90 results about "Obstacle avoidance algorithm" patented technology

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 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 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 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 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 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.

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 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 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.

The invention relates to the field of mechanical arm path planning, in particular to a path optimization obstacle avoidance method based on an improved Inform-RRT * mechanical arm motion planner. Firstly, parameter information required by path planning is acquired, then an initial step length is determined according to an environment, a random sampling point is generated, a nearest node is found, and a new node is generated according to the step length; continuously updating path points and father nodes, and optimizing path cost; by using the rewiring process in the RRT * algorithm, the radius search of the fixed step length is changed into the dynamic step length, the path length is shortened, and the algorithm efficiency is improved. In combination with an APF function, an artificial potential field function in the APF function is fused into an Inform-RRT * algorithm, and a good convergence effect of the algorithm is used for making up the defects that the number of nodes is too large and convergence is slow before the traditional Inform-RRT * forms an elliptical convergence domain. And finally, the improved path planning obstacle avoidance algorithm is added into an ompl planning library, and a path planning obstacle avoidance experiment is completed in ROS MoveIt in combination with a UR3 mechanical arm model. The number of sampling points is effectively reduced, and the working efficiency of the mechanical arm is improved.

The invention belongs to the technical field of unmanned aerial vehicle path planning, and particularly relates to a limited space unmanned aerial vehicle autonomous homeward voyage method based on real-time mapping and path evaluation. The method comprises the steps that when the unmanned aerial vehicle flies in a limited space without a GPS, real-time positioning is carried out through a laser radar SLAM technology, a three-dimensional grid map is constructed synchronously, and meanwhile a flight path is recorded in real time in a key point mode. When the electric quantity is insufficient or communication is lost to trigger return flight, the system generates two candidate paths in parallel, one path is a historical path which is backtracked in a reverse order, and the other path is a new path which is searched on a real-time map by using an A * algorithm. Then, the two paths are evaluated through a comprehensive scoring function, and scoring standards comprise path smoothness, path safety and path length; and finally, selecting a path with a relatively high score as an execution path, and performing safe return by combining a local obstacle avoidance algorithm. According to the invention, the limitation of pure path backtracking is overcome, and safer, more efficient and more intelligent autonomous homeward voyage is realized.

The invention belongs to the technical field of unmanned vehicles, and discloses an unmanned vehicle path planning method and system based on big data, and the method comprises the steps: obtaining traffic static data, traffic dynamic data and vehicle state data, and sequentially carrying out the data preprocessing and data fusion operation, and obtaining a vehicle traffic information data set; obtaining a vehicle path target and user preference, and performing path decision by using a pre-configured path decision model in combination with the vehicle traffic information data set to obtain a global initial path; and continuously acquiring real-time data of the vehicle in the process of executing the global initial path, and performing dynamic path adjustment by using a dynamic obstacle avoidance algorithm and a comprehensive cost function until the vehicle arrives at a destination. According to the method, the safety and efficiency of the unmanned vehicle are remarkably improved, the method is closer to the cognition and decision logic of real human driving, and the method has high engineering application value and popularization prospects.

The invention discloses a rapier weft insertion adjusting method and device based on loom opening yarn detection, and belongs to the field of intelligent control of textile machinery. The method comprises the following steps: acquiring image data for representing an opening form of a loom and an optical fiber signal for representing a yarn state in an opening area of the loom; performing anomaly detection on the image data and the optical fiber signal with the same timestamp according to a preset detection algorithm to obtain a detection result; according to the detection result, whether opening abnormity exists in the loom or not is determined, if yes, a rapier obstacle avoidance path is generated according to a preset rapier obstacle avoidance algorithm to adjust the rapier movement track, and if not, normal weaving is conducted according to the preset process. According to the invention, the opening detection precision of fabrics with complex structures can be improved, and the weaving efficiency is improved.

The application discloses a kind of multi-unmanned plane local dynamic obstacle avoidance method, device and storage medium, wherein method includes: based on Markov decision process, formalization modeling is carried out to distributed multi-unmanned plane dynamic obstacle avoidance problem;Obtain point cloud data by laser radar carried on each unmanned plane, point cloud data is processed, and obtain obstacle information;Deep neural network is constructed, to complete the mapping of observation input to action output and the method of network updating;Wherein observation input is obtained obstacle information;Based on ORCA algorithm, deep neural network is trained, and the model after training is used for multi-unmanned plane local dynamic obstacle avoidance.The application uses dynamic obstacle avoidance algorithm based on geometry method and deep reinforcement learning method as the obstacle avoidance method of multi-unmanned plane system, effectively solves multi-unmanned plane local path planning and obstacle avoidance problem.The application can be widely applied in multi-unmanned plane local dynamic obstacle avoidance and path planning field.

A mechanical arm ground small object grabbing method and system with obstacle avoidance capability, the method comprising: S1: arranging sensors, collecting point clouds for environment modeling; S2: carrying out noise filtering on the point cloud; S3: generating a hypothetical volume of the obstacle blocking part on the point cloud; S4: filtering the obstacles around the target on the point cloud; S5: force feedback control of mechanical arm obstacle avoidance; S6: object detection and end posture calculation; S7: planning path to execute grabbing. The present application is used when the mechanical arm grabs small-sized objects on hard ground, combines sensor technology, environment modeling, obstacle avoidance algorithm and grabbing strategy, so that the mechanical arm can autonomously avoid obstacles in a complex environment and accurately grab small objects on the ground.

This invention discloses a control system for an intelligent inspection robot for stormwater and sewage pipelines, comprising a microcontroller unit, a multi-sensor module, an image acquisition module, a communication module, and a power module. Through multi-sensor collaborative perception and intelligent decision-making mechanisms, it integrates multi-dimensional data such as images, distance, and environmental data, and employs adaptive path planning and obstacle avoidance algorithms, making it suitable for autonomous inspection tasks in complex pipeline environments. The image acquisition module integrates a high-definition camera and a WiFi module; the distance detection unit combines ultrasonic and infrared ranging sensors to achieve complementary ranging and improve detection accuracy; the environmental monitoring unit uses gas sensors to detect the concentration of toxic and flammable gases such as ammonia and sulfides in the pipeline in real time, and automatically triggers an early warning when the concentration exceeds the standard. This effectively solves the problems of existing pipeline robots, such as single sensor detection methods, poor environmental adaptability, and insufficient intelligence, improving inspection efficiency and system reliability, and reducing manual maintenance costs.

The invention discloses a pure visual navigation method for a legged robot fusing image guidance and image understanding. An image guiding part of the method adopts a target navigation planning algorithm based on a Transform architecture, outputs a relative navigation target point with a target image as guidance, and guides a robot to complete key path selection; in the image understanding part, a lightweight semantic segmentation and obstacle avoidance algorithm based on an improved Yov5 model is adopted, a passable area is obtained based on a semantic segmentation result and a preset passable semantic table, coordinates of obstacle avoidance target points are distributed and output at the position of the passable area in a visual angle, and a robot completes path following and obstacle avoidance tasks by means of semantic understanding. According to the method disclosed by the invention, the robot can perform a navigation task in a complex environment including an outdoor environment by only depending on a small number of target images, so that the generalization problem of an original pure end-to-end vision algorithm is effectively solved, and a long-distance navigation task in a complex scene is realized.

The application discloses a robot obstacle avoidance method based on model-based and model-free reinforcement learning, belongs to the field of robot navigation, and adds model-based reinforcement learning as a forward-looking module into the framework of model-free reinforcement learning, uses the model-based reinforcement learning to improve the sample data utilization efficiency of the model-free reinforcement learning, and avoids the robot from selecting actions that may cause the robot to be in a dangerous situation by deducing the interaction between the robot and the surrounding environment in a future period of time, uses the model-free reinforcement learning as a main decision algorithm, and reduces the influence caused by an inaccurate environment model; the obstacle avoidance strategy is trained in a two-dimensional simulation environment, the perception module is designed and built offline on a data set, the perception module with the output information in the form of pseudo laser radar data is obtained, and then the obstacle avoidance strategy network obtained in the two-dimensional simulation environment can be combined to realize obstacle avoidance in a real environment. The application improves the performance of the obstacle avoidance algorithm.

The invention belongs to the technical field of aircraft control, and particularly relates to a simulation test system and method for an aircraft obstacle avoidance algorithm. The invention discloses a simulation test system and method for an aircraft obstacle avoidance algorithm. The system comprises a virtual environment modeling device, a dynamics resolving device, an obstacle avoidance logic processing device, a physiological feature modeling device, a stress parameter mapping device, a comprehensive evaluation feedback device and a data monitoring device. According to the method, kinematic parameters are mapped into physiological stress parameters such as gravity overload and motion sickness, and an obstacle avoidance track is fed back and corrected according to a physiological comfort threshold, so that an obstacle avoidance path conforms to the tolerance limit of a human body on the premise of ensuring safety. According to the method, deep fusion of control engineering and aeronautical medicine is realized, obstacle avoidance assessment extends to the field of physiological tolerance from physical safety, the scientificity of simulation assessment of the manned aircraft is improved, and the risk of personnel disability caused by violent maneuver in real flight is reduced.

The invention relates to the technical field of unmanned walking devices, in particular to a deviation correction and obstacle avoidance control method for a large-scale multi-crawler walking device, and the method comprises the steps: carrying out the real-time detection of an obstacle and the continuous advancing judgment through an intelligent sensing system based on the three-dimensional laser radar point cloud data of an intelligent holder; when the track offset is larger than a set threshold value, multi-crawler walking device kinematics model analysis and multi-crawler walking device deviation correction judgment are carried out through an automatic deviation correction system, and a rapid adjustment strategy and a precise deviation correction control strategy are formulated; and when an obstacle which cannot pass or affects the passing stability exists in the front, a new advancing path is planned through the automatic obstacle avoidance system. According to the invention, remote obstacle intelligent identification and high-precision positioning and orientation of the multi-crawler walking device can be realized, a safe path for avoiding obstacles is planned by applying an automatic obstacle avoidance algorithm, and the multi-crawler walking device can make an obstacle avoidance decision autonomously, so that safe and stable driving of the multi-crawler walking device is ensured.

The invention provides a method for evaluating the safe obstacle avoidance capability of an unmanned aerial vehicle, and the method comprises the steps: constructing an unmanned aerial vehicle obstacle avoidance track simulation test system, recording a real unmanned aerial vehicle motion track and a virtual expected track through the obstacle avoidance track simulation test system, and evaluating the safe obstacle avoidance capability of the unmanned aerial vehicle through comparative analysis. The system can reproduce aerodynamic force and inertia force feelings in a real obstacle avoidance scene indoors in an indoor controlled and repeatable environment, synchronously provides a testing method of a deceptive positioning signal, safely and accurately simulates the whole obstacle avoidance process when an unmanned aerial vehicle encounters an obstacle outdoors, quantitatively evaluates the track quality of the unmanned aerial vehicle, and improves the obstacle avoidance accuracy. Therefore, data support is provided for optimization and performance authentication of an obstacle avoidance algorithm.

The application relates to a kind of neural puncture dynamic obstacle avoidance navigation method and system based on multi-modal image fusion, belong to medical image navigation and surgical operation technical field, by fusing preoperative high-resolution static image and intraoperative real-time dynamic ultrasound data, construct unified space-time reference model, and utilize artificial intelligence technology to automatically segment and reconstruct the three-dimensional model of key neural vascular structure, can be based on real-time image data dynamic calculation optimal puncture path and real-time monitoring collision risk in the process of needle insertion, provide multi-sensory fusion intuitive navigation guide for operator through augmented reality technology, through recording and analyzing the whole process data of operation, utilize big data platform to continuously iterate optimization to segmentation and obstacle avoidance algorithm, form self-improving intelligent closed loop.The application effectively solves the problems that traditional neural puncture surgery depends on the experience of operator, cannot real-time dynamic obstacle avoidance and system lacks learning ability, significantly improves the precision, safety and intelligent level of operation.

The invention discloses an advanced geological exploration method of an airborne advanced drilling machine, and relates to the technical field of geological exploration. The method comprises the following steps of constructing a multi-dimensional while-drilling data acquisition system, performing data preprocessing, constructing a cross-dimensional fusion transmission network, constructing a multi-source data training set, training a lithology-strength intelligent analysis model, performing data iterative optimization, performing dynamic path planning and performing result visual output. According to the method, a multi-dimensional while-drilling data acquisition system is constructed, a cross-modal feature fusion model and a prediction network based on an attention mechanism are combined, lithology classification is realized, a dual-objective function of geological cost and distance cost is introduced, a DWA local obstacle avoidance algorithm is improved, and a real-time geological prediction result and a historical knowledge base are used for predicting the real-time geological prediction result. The optimal tunneling path is automatically calculated, the path adjustment response time is shortened, and the average tunneling efficiency of the TBM is improved.