130 results about "Obstacle avoidance algorithm" patented technology

The invention discloses an unmanned aerial vehicle anti-collision method and system based on LoRa communication, and belongs to the technical field of risk avoidance of multiple unmanned aerial vehicles. The method comprises the following steps: constructing an ad hoc network communication architecture to realize flight state broadcast and synchronization; fusing the multi-source data by adopting a space-time alignment algorithm, and constructing a dynamic airspace situation map taking a local machine as a center; calculating a time window and space overlapping probability of the predicted intersection; a multi-priority dynamic negotiation protocol is considered, and autonomous decision making of the unmanned aerial vehicle cluster in a scene without a central node is supported; and dynamically adjusting the flight path, speed and height through a collaborative obstacle avoidance algorithm. Through a spread spectrum modulation technology and an adaptive power control mechanism, over-the-horizon communication coverage of more than 10km level is realized; through fusion of a kinematics prediction model and a risk quantification algorithm, millisecond identification and hierarchical response of collision threats are realized, a global optimal avoidance strategy is achieved, communication delay and calculation bottleneck brought by centralized decision are effectively avoided, and the real-time collaborative obstacle avoidance capability in a complex airspace is ensured.

The invention discloses a multi-unmanned aerial vehicle negotiation anti-collision method and system based on 5G unmanned aerial vehicle communication, and belongs to the technical field of multi-unmanned aerial vehicle risk avoiding. The method comprises the following steps: collecting multi-source flight state data; after the multi-source flight state data are fused through Kalman filtering, an airspace situation map is constructed; a 5G network architecture is constructed, and real-time data interaction between the unmanned aerial vehicles is realized; a dynamic negotiation protocol is constructed, and fast exchange of obstacle avoidance proposals when collision risks are triggered by multiple machines is supported; and constructing a distributed collaborative obstacle avoidance algorithm, and generating a smooth obstacle avoidance path in real time. According to the invention, through a dynamic negotiation protocol, multiple machines are supported to rapidly exchange obstacle avoidance proposals when a collision risk is triggered; a global optimal strategy is generated through edge node intelligent arbitration; the priority mechanism gives consideration to task urgency and environment dynamic change, ensures that a high-value task unmanned aerial vehicle passes preferentially, balances fairness through distributed voting, avoids path stiffness caused by decision conflict or resource competition, and realizes efficient and reasonable collaborative decision.

The invention provides a dynamic obstacle avoidance path planning control method for a redundant mechanical arm, which adopts a self-adaptive artificial potential field dynamic obstacle avoidance algorithm based on a second-order dynamic control barrier function. The problems that a traditional artificial potential field method cannot reach a target, is prone to falling into a local minimum value and cannot effectively deal with a dynamic obstacle are solved. The efficient and safe obstacle avoidance of the mechanical arm in a dynamic complex environment is realized by constructing a self-adaptive velocity repulsion field and a virtual obstacle model, combining a high-order dynamic control barrier function as a safety constraint and utilizing a quadratic programming optimization algorithm. Experimental results show that the method can effectively guide the mechanical arm to escape from the local minimum point, the target point can be reached, meanwhile, the safe distance between the mechanical arm and a dynamic obstacle is guaranteed, the movement efficiency and track quality of the mechanical arm are remarkably improved, and the real-time performance and safety requirements of a dynamic obstacle avoidance task are met.

The invention relates to an unmanned aerial vehicle detection countering method, which comprises the following steps of: deploying a multi-dimensional sensing network to carry out full-band instantaneous scanning, synchronously acquiring noise characteristics of radio signals, material characteristics of a terahertz wave band and Doppler effect characteristics, mapping to a virtual twinborn body, reconstructing physical parameters, a communication protocol and a behavior mode of a target unmanned aerial vehicle, performing depth comparison by combining a disguise sample library generated by the antagonism generation network, and generating a holographic target file containing a three-dimensional motion vector, an energy feature and a potential threat intention; a target quantum encryption communication link is interfered through a quantum entanglement signal, a controllable plasma cloud cluster is generated, navigation and image transmission signals of the controllable plasma cloud cluster are selectively attenuated, the controllability of the target is judged, and an uncontrollable target is repelled through coded sound waves; and sending a control instruction containing a biological heuristic obstacle avoidance algorithm in stages, and guiding the control instruction to an intelligent recovery cabin. According to the invention, accurate detection, intelligent identification, safe countering and reliable takeover of the unmanned aerial vehicle are realized, and the airspace safety guarantee capability is improved.

The invention relates to an unmanned aerial vehicle cluster autonomous crossing method and system. The method comprises the following steps: expanding channel capacity, carrying out time delay analysis, and introducing a non-orthogonal multiple access technology to carry out interference management and resource allocation so as to complete network deployment; establishing a linear state space model of a multi-unmanned aerial vehicle system for the unmanned aerial vehicle cluster, setting a cooperative control strategy and an obstacle avoidance strategy, and performing model performance analysis; acquiring environment information through each sensor arranged on each unmanned aerial vehicle in the unmanned aerial vehicle cluster to obtain an environment state; an artificial potential field method is adopted to complete a dynamic obstacle avoidance algorithm according to environment state design, and the path of the unmanned aerial vehicle is adjusted in real time according to the dynamic obstacle avoidance algorithm; establishing an unmanned aerial vehicle kinematic model, designing an optimization algorithm of path planning based on the unmanned aerial vehicle kinematic model, and performing real-time dynamic path adjustment according to the optimization algorithm; setting a security and fault-tolerant mechanism; and autonomous crossing of the unmanned aerial vehicle cluster is realized. And the high-efficiency collaboration and stability of the unmanned aerial vehicle cluster are improved.

The invention discloses a multi-mode satellite communication-mercuric chloride computing power integrated module and a data processing method, and the module comprises a mercuric chloride NPU chip which is used for operating a scheduling model based on an AI algorithm, carrying out the real-time prediction of an unmanned plane, evaluating the link time delay, bandwidth and power consumption, and carrying out the real-time prediction of the link time delay, bandwidth and power consumption according to a path planning algorithm and an obstacle avoidance algorithm. Dynamically selecting an optimal communication mode; the Tiantong satellite communication module is used for transmitting the state log of the unmanned aerial vehicle; the Beidou positioning and short message module is used for transmitting the position, the state and the emergency message of the unmanned aerial vehicle; the 4G / 5G module is used for transmitting real-time control instructions and data; the IMU module is used for carrying out combined navigation and positioning by combining a Beidou GNSS; and the antenna is used for transmitting and receiving wireless signals. According to the invention, a plurality of communication modes are fused, and protocol scheduling optimization based on mercuric chloride computing power is combined, so that a communication function of wide-area coverage and accurate control is provided for the low-altitude unmanned aerial vehicle.

The invention discloses an agricultural robot control method based on multi-agent cooperation, and the method comprises the following steps: S1, collecting environment data of a farmland region, and carrying out the preprocessing of the environment data; s2, constructing a job task graph, and forming structured task graph data; s3, inputting the operation task graph into the graph neural network model, and performing task initialization on all agricultural robots; s4, sharing the local operation sub-graphs among the agricultural robots, and fusing all the local operation sub-graphs based on a graph neural network model; s5, executing a multi-agent cooperative task division operation, and generating a task allocation matrix; s6, after the agricultural robot receives the operation instruction, executing a path planning and obstacle avoidance algorithm; and S7, summarizing operation states of the agricultural robots in real time, and dynamically updating weight parameters of the graph neural network model according to operation feedback. Efficient cooperation and intelligent scheduling of the agricultural robot are realized, and the operation efficiency and the coverage rate are improved.

The invention relates to the technical field of tower crane control, and provides an aerial high-risk area identification and hoisting path reconstruction method and system. The method comprises the following steps of: respectively mounting two rotary radars on a rotatable bracket of the tower crane to form a radar sensing module comprising a double-radar module; dynamically scanning the potential risk area in the construction scene through a radar sensing module to obtain real-time position information of the potential risk area; receiving the real-time position information through a processing module, and converting the real-time position information into real-time three-dimensional coordinate data in a motion coordinate system of the hoisting equipment by adopting a three-dimensional coordinate conversion algorithm; constructing a three-dimensional model of the potential risk area based on the real-time three-dimensional coordinate data; and identifying the three-dimensional model to determine a high-risk area, adopting an improved fast exploration random tree algorithm as a path obstacle avoidance algorithm, combining the three-dimensional model to dynamically reconstruct an initial hoisting path set by a user, and generating an optimal risk-avoiding hoisting track avoiding the high-risk area.

The invention proposes a robot path planning algorithm based on a particle swarm optimization algorithm and a dynamic window method, and relates to the field of control theories and electronic information, and the method comprises the steps: introducing an inertia weight updating strategy based on a state factor, setting adaptive parameters and crossover and mutation operators to improve the global search capability, increase the population diversity, and improve the robot path planning precision. On-line self-adaptive updating of inertia weight and learning factors is realized on line in combination with Q-learning, gene combination modes are enriched through crossover operators, convergence and exploratory performance of the algorithm are improved, an obstacle avoidance strategy of a traditional DWA algorithm is improved, weight parameters of a dynamic window evaluation function are dynamically adjusted according to real-time information of a target and an obstacle, and an obstacle avoidance algorithm is established. According to the method, the global planning is adopted, the path points generated through global planning are adopted as temporary targets, fusion of MOQLCOPSO and the improved DWA algorithm is achieved, local optimum is effectively avoided, the planning efficiency and path safety are improved, and the method is suitable for mobile robot navigation under the complex three-dimensional terrain.

The invention relates to an indoor unmanned aerial vehicle dynamic obstacle avoidance method and device, a medium and a program product. The method comprises the following steps: acquiring an initial planning path; dynamic obstacle detection is carried out to obtain an obstacle detection result, and the obstacle detection result comprises 4D grid codes corresponding to grid volume elements where one or more detected dynamic obstacles are located; segmenting the initial planning path based on the 4D grid codes of the one or more dynamic obstacles, and determining one or more target road sections needing to be replanned in the initial path; and re-planning the target road section by using an improved A star algorithm and a node search strategy, and combining the re-planned road section with other road sections in the initial planning path to obtain a final path planning result. According to the dynamic obstacle avoidance algorithm for the indoor unmanned aerial vehicle, sudden dynamic obstacles in an indoor environment are effectively dealt with, and path adjustment during dynamic obstacle avoidance is realized.

The embodiment of the invention provides a wheeled robot autonomous obstacle avoidance method and system based on fusion of multiple sensors and a medium, and the method comprises the steps: setting a detection region, obtaining collection data in the detection region based on multiple sensors, and obtaining multi-source data; performing fusion processing on the multi-source data based on a fusion strategy, and analyzing barrier distribution information in the detection area; generating an obstacle avoidance strategy based on an obstacle avoidance algorithm, and generating wheeled robot path planning information based on the obstacle avoidance strategy; comparing the moving track information of the wheeled robot with the path planning information of the wheeled robot to obtain path difference information; analyzing the performance index of the obstacle avoidance strategy, and dynamically correcting the obstacle avoidance strategy and the wheeled robot path planning information; the multi-source data is acquired through multiple sensors, the multi-source data is subjected to fusion processing, and the obstacle avoidance strategy is generated, so that the wheeled robot can plan the obstacle avoidance path more quickly and accurately, unnecessary path adjustment is reduced, the obstacle avoidance time is saved, and the overall moving efficiency is improved.

The invention discloses a real-time collaborative cross-scene visual assistance and environment perception system for visually impaired people based on head-mounted equipment. According to the invention, through fusion of a multi-mode perception technology and a neural feedback mechanism, all-around environmental cognition support is provided for visually impaired people. Key targets such as curbs, steps and traffic signals in a complex scene can be accurately recognized, and a safe navigation scheme is generated in real time in combination with a dynamic path planning and obstacle avoidance algorithm. Through personalized feedback modes such as voice and vibration, the system can adjust the interaction rhythm according to the behavior habit and cognitive state of the user, ensure efficient and natural information transmission, effectively reduce the cognitive load of the user, help the visually impaired people to travel autonomously in different environments, improve the independent living ability, and improve the user experience. Real-time response and calculation efficiency are balanced through a collaborative architecture, so that a high-performance auxiliary function is not limited by heavy hardware equipment any more. The universal design reduces the use threshold, and is helpful for more visually impaired people to enjoy the convenience brought by science and technology.

The invention provides an unmanned aerial vehicle autonomous homing and obstacle avoidance method, system and device and a medium, and the method comprises the following steps: 1, an unmanned aerial vehicle reads elevation data according to the longitudes and latitudes of a target point and a starting point, and converts the elevation data into three-dimensional coordinates; 2, performing global path exploration by taking elevation data represented by three-dimensional coordinates and unmanned aerial vehicle dynamics constraints as restrictions, and generating a reachable route composed of a plurality of waypoints; step 3, smoothing the reachable route to enable the reachable route to become a better homing route; and 4, homing the unmanned aerial vehicle through the homing route, and avoiding obstacles in the homing process through a laser radar and a DWA algorithm. The method is realized based on an elevation map, and relates to an embedded system, laser radar environment perception and an obstacle avoidance algorithm, so that the unmanned aerial vehicle can safely and quickly realize autonomous homing in an unknown environment.

The invention relates to a neural puncture dynamic obstacle avoidance navigation method and system based on multi-modal image fusion, and belongs to the technical field of medical image navigation and surgical operations. A unified space-time reference model is constructed by fusing preoperative high-resolution static images and intraoperative real-time dynamic ultrasonic data; a three-dimensional model of a key nerve and blood vessel structure is automatically segmented and reconstructed by using an artificial intelligence technology, an optimal puncture path can be dynamically calculated based on real-time image data, a collision risk in a needle inserting process can be monitored in real time, and visual navigation guidance of multi-sensory fusion is provided for an operator through an augmented reality technology; by recording and analyzing data of the whole operation process, a big data platform is used for carrying out continuous iterative optimization on a segmentation and obstacle avoidance algorithm, and a self-perfect intelligent closed loop is formed. The problems that in a traditional nerve puncture operation, experience of an operator is relied on, real-time dynamic obstacle avoidance cannot be achieved, and the system lacks learning ability are effectively solved, and the accuracy, safety and intelligent level of the operation are remarkably improved.

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 an unmanned aerial vehicle end-to-end planning method and system based on brain-like calculation and guide learning, and the method comprises the steps: designing an unmanned aerial vehicle planning algorithm based on an end-to-end single stage based on a Spike response model of a pulse neural network and a pulse network model based on Poisson coding; a high-order polynomial trajectory representation and a neural network architecture integrating perception, front-end path search and back-end optimization are provided. In order to optimize a network structure more efficiently, guide learning based on an Actor-Critic framework and privileged learning based on an ESDF map numerical gradient are designed. The system comprises a brain-like chip, a calculation module, an unmanned aerial vehicle body, a task load and the like. According to an unmanned aerial vehicle obstacle avoidance algorithm and a module based on brain-like calculation in an environment with an obstacle, through input of a depth image, a current state and a target direction, and end-to-end output of an unmanned aerial vehicle planning track and a control quantity, rapid real-time autonomous planning is realized, a flight obstacle avoidance effect is improved, and calculation power consumption is reduced at the same time.

The invention discloses a plant protection machine intelligent obstacle avoidance algorithm suitable for a farmland environment, and belongs to the technical field of intelligent agriculture and intelligent agricultural machinery equipment research and development, and the algorithm comprises the steps: employing multiple sensors to collect farmland environment information and plant protection machine state information; performing time synchronization and space registration on heterogeneous data acquired by multiple sensors; processing the images and the point cloud data acquired by the multiple sensors based on a machine learning model, identifying an obstacle and a position and a speed thereof, and marking a crop area; performing global path planning by adopting an improved A * algorithm, and generating a global path of the plant protection machine from the starting point to the target point; re-planning a local path by adopting a D * algorithm; and generating a control instruction to guide the plant protection machine to run along the planned path. According to the invention, the plant protection machine can adapt to the terrain change of the farmland, and also can timely and efficiently cope with the dynamic obstacle and environment change, so that the intelligent level of the plant protection machine is improved.

The invention provides a path planning method, device and equipment and a readable storage medium, and the method is applied to a mobile device, and comprises the steps: obtaining a target map, dividing the target map into a plurality of sub-regions, and enabling the plurality of sub-regions to comprise a starting point region and an ending point region; determining an access point in the starting point area, and determining an outgoing point in the ending point area; a hybrid obstacle avoidance algorithm is utilized to determine an access path between the starting point and the access point and determine an outgoing path between the outgoing point and the terminal point, and the hybrid obstacle avoidance algorithm is a path planning algorithm combining a heuristic search algorithm and vehicle dynamics constraints; determining a main road path between the access point and the outgoing point by using a shortest path search algorithm; and determining a target path of the movable device based on the access path, the main road path and the access-out path. The driving requirements of the movable device can be met while the driving path is optimized.

The invention discloses an autonomous navigation dynamic obstacle avoidance method for an unmanned agricultural machine, and relates to the field of agricultural engineering. The method mainly comprises the following steps: 1, acquiring position information of an operation starting point and an end point of the unmanned agricultural machine, and obtaining an initial track through a path planning module; 2, acquiring obstacle information and road information through a vehicle-mounted laser radar; 3, dividing the vision field of the laser radar, and adopting different obstacle avoidance algorithms for obstacles in different areas; and 4, adjusting the initial planning trajectory by using the obstacle information, the road information and an obstacle avoidance algorithm to obtain a target trajectory. According to the method, the vision field of the laser radar is divided, and different obstacle processing methods are adopted, so that the calculation burden is reduced while effective obstacle avoidance is ensured; besides, by fusing road boundary information, multi-obstacle state estimation and dynamic constraints, a smooth trajectory is generated to adapt to the operation characteristics of the agricultural machine, and the autonomous navigation applicability of the unmanned agricultural machine in a variable farmland scene is expanded.

The invention discloses a full-coverage path planning method and system for a cleaning robot, and the method comprises the following steps: setting a starting position of the cleaning robot, and obtaining the information of a grid environment map; performing full-coverage path planning based on the grid activity value to obtain an initial cleaning path; for a dead zone condition encountered in a task execution process of the cleaning robot, searching a non-traversed grid closest to a current grid based on an improved A * algorithm, searching a path quickly reaching the non-traversed grid, and continuing full-coverage path planning based on a grid activity value; based on a dynamic obstacle avoidance TEB algorithm, designing a reward or penalty function for each influence factor by configuring the weight of each influence factor of an obstacle, a speed and an acceleration, and processing the situation that the cleaning robot encounters the obstacle in the task execution process; the cleaning robot can quickly and efficiently complete the cleaning task.

The invention discloses a disinfection robot dynamic path planning embedded system oriented to a complex environment, and relates to the technical field of path planning, a data acquisition module is used for acquiring environmental data in real time, a dynamic map establishment module is used for establishing a dynamic map by using a real-time positioning and mapping technology, and the dynamic path planning module is used for planning the dynamic path of a disinfection robot. The intelligent planning module executes optimal global path planning according to the constructed dynamic map and calculates an optimal global path from a starting point to a target point, and the obstacle avoidance module avoids sudden dynamic obstacles and dynamically adjusts the advancing route of the robot through a local obstacle avoidance algorithm on the basis of keeping an original path. And the intelligent adjustment module adjusts the moving speed and direction of the robot in real time according to the optimal global path and the dynamic adjustment result. Through combination of global path planning and intelligent regulation and control, the robot not only can plan an initial path, but also can adjust the path in real time according to a dynamic environment in an execution process, and the adaptive capacity of the robot in the dynamic environment is effectively improved.

The invention discloses an automatic guided vehicle intelligent scheduling and path optimization system based on image recognition, and relates to the technical field of intelligent warehousing and logistics automation, and the system comprises an information collection module which is used for collecting a goods taking task signal, goods receiving point image information and AGV operation state data; the intelligent dispatching center is used for performing task distribution, global path planning and dynamic conflict resolution based on the goods taking task signal, the goods receiving point image information and the AGV operation state data and issuing a dispatching instruction; and the task execution and monitoring module is used for issuing a driving instruction to each target AGV according to the scheduling instruction and monitoring the task execution process of the AGV. According to the method, image recognition, statistical modeling and rolling optimization are taken as the core, the improved path optimization and the obstacle avoidance algorithm are combined, a multi-AGV high-concurrency-response, scientific-scheduling, collision-free, optimal-path and high-robustness transportation system is constructed, the transportation efficiency, safety and flexibility are remarkably improved, the intelligent degree is high, and the expandability is high.

The invention discloses a wall tracking flight method for an unmanned aerial vehicle in a closed environment based on visual inertia fusion and an RANSAC algorithm, relates to the technical field of unmanned aerial vehicle autonomous flight, and aims to solve many limitations of an existing unmanned aerial vehicle autonomous obstacle avoidance technology in the aspects of cost, calculation complexity and environmental adaptability. In order to solve the problems that in the prior art, an unmanned aerial vehicle autonomous obstacle avoidance algorithm based on visual inertia fusion and a boundary tracking algorithm based on RANSAC work cooperatively, accurate pose estimation and environment perception are provided through visual inertia fusion, basic data are provided for the boundary tracking algorithm, and the requirement of autonomous flight in a complex indoor environment is difficult to meet. And meanwhile, target point information output by the boundary tracking algorithm is fed back to a path planning module of the autonomous obstacle avoidance system to form closed-loop control. The method solves the problems of high calculation complexity and poor environmental adaptability in the prior art, has the advantages of low cost, high robustness, real-time performance and the like, and is suitable for routing inspection and surveying and mapping tasks of complicated indoor environments such as warehouses, factories and the like.

The invention relates to the technical field of laser radars, and discloses a laser radar beam scanning path optimization and energy efficiency management method based on an FPGA, and the method comprises the following steps: S1, collecting the point cloud data and obstacle distribution information of a target region in real time through an environment sensing module; s2, generating a dynamic scanning path based on space partition, and generating a self-adaptive spiral scanning path for avoiding obstacles; s3, executing multi-parameter collaborative energy efficiency adjustment, and dynamically matching the laser transmitting power, the receiver gain and the MEMS driving voltage; and S4, synchronously outputting the optimized scanning path and the energy efficiency instruction to a laser driving circuit and a light beam deflection controller through a hierarchical scheduling module of the FPGA. A detection area is divided into priority grids through an FPGA, a self-adaptive spiral scanning path is generated, a hardware-level sudden obstacle avoidance algorithm is utilized, the response time is shortened to be smaller than or equal to 5 ms, and the integrity of point cloud data is ensured.

This invention relates to a method for detecting and countering unmanned aerial vehicles (UAVs). It deploys a multi-dimensional sensing network for instantaneous scanning across the entire frequency band, simultaneously acquiring noise characteristics of radio signals, material characteristics in the terahertz band, and Doppler effect characteristics. These are mapped to a virtual twin to reconstruct the physical parameters, communication protocols, and behavioral patterns of the target UAV. A deep comparison is then performed using a camouflage sample library generated by an adversarial generative network to generate a holographic target profile containing three-dimensional motion vectors, energy characteristics, and potential threat intent. Quantum entanglement signals are used to interfere with the target's quantum-encrypted communication link, generating a controllable plasma cloud to selectively attenuate its navigation and image transmission signals. The controllability of the target is assessed, and uncontrollable targets are driven away using coded acoustic waves. Control commands incorporating bio-inspired obstacle avoidance algorithms are sent in stages to guide the UAV to an intelligent recovery pod. This invention achieves accurate detection, intelligent identification, secure countermeasures, and reliable takeover of UAVs, enhancing airspace security capabilities.

The invention provides a cooperative control method for a hexapod robot and a bionic mechanical arm, and the method comprises the steps: firstly transmitting a laser pulse through a laser radar at high frequency, and generating three-dimensional point cloud data based on a plurality of distance measurement points in a space; performing denoising, registration, filtering, simplification and data fusion on the data to construct a complete three-dimensional scene model; generating a digital topographic map containing terrain surface and obstacle information through feature extraction; the main control component controls the movement of the multi-joint legs based on a terrain model: in the walking stage, in combination with global path planning and a local obstacle avoidance algorithm, static obstacle avoidance and dynamic obstacle real-time avoidance are realized; and after the robot reaches the vicinity of the target, the main control component controls the mechanical arm of the robot to grab, and the angle of each joint of the mechanical arm is adjusted by utilizing path planning and an obstacle avoidance algorithm, so that the mechanical arm avoids the obstacle, and the mechanical arm can accurately grab the target object.

The invention discloses a self-adaptive intelligent obstacle avoidance method for a water surface unmanned vehicle, which comprises the following steps: establishing a kinematic model of the water surface unmanned vehicle, delimiting an environment boundary, and presetting a plurality of obstacle avoidance algorithms in a system; sensing an obstacle set in a range through a distance-based intelligent local sensing mechanism, modeling a static obstacle, and performing trajectory prediction on a dynamic obstacle; grading the obstacle environment; calculating an overall threat level; based on an obstacle environment grade division result and an overall threat grade, scoring the calculation applicability of each algorithm, and selecting an obstacle avoidance algorithm according to a scoring result and a historical success rate of the algorithm; and generating or updating an obstacle avoidance path according to the selected algorithm. According to the method, the autonomous obstacle avoidance capability with high safety, good real-time performance and relatively low calculation overhead can be provided for the USV under typical complex sea area conditions such as static-dynamic mixing, dense narrow channels and rapid environment change.

The invention provides an industrial humanoid robot teleoperation cooperation system and method based on multi-modal motion capture fusion, and belongs to the technical field of industrial humanoid robots. According to the invention, an optical motion measurement module is used for collecting whole body position data of an operator in an unshielded scene; the inertial motion capture module is used for collecting joint angle data of an operator in a shielding scene; the mode switching module is used for switching working modes; the environment sensing module is used for collecting environment data, workpiece images and robot tail end contact force information and sending the information to the cooperative control unit. The force feedback device is used for converting the contact force information of the tail end of the robot into tactile vibration and collecting grip strength data of an operator; the cooperative control unit is used for fusing the multi-modal dynamic capture pose data and generating an execution instruction in combination with an obstacle avoidance algorithm and a force control model; the industrial humanoid robot is used for receiving the execution instruction. The operation stability and environmental adaptability of the robot in scenes such as high-precision assembly and heavy carrying can be improved.

The invention discloses an indoor dynamic environment modeling and self-adaptive way-finding obstacle avoidance algorithm based on multi-sensor fusion, belongs to the technical field of intelligent navigation, is suitable for indoor autonomous moving scenes such as service robots and intelligent inspection equipment, and solves the problems of weak dynamic environment modeling capability, poor path planning adaptability and low multi-sensor fusion efficiency in the traditional technology. Fusing ultrasonic wave (20Hz), visual sense (30fps) and IMU (100Hz) data, and realizing state estimation through extended Kalman filtering (Q, R matrix definition); constructing a dynamic map by using improved RANSAC (Random Sample Consensus) denoising and an octree (a time decay factor lambda is equal to e-t / 5); path planning and obstacle avoidance are achieved through an improved A * algorithm (heuristic function fusing path length and other three elements) and a dynamic window method (double constraints), the method is used for indoor autonomous navigation and dynamic obstacle avoidance, the actually measured navigation success rate is 92%, the average curvature of the path is reduced by 30%, the positioning error is reduced to 10 cm, and the operation of embedded equipment reaches 28 FPS.

This invention discloses an iterative learning path tracking control method for USVs based on virtual potential field guidance, comprising: establishing a nonlinear mathematical model of the USV; obtaining the gravitational model and repulsive model of the virtual potential field; obtaining the guidance model of the USV; obtaining the kinematic controller of the USV system; obtaining the dynamic controller of the USV system, and controlling the USV system. This invention's iterative learning path tracking control method for USVs based on virtual potential field guidance, by establishing the gravitational and repulsive models of the virtual potential field of the USV model, incorporates an obstacle avoidance mechanism that considers the shape of the obstacle itself into the guidance algorithm. This solves the problem that most existing adaptive control obstacle avoidance algorithms treat obstacles as point masses, making the obstacle avoidance mechanism of this invention more reliable and enhancing the autonomous collision avoidance performance of ships.