual semantic guidance and grabbing pose generation module, a smooth grabbing control module based on visual touch fusion and a dynamic planning module based on model prediction path integration. The visual module efficiently generates a six-degree-of-freedom grabbing pose from the point cloud data by using a deep learning network; the smooth grabbing control module monitors object slippage in real time and adaptively adjusts clamping force by fusing visual and tactile information, and stable grabbing of fragile or slippery objects is achieved. And the dynamic planning module adopts a model prediction path integral algorithm MPPI to realize collision-free path planning of the mechanical arm in a complex dynamic environment. The problems that an existing mechanical arm is insufficient in sensing capacity, poor in grabbing robustness and low in path planning efficiency in an unstructured scene are solved, and the autonomous operation capacity and safety of the mechanical arm in the scenes of accompanying, intelligent sorting and the like are remarkably improved.

The invention discloses a virtual simulation and space collision early warning method for a gas turbine unit hoisting path, and belongs to the technical field of engineering assistion.The method comprises the steps that firstly, models of a gas turbine unit, hoisting equipment and an operation environment are built through high-precision three-dimensional modeling, and an initial hoisting path is planned based on the models; subsequently, the hoisting process is simulated through virtual simulation, preliminary collision detection is carried out, and the path is verified and optimized, so that an optimized hoisting path which is free of collision in a simulation system and meets a safe distance is generated, and the reliability and the safety of the scheme are remarkably improved. The whole-course intelligent safety guarantee of hoisting operation from planning to execution is achieved, collision accidents are effectively avoided, and engineering risks and economic losses are greatly reduced.

According to the motion planning method and device for the multi-mechanical-arm cooperative task, a large number of potential conflicts are actively avoided in the early stage of task allocation through division of a virtual wall and a safety area and a danger area, the difficulty and the calculated amount of follow-up track collaboration are remarkably reduced, and the success rate and the efficiency of planning are improved. Cost and load balance are considered in the task allocation stage; in the path point sorting stage, minimizing the total movement distance and the total completion time is taken as a target; and track generation adopts a time optimal algorithm. The layered decoupling design avoids the huge calculation overhead of centralized planning, and meanwhile, the quality of a final solution is ensured through the optimization strategy of each stage. The innovative cost function takes collision distance into consideration, and guides the planner to select a safer path. Through layered and ordered collision solution strategies, such as deceleration, local re-planning and optimal waiting, it is ensured that a collision-free solution can be always found in a complex dynamic environment, and robustness is high.

The invention discloses a robot grabbing pose generation method and device, and the method comprises the steps: obtaining a natural language grabbing task instruction, and carrying out the analysis based on a large language model, and generating a structured task parameter containing target object identification information and grabbing constraint information; performing open vocabulary target detection on the image based on the identification information, and obtaining a target area mask in combination with pixel-level segmentation; generating a target point cloud based on the mask, the depth map and the camera internal reference, carrying out depth completion or denoising, outlier elimination and threshold confirmation filtering, inputting the processed target point cloud into a grabbing pose prediction network, outputting a plurality of candidate six-degree-of-freedom grabbing poses, carrying out constraint screening according to grabbing constraints, and carrying out collision detection; and selecting the candidate with the optimal confidence coefficient from the collision-free candidates as the optimal grabbing pose to be output. Task constraints penetrate through a target extraction process, a point cloud construction process and a pose screening process, and point cloud processing and collision verification are combined, so that an executable six-degree-of-freedom grabbing pose which meets the constraints is output.

The invention belongs to the technical field of unmanned aerial vehicle path planning, and particularly relates to a deep reinforcement learning multi-unmanned aerial vehicle path planning method fused with an artificial potential field, which comprises the following steps: S1, constructing a three-dimensional simulation environment containing static and dynamic obstacles, and constructing a system model for performing path planning on an unmanned aerial vehicle (UAV) in an unknown environment, and constructing an optimization problem of UAV path planning based on the constructed system model, and aiming at minimizing task completion time and avoiding obstacles. According to the method, the artificial potential field resultant force is embedded into the state space of the TD3 algorithm, and the multi-dimensional reward function is designed, so that the problems of low training efficiency and insufficient obstacle avoidance capability of traditional deep reinforcement learning are solved. Experiments show that the task success rate of the method in a dynamic obstacle scene is improved by 10%-30%, the collision rate is reduced by 20%-50%, a smooth collision-free trajectory can be generated in real time, and the method is suitable for collaborative navigation and emergency rescue tasks of a multi-unmanned aerial vehicle cluster.

The disclosure relates generally to methods and systems for optimizing multi-robot task allocation through heuristic-guided reinforcement learning in dynamic environments. Conventional RL framework-based approaches focus on optimal task selection, neglecting task-to-robot assignment under the assumption of constant robot availability post-selection. The present disclosure solves the technical problems in the art through heuristic-guided reinforcement learning (RL) in dynamic environments. The methods and systems of the present disclosure (coined as HeuRAL-MATE) combine heuristic guidance with RL to address the multi-robot task allocation challenge in warehouse environments. The HeuRAL-MATE effectively manages real-time task selection, the allocation of tasks to robots, and the secure navigation of robots, by minimizing both the total travel distance of robots and the delay in task execution while considering practical charging / discharging constraints and collision-free navigation of the robots.

The invention provides an unmanned aerial vehicle and unmanned tractor collaborative operation method, which comprises the following steps: step 1, scanning and constructing a two-dimensional grid map of a field environment through a laser radar installed on an unmanned aerial vehicle, and sending the two-dimensional grid map to an unmanned tractor; step 2, the unmanned tractor carries out field full-coverage path planning based on the two-dimensional grid map and by considering tractor steering constraints; step 3, performing a path tracking task by the unmanned tractor based on field full-coverage path planning; and 4, when the path tracking task is carried out, information obtained by real-time scanning of a laser radar carried by the robot is fused, obstacle detection is carried out, and the path is dynamically updated to be a collision-free path according to the obstacle.

The invention relates to the technical field of digital manufacturing and precision machining, and discloses a part machining method and system based on inspection planning and feature driving, and the method comprises the steps: reading a full three-dimensional model, analyzing a tolerance mark, instantiating the tolerance mark into a procedure inspection item node, and building a persistent reference; identifying geometric features and matching the geometric features with the inspection items to generate a to-be-tested feature set; measuring point distribution is calculated in a self-adaptive mode according to the feature types, and a collision-free measuring path is generated; converting the path into a numerical control code containing a radius compensation algorithm and embedding the numerical control code into a machining program; after the numerical control center executes measurement, abnormal data calculation deviation is eliminated, and a compensation value is written into a register to correct parameters. By establishing full three-dimensional model data association and applying curvature adaptive sampling and statistical data cleaning strategies, data faults of design and detection are eliminated, the problem of environmental noise interference is solved, and the precision control level of complex feature processing is improved.

The invention discloses a mechanical arm tail end posture constraint path optimization method based on constraint manifold. A discrete constraint manifold point set which is high in posture precision, robust in singular point, uniform in space distribution and constrained in kinematics is generated based on a Monte Carlo method, a weighted damping pseudo-inverse method, an attenuation type learning rate gradient descent iteration method and voxel density optimization; based on measurement of a laser tracker, the relation between static environment information and a target point relative to a mechanical arm base coordinate system is constructed; a coarse path is obtained through a sampling method, and a feasible path on a constraint manifold meeting attitude constraint and having no collision is generated based on a search strategy of Euler distance difference + maximum joint angle variation + collision detection + index reconstruction criterion; and in combination with a multi-target weighted CHOMP algorithm, the feasible path is optimized into a collision-free and smooth path which meets the requirements of attitude constraint and kinematics constraint. According to the method, the problems of insufficient consideration of attitude constraint, low efficiency, local convergence and the like of traditional path planning are solved.

To provide a method for generating a dataset of robot motion programs for training a path generation neural network.SOLUTION: A large language model uses a robot task library and an asset geometry database as inputs. The large language model breaks down a task into steps, and then generates code describing a robot and object motion to complete the task. The generated code produces robot motions for the task, and a corresponding robot motion program is created and executed in simulation. The simulated robot motion programs are used to generate collision-free robot paths via RRT and / or optimization, and the collision-free paths are validated for robot reachability and object placement success. The validated motion programs are added to a dataset and used for training a path generation neural network.SELECTED DRAWING: Figure 3

The invention discloses an additive and subtractive composite manufacturing process planning method for element runner structure features, which comprises the following steps: S1, analyzing the feature structure of a runner, optimizing the runner for additive and subtractive composite manufacturing, and splitting a multi-runner structure into a plurality of single-runner structures; s2, a machining strategy is set, namely, the single runners are machined one by one in sequence, and a single-runner additive machining strategy is machining along a centroid shaft; s3, developing a collision detection algorithm for flow channel additive and subtractive machining, and circularly solving a machining area segmentation plane by using the collision detection algorithm along a flow channel centroid shaft to obtain a flow channel additive and subtractive alternate area division scheme; s4, generating specific additional material and subtractive material processing procedures; and S5, post-processing to generate a processing code, and importing the processing code into additive and subtractive composite manufacturing equipment for processing. According to the invention, the process planning result without collision and with high processing efficiency can be quickly and efficiently obtained.

The invention belongs to the field of unmanned aerial vehicle collaborative search, and particularly discloses an unmanned aerial vehicle water surface target collaborative search method, which comprises the steps of 1, constructing a multi-dimensional dynamic environment model, including dynamic environment modeling based on dynamic environment parameters; step 2, constructing a heterogeneous platform kinematic model based on the multi-dimensional dynamic environment model constructed in the step 1; step 3, constructing a distributed cooperative task allocation strategy based on the heterogeneous platform kinematic model constructed in the step 2; 4, predicting a target track by adopting an extended Kalman filtering algorithm to obtain optimal state estimation; and step 5, based on the optimal state estimation obtained in the step 4, performing collision-free path planning by adopting a TangentBug real-time obstacle avoidance algorithm. By adopting the scheme provided by the invention, the problems of difficulty in adapting to a dynamic environment, accurately predicting a target trajectory, robustly allocating tasks and safely avoiding obstacles in a complex ocean scene in the prior art can be solved.

The invention discloses a multi-robot affine formation path optimization method driven by a virtual navigator, and the method comprises the steps: firstly setting a point set composed of the virtual navigator and followers, enabling the virtual navigator to form a convex hull and meet an affine localization condition, so as to guarantee that the followers are always located in the convex hull; generating a collision-free reference trajectory of the mass center of the virtual navigator based on a fast random tree algorithm; constructing a nonlinear constraint optimization problem NCO which contains linear transformation constraint and meets smoothness, obstacle avoidance, speed and angular speed constraint; and establishing a communication graph of the virtual navigator and the follower, calculating balance stress, designing a smoother follower distributed differential speed control law, and realizing formation path optimization and obstacle avoidance control. Compared with an existing method, the formation path generation method has the advantages that the smooth and stable formation path fitting the reference trajectory can be generated while obstacle avoidance and physical feasibility are guaranteed, and the cooperative motion capability and the control performance of a multi-robot system in a complex environment are improved.

This invention discloses a method for planning U-turns of automated vehicles in narrow passages in mines, comprising: S1, determining the starting area for U-turn planning; S2, performing tunnel area planning to generate interval navigation paths and local paths; S3, performing chamber area planning: the automated vehicle travels to the starting area for U-turn planning based on the tunnel area planning results, establishes a discrete map of the chamber planning area and sets a step size Sn, expands nodes until the target point is reached, and generates a collision-free path; S4, post-processing the collision-free path generated in step S3: a nonlinear optimization planning problem is modeled using a constructed objective function for the discrete points of the collision-free path, and a corresponding solver is used to obtain the final smooth path solution. This method can quickly and effectively complete U-turn trajectory planning in narrow areas using the chamber space, achieving relatively coherent U-turn actions, ensuring the real-time performance of the planning algorithm, and improving vehicle traffic efficiency.

The invention discloses an intelligent feeding and discharging management method and system for numerical control machining based on task optimization, and relates to the technical field of feeding and discharging management, and the method comprises the steps: deploying P edge task monitoring nodes and P multi-source sensing modules, and constructing a one-way communication link; p real-time sensing data streams are received for machine tool discharging time fusion prediction, P discharging prediction time points are output, P feeding and discharging time windows are generated through fitting in combination with P feeding time consumption features, multi-feeding and discharging task conflict resolution fitting is carried out, and an initial empty coupling collision-free track is output. And performing joint space trajectory compensation according to the processing progress deviation tracking result of the P numerical control machine tools. The technical problems that in the prior art, the waiting time is too long, and task conflicts are difficult to coordinate in the feeding and discharging process of the numerical control machining tool are solved, and the technical effects of improving the feeding and discharging efficiency of the numerical control machining tool, optimizing task scheduling and avoiding collision are achieved.

The invention discloses a multi-AUV global path planning method and device based on an improved GWO algorithm, and a storage medium, and belongs to the technical field of autonomous underwater vehicles. According to the method, on the basis of a grey wolf optimization algorithm (GWO), environmental constraints, distance constraints, energy consumption constraints, constraints among multiple AUVs and the like are considered, the basic grey wolf optimization algorithm in which a nonlinear convergence factor is designed is adopted to update the position, and a Levy flight strategy is introduced to update the position so as to prevent the algorithm from falling into local optimum; a method in which two fitness functions are relatively large is selected for each time of position updating, and a position updating thought of a particle swarm optimization (PSO) is introduced, so that the convergence speed of the algorithm is improved; and finally, a safe, collision-free, time-saving and energy-saving path is generated for each AUV by utilizing a polynomial trajectory generation method, and global path planning of the multiple AUVs in a complex marine environment is realized.

The invention relates to the technical field of automatic driving, and discloses a collision detection method and device, a vehicle and a storage medium, and the method comprises the steps: building a geometric model of an obstacle when a vehicle is in a turning working condition and a sensing system recognizes that the obstacle in front is a concave polyhedron; decomposing the geometric model of the obstacle by adopting a convex decomposition algorithm to obtain at least two convex polygons; constructing a candidate separation axis set based on the geometric model of the vehicle and each convex polygon; performing unitization processing on the vector of each separation axis in the candidate separation axis set, and performing de-duplication on the unitized vector based on a hash algorithm to obtain a de-duplication candidate separation axis set; removing approximately parallel axes according to the included angles between the separation axes in the de-weighting candidate separation axis set to obtain a target separation axis set; and sequentially performing projection judgment on the separation axes in the target separation axis set, and if the projections on any separation axis are not overlapped, judging that there is no collision risk.

The invention discloses a three-dimensional fixed-wing unmanned aerial vehicle path planning method, device and equipment. The method comprises the following steps: generating a three-dimensional occupation grid map; acquiring a starting point and a target point, and respectively calculating the minimum horizontal safety distance between the starting point and the target point and the safety height of the ground surface point under the target point; determining the starting point as a current father node, and determining a current step search direction according to the constructed horizontal position vector from the current father node to the target point; calculating the current maximum collision-free search step length meeting the terrain constraint and the unmanned aerial vehicle path climbing angle or glide angle constraint; determining child nodes of the current father node according to the current father node, the current step search direction and the current maximum collision-free search step size; and determining the child node as a new current father node, repeatedly executing the step of constructing the horizontal position vector from the current father node to the target point until a search termination condition is reached, and controlling the unmanned aerial vehicle to fly from the starting point to the target point. According to the invention, the real-time planning of the path of the unmanned aerial vehicle is realized, and the safety and reliability are improved.

The invention relates to a mechanical arm disordered grabbing algorithm, and belongs to the technical field of robotics, and the method comprises the steps: firstly, obtaining a target material image and a point cloud sample, and training a multi-modal attention fusion perception model through sample enhancement processing and a difficult case mining mechanism after the target material image and the point cloud sample are labeled with categories, postures and grabbing areas; secondly, a mechanical arm obtains operation area data, the operation area data are input into a multi-modal attention fusion perception model to obtain initial attitude parameters, errors are corrected by combining with a visual data fusion calibration model, and the material stacking state is judged through a dynamic reference comparison method; thirdly, based on the correction parameters and the stacking state, an evaluation function is constructed to screen collision-free and stress-balanced grabbing points, and a collision-free path is planned by fusing kinematics constraints of the mechanical arm; and finally, the grabbing action is executed, the strength and the posture are adjusted in real time according to the contact force and the position deviation, the material stability is evaluated through vision after grabbing, and the stability and the operation efficiency of the mechanical arm in a complex disordered scene are improved.