212 results about "Obstacle avoidance algorithm" patented technology

The invention discloses a photovoltaic cleaning robot based on automatic path planning and an operation method thereof, relates to the technical field of robots, and solves the problem of cleaning blind spots in an existing cleaning robot and the endless loop risk of a path planning algorithm. According to the invention, the surface information of the photovoltaic panel is accurately captured through the multispectral acquisition module, a high-resolution and high-definition comprehensive image is generated through the data fusion processing module, then dirt is accurately identified on different scales by using a multi-scale pyramid structure and an adaptive feature fusion mechanism, and an attention mechanism and environmental perception data are combined, so that the real-time performance of the system is improved. The recognition threshold is dynamically adjusted, and misrecognition and missing detection are reduced; and finally, enabling the robot to autonomously learn in a complex environment and optimize an obstacle avoidance strategy through a dynamic obstacle avoidance algorithm based on deep reinforcement learning. The intelligent level and the cleaning effect of an existing photovoltaic cleaning robot are greatly improved.

The invention discloses an unmanned aerial vehicle flight control system and method with precise positioning and autonomous obstacle avoidance, and particularly relates to the field of control, and the system comprises a positioning module, an obstacle detection module, a data processing and analysis module, an autonomous obstacle avoidance algorithm module, and a flight control module. Static and dynamic obstacles are detected in real time by integrating the laser radar, the millimeter wave radar, the visual sensor and the ultrasonic sensor, and an obstacle information report is generated through a data fusion algorithm based on machine learning; based on the processed environment data, the system uses deep reinforcement learning and a model prediction control algorithm to generate a safe flight path in real time, and an obstacle avoidance decision is optimized; the flight control part adopts a hierarchical control framework, and precise flight of the unmanned aerial vehicle is realized through trajectory tracking, attitude control and actuator control.

The invention discloses an intelligent storage robot path planning method, and belongs to the technical field of intelligent robots, and the method comprises the steps: improving a bidirectional A-Star algorithm, and combining a Time Elasticic-Band (TEB) obstacle avoidance algorithm, and providing a new intelligent storage path planning method. The improved A-Star algorithm adopts bidirectional search to replace unidirectional search of a traditional A-Star algorithm; 5-neighborhood search is adopted to replace 8-neighborhood search; introducing a self-adaptive dynamic search factor to improve a heuristic function; and smoothing the path by adopting a redundant node removal and linear interpolation method. The improved bidirectional A-Star algorithm searches to obtain a global optimal path, and the TEB algorithm optimizes and adjusts a local path according to the global path to complete dynamic obstacle avoidance. According to the method, the speed and precision of path planning are effectively improved, the local path can be adjusted in real time to avoid dynamic obstacles, and the requirement for path planning of the intelligent storage robot is met.

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 relates to an unmanned aerial vehicle autonomous flight and inspection system based on a multi-sensor fusion technology, which integrates various sensors such as a visual camera, a laser radar, an inertial measurement unit and a GPS (Global Positioning System), and realizes hierarchical fusion processing of multi-sensor data based on improved adaptive extended Kalman filtering. And a high-precision three-dimensional environment map and pose estimation of the unmanned aerial vehicle are generated in combination with a synchronous positioning and mapping algorithm. And then, the path planning module generates an optimal flight path according to the inspection task area and the environment structure, and automatically calls an obstacle avoidance algorithm to perform dynamic path adjustment when encountering an obstacle. And then, the visual detection module analyzes, identifies and marks the real-time image data through a deep learning algorithm, and locates abnormal conditions in the target area. The system has remarkable advantages in the aspects of inspection precision, stability and adaptability, and is suitable for multiple fields such as geological inspection, road inspection, power inspection, traffic facility detection and disaster monitoring.

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 discloses a vehicle-road collaborative sensing and parking method and system for a shaft mine draw shaft environment, and relates to the field of unmanned driving, and the method comprises the steps: collecting roadside laser radar data, roadside camera data, roadside laser range finder data, vehicle-mounted laser radar data and vehicle-mounted camera data; respectively calculating roadside obstacle information and vehicle-mounted obstacle information; vehicle-road obstacles are matched through a Hungary algorithm, and a vehicle-road collaborative perception result is generated; performing global path planning by calculating an optimal driving path from the starting point to the unloading point by using the global map to generate a global path; performing local path planning through a path obstacle avoidance algorithm to generate a local path; and according to the global path and the local path, controlling the driving path, the vehicle speed and the parking of the unmanned vehicle. In order to solve the problems that in the prior art, an unmanned vehicle cannot comprehensively sense obstacles and cannot stop in place under the mine shaft environment, comprehensive sensing of the obstacles in the mine shaft environment and accurate parking of the vehicle are achieved.

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 discloses a self-adaptive curvature constraint-based autonomous obstacle avoidance smoothness optimization method and system, and the method comprises the steps: firstly, employing the convex hull characteristics of a B spline to efficiently calculate the third-order and fourth-order derivative information of a trajectory, and enabling the third-order and fourth-order derivative information to be the high-order energy information of the trajectory; and then, curvature information is used as a dynamic regulation factor to be introduced into an autonomous obstacle avoidance algorithm smoothness optimization process, so that constraint conditions of different degrees are applied to different sections. Through the self-adaptive segmented optimization strategy, the unmanned aerial vehicle can generate a flight trajectory which is integrally smooth and meets dynamic constraints in a real-time trajectory planning environment, the flight efficiency, safety and reliability are improved, and the algorithm can effectively reduce violent acceleration change and mechanical structure vibration caused by trajectory abrupt change. The service life of the unmanned aerial vehicle flight platform is prolonged; and meanwhile, the energy consumption in the flight process is reduced, and the inspection duration and the operation range of the unmanned aerial vehicle are prolonged, so that the practicability and the economical efficiency of the autonomous inspection task of the unmanned aerial vehicle are remarkably enhanced.

The invention relates to the technical field of intelligent warehousing systems, and discloses a small intelligent central storage system. Comprising a central three-dimensional warehouse management module, a workbin robot control module, a warehouse management module, an AGV scheduling module and a sliding table control module. The central three-dimensional warehouse management module generates a distribution instruction according to the material attribute and a preset rule; the workbin robot control module receives the instruction and controls a robot to carry a workbin through a dynamic obstacle avoidance algorithm; the warehouse management module generates a warehouse-in and warehouse-out instruction and associates material information; the AGV scheduling module plans an AGV transportation path by using a topological map and an ant colony algorithm; the sliding table control module responds to the operation signal, controls the sliding table and triggers the positioning signal. The system can accurately store and distribute materials, efficiently carry the materials, intelligently dispatch the AGVs and achieve accurate sliding table positioning, the warehouse management efficiency is effectively improved, the cost is reduced, and the cargo safety is guaranteed.

The invention relates to an unknown dynamic environment mobile robot path planning method based on reinforcement learning, and belongs to the technical field of path planning. According to the method, a two-layer reinforcement learning structure is adopted, a bottom layer module learns global path planning, and an ellipse specific and precise strategy and a GliEF-Dyna-Q algorithm are provided in the early stage; the high-level module learns an obstacle avoidance rule to complete local dynamic obstacle avoidance; according to the GliEF-Dyna-Q algorithm, elliptic geometric knowledge is introduced to design an elliptic knowledge special and precise strategy to narrow the sampling range, a reward mechanism and path search are constrained and optimized through global and local perspectives, and an improved SA-epsilon greedy strategy is fused into Dyna-Q to achieve efficient global path planning. The high-level module provides a brand-new obstacle avoidance behavior reward mechanism and a more adaptive value function updating method so as to carry out comprehensive local obstacle avoidance activities. According to the invention, the robot can more efficiently realize obstacle avoidance and complete optimal path driving in a dynamic environment.

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 a lightweight unmanned aerial vehicle visual obstacle avoidance method and device based on improved YOLOv8. The method comprises the steps that an improved YOLOv8n network structure is used for obstacle detection; fusing the detected obstacle information and the original depth information, and processing the original depth information based on adaptive distance detection of obstacle attributes to obtain the distance between the unmanned aerial vehicle and the obstacle; designing an unmanned aerial vehicle visual obstacle avoidance algorithm according to the relative position relationship between the obstacle and the unmanned aerial vehicle and the safe flight area without the obstacle, and obtaining the safe flight area; and outputting the next obstacle avoidance instruction, the obstacle information, the standard detection frame, the obstacle type, the confidence coefficient, the distance value between the unmanned aerial vehicle and the obstacle and the maximum obstacle-free area. The device comprises a processor and a memory. According to the method, the problems of large depth estimation error of a monocular camera and high cost of an RGB-D camera are solved, and the binocular camera is adopted for detection and depth information acquisition; the method gives consideration to the detection performance of irregular obstacles and small targets.

The invention discloses an unmanned aerial vehicle obstacle avoidance method based on obstacle state prediction. The method comprises the following steps: acquiring an environment depth image by using a depth camera and performing point cloud conversion; cutting and filtering the original point cloud information; clustering the filtered point cloud data to obtain the position and size information of a single obstacle; tracking and matching the same obstacle in continuous frames of the camera, and recording historical state information; designing an adaptive Kalman filtering algorithm, and predicting a future motion state based on the historical state information of the obstacle; based on the predicted motion state of the obstacle, an unmanned aerial vehicle speed obstacle avoidance algorithm in a three-dimensional space is designed; and re-planning a flight path based on the calculated speed direction for avoiding the obstacle.

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 discloses a closed space unmanned aerial vehicle intelligent navigation method and system based on three-dimensional reconstruction, and the method comprises the steps: S1, scanning a closed space based on a sensor, and obtaining environment data in the closed space; s2, constructing a three-dimensional model of the closed space by utilizing a three-dimensional reconstruction technology according to the acquired environment data; s3, according to the constructed three-dimensional model, planning a flight path of the unmanned aerial vehicle based on an A * algorithm; s4, monitoring the change of the environmental data in real time in the flight process, and adjusting the flight path according to the change; and S5, matching the real-time position of the unmanned aerial vehicle with the three-dimensional model to realize accurate navigation. According to the method, the three-dimensional model of the closed space can be constructed based on the three-dimensional reconstruction technology, the unmanned aerial vehicle can obtain more accurate space information, so that accurate navigation is realized, the change of environmental data is monitored in real time, the flight path is adjusted according to the change, an improved fuzzy obstacle avoidance algorithm and a backstepping flight control algorithm are combined, obstacles are effectively avoided, and the unmanned aerial vehicle is more accurate in navigation. And the flight safety is improved.

The invention provides an automatic obstacle avoidance method and system based on user-defined coordinate system navigation, and relates to the technical field of automatic navigation, and the method comprises the following steps: obtaining global position information of a moving unit in a global coordinate system; judging whether the moving unit enters a new scene internal area according to the global position information; if yes, acquiring spatial morphological characteristics of an internal area of the scene into which the moving unit newly enters; according to the acquired information, judging the area type of the internal area of the newly entered scene; establishing a local coordinate system corresponding to the internal area of the newly entered scene according to the area type; and generating a local navigation path in the internal area of the scene based on the established local coordinate system. According to the method, a local coordinate system is defined, a coordinate system better matched with specific task requirements is adopted in a small range, meanwhile, the data processing amount is reduced, path planning and an obstacle avoidance algorithm are easier to implement, and higher flexibility, lower calculation cost and better precision are provided under specific conditions.

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 invention provides an unmanned aerial vehicle emergency processing method and system, and belongs to the technical field of unmanned aerial vehicle emergency management, and the method comprises the steps: detecting historical related parameters and historical environment parameters of an unmanned aerial vehicle, analyzing the historical related parameters and the historical environment parameters, and setting a triggering condition; judging whether a triggering condition is met or not according to the real-time data of the unmanned aerial vehicle, determining a corresponding emergency condition from a condition-condition table, switching to an emergency model according to the emergency condition, and preparing to execute return flight operation; acquiring current position information and environment data, planning a return path by using an obstacle avoidance algorithm, and planning a flight strategy by using an intelligent algorithm; according to real-time relevant parameters of the unmanned aerial vehicle, a landable area around a path is determined, the landable area is screened in real time, a final landing area is determined, and landing operation is executed, so that the autonomous coping ability and safety of the unmanned aerial vehicle in an emergency are improved, and it is ensured that the unmanned aerial vehicle effectively executes tasks in a complex environment.

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 relates to the technical field of robot control, in particular to a networking type multi-machine linkage pay-off robot control system. The system comprises a robot coordination module, a positioning and tracking module, a collision avoidance planning module, a synchronous correction module and a communication coordination module. The robot cooperation module is used for cooperative work among a plurality of paying-off robots and dynamic task allocation; the positioning and tracking module tracks and predicts the motion state of the robot in real time through high-precision positioning and a causal convolutional neural network algorithm; the collision avoidance planning module plans an optimal collision avoidance path through a dynamic obstacle avoidance algorithm and a path planning algorithm; the synchronous deviation rectification module realizes accurate track deviation rectification by utilizing fuzzy control and Bezier curve fitting; the communication coordination module constructs a high-speed wireless communication network and supports distributed control and data transmission. According to the invention, the cooperative operation capability of a plurality of paying-off robots in a complex environment is greatly improved, and efficient and reliable paying-off task execution is realized.

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 discloses a humanoid robot path planning system based on an obstacle avoidance algorithm, and the system comprises the steps: S1, obtaining multi-source environment data, and generating a multi-dimensional dynamic environment representation; s2, realizing real-time avoidance of dynamic obstacles through local path re-planning; s3, a path multi-objective optimization module is communicated with a self-adaptive path adjustment module, and an optimal path with global adaptability is generated through a reinforcement learning mechanism and an iterative optimization process; s4, a multi-modal data fusion module which constructs a unified environment feature model through a feature extraction and fusion algorithm; s5, based on an initial path generated by a path multi-objective optimization module, performing dynamic correction on the path by using a nonlinear programming technology, and updating spatial distribution of path points in real time; and S6, quickly searching the global path space through a topological optimization algorithm, and determining a global optimal path. The method has the advantages of strong real-time performance, high adaptability, strong path optimization capability and accurate environment perception.

The invention discloses an inspection robot obstacle avoidance algorithm based on imitation learning. The algorithm comprises sensor data acquisition, picture depth estimation, obstacle detection, multi-modal data fusion and action sequence generation. In the model generated by the action sequence, task actions are divided by adopting an action partitioning technology; the action blocking technology comprises the steps that the size of an action block is set to be k, every k time steps, an action sequence generation model receives a set of multi-modal data, k actions are generated, and the actions are executed in sequence. According to the invention, through multi-modal input data and a generation model, autonomous inspection and obstacle avoidance of the robot can be completed under the conditions of a complex external environment and a dynamic obstacle.

The invention discloses a troweling robot motion control method and system, and belongs to the technical field of robot control. According to the method, the movement route of the troweling robot is obtained in combination with path planning and an obstacle avoidance algorithm, and continuity and safety of the construction process of the troweling robot are ensured; a real-time control technology is combined with a model prediction controller (MPC) to predict future system behaviors of the troweling robot according to kinetic parameter information and ground environment parameter information and adjust a control instruction according to the future system behaviors, so that fine adjustment of the troweling robot on the action of a troweling mechanism (a troweling device) is realized; meanwhile, by monitoring the contact force between the troweling robot and the ground in real time and applying proper contact force according to different ground characteristics, the troweling effect can be further improved, and meanwhile the damage risk to the concrete surface is reduced.

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.