2235 results about "Path plan" patented technology

The invention relates to a heterogeneous multi-unmanned aerial vehicle cooperative path planning method based on multi-agent deep reinforcement learning, and solves the problem that a traditional path planning algorithm is difficult to deal with a dynamic planning problem and an agent cooperation problem compared with the prior art. And the existing multi-unmanned aerial vehicle cooperative path planning algorithm has the defects of insufficient feature extraction capability, low experience learning efficiency and poor cooperative strategy flexibility in a dynamic uncertain environment. The method comprises the following steps: analyzing a multi-unmanned aerial vehicle cooperative path planning task; building an airspace reinforcement learning environment; introducing an unmanned aerial vehicle kinetic equation; modeling a multi-unmanned aerial vehicle decision model in multi-unmanned aerial vehicle cooperative path planning as a POMDP model; designing an MASAC-SEPR algorithm model to carry out path planning on multiple unmanned aerial vehicles, and generating a multi-agent collaborative path optimization network model; and training the multi-agent collaborative path optimization network model. According to the method, the efficiency and the accuracy of multi-unmanned-aerial-vehicle cooperative path planning are remarkably improved, and a reliable solution is provided for heterogeneous multi-unmanned-aerial-vehicle cooperative operation in a dynamic uncertain environment.

An autonomous robotic vehicle with dynamic path planning capabilities, including: a dynamic path planning module configured to generate and adapt navigation paths in real time based on environmental changes and mission objectives; a sensor fusion module that integrates data from multiple sensors, including LiDAR, radar, cameras, ultrasonic sensors and GPS, to create a comprehensive environmental model; an obstacle avoidance module capable of detecting and predicting both stationary and moving obstacles and modifying the navigation path to avoid collisions; an artificial intelligence-based decision-making module for analyzing environmental data and learning from past navigation scenarios for improved performance; a decentralized control module that enables the independent operation of the vehicle in areas with interrupted or no connection to a central server; an energy management module configured to optimize battery usage by dynamically adjusting operating parameters based on real-time data and mission requirements; a cooperation module that enables communication and coordination between multiple autonomous robot vehicles for fleet management or the synchronized execution of tasks; a fail-safe module to ensure uninterrupted operation and data security in the event of communication failures or hardware malfunctions.

The invention relates to the technical field of AGV path planning, in particular to an intelligent obstacle avoidance and path optimization method and system for an AGV. The method comprises the following steps: obtaining an omnibearing image of an AGV activity area; performing static obstacle recognition on the omnibearing image to construct each static obstacle model; performing scene three-dimensional structure perception on the omni-directional image, and performing three-dimensional mapping based on each static obstacle model so as to obtain a global perception scene model; obtaining a cargo transportation planning log; performing cargo global available path planning on the global perception scene model according to a cargo transportation planning log, and constructing a global available planning path; and carrying out dynamic path optimization based on the global available planning path, and carrying out dynamic obstacle potential collision point mining according to the dynamically optimized transportation path so as to generate a collision time point and a collision position point. According to the invention, through dynamic and efficient path obstacle avoidance planning, the accuracy and stability of AGV driving are improved.

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

The invention relates to an automatic driving vehicle path planning method and device based on a large language model, equipment and a medium. The method comprises the steps that a traffic scene RGB image, a traffic scene depth image, laser radar point cloud data and inertial measurement unit data of an automatic driving vehicle are acquired; performing feature extraction on the traffic scene RGB image, the traffic scene depth image, the laser radar point cloud data and the inertial measurement unit data to construct multi-modal traffic scene information; calling a preset large language model to determine the motion state of the autonomous vehicle and natural language description of traffic targets around the autonomous vehicle according to the multi-mode traffic scene information; and determining the attention weight of the target node by adopting an attention mechanism in a large language model according to the natural language description of the traffic target, and determining the optimal driving path by adopting a preset path planning algorithm according to the attention weight of the target node, the motion state of the autonomous vehicle and the target position. According to the invention, the adaptive capability of path planning is obviously improved.

The invention discloses a reinforcement learning unmanned forklift truck obstacle avoidance scheduling method and system for a dynamic obstacle, relates to the technical field of unmanned driving, and discloses the reinforcement learning unmanned forklift truck obstacle avoidance scheduling method for the dynamic obstacle. According to the method, the system and the system, the system and the system, global path planning and local obstacle avoidance decision making are carried out in combination with the reinforcement learning network, the problems of obstacle avoidance response delay and unreasonable path planning in a dynamic environment are solved, the obstacle avoidance response speed, the path planning rationality and the multi-modal data fusion precision in the dynamic environment are improved, and then the safety and efficiency of unmanned forklift dispatching are improved.

The invention discloses a multi-robot collaborative scheduling system in an automatic warehousing system, which relates to the technical field of robot collaborative scheduling and comprises a task management module, a path planning module, a communication collaborative module, an exception handling module, a warehousing space dynamic partition module and a task fusion scheduling module. The task management module comprises a task priority calculation unit, a task distribution unit and a dynamic energy consumption evaluation unit, the path planning module comprises a global path optimization unit, a local path adjustment unit and an obstacle avoidance path optimization unit, and by arranging the task management module, the dynamic task priority calculation and distribution function is achieved, and the dynamic energy consumption is evaluated. The problem of adaptation of dynamic task requirements and complex environments is solved, and the completion speed of task allocation and path planning is increased; by arranging the path planning module, the function of dynamically optimizing the path according to the real-time environment is realized, the problem of path conflict optimization in multi-robot scheduling is solved, and the path obstacle avoidance and execution efficiency is ensured.

The invention discloses a land surveying and mapping path planning method based on an unmanned aerial vehicle technology, and the method comprises the steps: enabling an unmanned aerial vehicle to achieve the precise perception and recognition of static and dynamic obstacles in a dynamic environment through a self-adaptive multi-mode perception fusion algorithm; on the basis of multi-modal state estimation and the game theory, modeling and predicting behaviors and future trajectories of the dynamic obstacles; an obstacle avoidance path is generated and optimized in combination with an adaptive fast random tree and deep reinforcement learning; the unmanned aerial vehicle tracks a planned path and deals with dynamic environment changes in real time; the multiple unmanned aerial vehicles realize cluster cooperation through a group self-organizing flight optimization algorithm, and dynamically adjust task allocation and flight strategies; and through incremental environmental perception updating and feedback-based path planning optimization, the perception and decision model is automatically updated after each task is executed. According to the method, the defects of a traditional path planning algorithm in a complex dynamic environment are overcome, and the real-time performance and adaptability of the unmanned aerial vehicle for executing the land surveying and mapping task are greatly improved.

The embodiment of the invention provides a welding path planning method and device, and the method comprises the steps: carrying out the three-dimensional scanning of a to-be-processed object through a machine vision algorithm, obtaining three-dimensional point cloud data, carrying out the data analysis of the three-dimensional point cloud data through a segmentation algorithm based on deep learning, and carrying out the recognition to obtain geometric features; the geometric features are constructed into an undirected graph, an optimal communication path of a weld joint center line is searched on the graph through a minimum spanning tree algorithm, and a fine three-dimensional model of the machined object is constructed through the optimal communication path; according to the three-dimensional coordinates of the welding seam locating point, the current welding gun posture and the target welding seam length, the optimal locating height, locating depth, inclination angle and offset of a welding gun are determined, and a corresponding welding path plan is generated through a welding process simulation algorithm based on the locating height, the locating depth, the inclination angle and the offset; the precision and efficiency of welding path planning can be effectively improved.

The invention discloses an unmanned aerial vehicle path planning method and system based on digital twinning. The method comprises the following steps: constructing a three-dimensional environment model based on first target environment data in first target multi-source environment perception data; constructing a digital twin based on the geometric model, the physical model, the control model, the target physical data hybrid dynamic model and the three-dimensional environment model; inputting the second target multi-source environment sensing data into the digital twin, and dynamically simulating the flight behavior of the physical unmanned aerial vehicle based on the second target multi-source environment sensing data to obtain simulated flight environment state data; and based on the simulated flight environment state data, performing path planning on the physical unmanned aerial vehicle in combination with a prediction control algorithm and a path planning algorithm to obtain a target flight path, transmitting the target flight path to the physical unmanned aerial vehicle, and controlling the flight of the physical unmanned aerial vehicle. The method can dynamically simulate the real-time change of the physical unmanned aerial vehicle, plan the optimal flight path and effectively avoid obstacles, and can be widely applied to the technical field of unmanned aerial vehicle path control.

The invention relates to the technical field of equipment control, and provides a vision-based collaborative robot end control method and related equipment thereof. Multi-modal dynamic anti-interference filtering and semantic fusion are carried out on original visual data to obtain three-dimensional semantic point cloud data, and coordinate system alignment is carried out on the three-dimensional semantic point cloud data according to a robot-based coordinate system to obtain a target pose parameter set; performing dynamic path planning on the target pose parameter set to obtain a joint space motion track, and performing inverse kinematics solution and motor instruction synthesis on the joint space motion track according to robot D-H model parameters to obtain a motor control instruction, and performing closed-loop feedback optimization on the motor control instruction according to the state data of the end effector to obtain a dynamic correction control instruction. According to the method, through full-link optimization from sensing, planning to execution, the operation precision and safety of the collaborative robot in an uncertain environment are remarkably improved.

The invention discloses a path planning heuristic function generation platform and method based on collaborative optimization of a large-scale language model and evolutionary computation. According to the technology, the large-scale language model (LLM) and evolutionary computation (EC) work cooperatively. The platform generates or mutates a heuristic function expressed as an executable code through LLM based on a structured prompt containing an environment context and performance feedback; and an EC framework (such as genetic programming) is combined with performance evaluation feedback to perform selection and iterative optimization on a heuristic code population, and population diversity is maintained. The method aims at overcoming the limitation that a traditional heuristic design is difficult and poor in adaptability, a high-quality heuristic function adapting to a complex and dynamic environment is automatically generated, and therefore the efficiency of a path planning algorithm and path quality are remarkably improved.

The invention relates to a path planning method for a mobile charging robot in an unstructured environment, which combines global path planning and local dynamic obstacle avoidance, performs global path planning through an improved A * algorithm, and performs real-time local obstacle avoidance by using a dynamic window method. The global path and the local path are fused through dynamic weight adjustment, and it is ensured that the robot keeps the continuity of the global path while avoiding obstacles. In the path planning process, factors such as terrain complexity and robot advancing speed are considered, and the energy consumption of the path is minimized by adopting an energy consumption optimization algorithm, so that the working time of the robot is prolonged, and the execution efficiency is improved. The path planning method is suitable for storage, industry, field inspection and other complex dynamic environments, has high adaptability, real-time performance and robustness, and provides an effective solution for efficient application of the mobile charging robot in an unstructured environment.

The invention relates to the technical field of robot autonomous navigation and control, and discloses a robot system based on a sensing system, which comprises a multi-mode sensing module for outputting data to a dynamic environment modeling module; the dynamic environment modeling module is used for transmitting the model to the path planning module; the path planning module outputs the path sequence to the neural motion control module; the neural motion control module outputs the control signal to the dynamic execution module; the dynamic execution module is used for receiving the pulse control signal and driving each joint of the robot to execute the path in combination with inverse kinematics solution and a feedback control mechanism; and the collaborative optimization module is in two-way communication with the modeling module, the path planning module and the neural motion control module. According to the method, the contradiction between real-time performance and integrity of environment modeling in a complex scene is solved by fusing cross-modal information complementary characteristics of binocular vision, millimeter-wave radar and inertial data and combining mathematical representation of a hypergraph topology model on a dynamic obstacle interaction relationship.

Provided are a method, system and device for global path planning for an unmanned vehicle in an off-road environment. The method includes: obtaining satellite elevation data and a satellite remote sensing image of a current off-road environment; constructing a digital elevation model (DEM); determining slope and land surface relief of each grid in the current off-road environment; performing gray processing on the satellite remote sensing image to obtain grayscale values of the grids; determining traversal costs of the grids corresponding to different ground types; constructing a global grid map based on the slope and the land surface relief of each grid, as well as the traversal costs corresponding to the different ground types; determining a rugged terrain potential field and path costs; and searching for paths using a Bresenham's line algorithm and Theta* algorithm based on the rugged terrain potential field and the path costs, to generate a global path.

The invention provides a multi-AGV distributed path planning method based on graph reinforcement learning, which is used for solving the technical problem of low path planning efficiency of a multi-AGV system in an information limited dynamic environment. The method comprises the following steps: establishing a dynamic heterogeneous graph model in multi-AGV collaborative path planning; constructing a distributed partially observable Markov decision model based on a graph structure in combination with path planning and collaborative decision; on the basis of a multi-agent deep reinforcement learning framework of a graph neural network and a multi-head attention mechanism, a multi-AGV cooperative path planning algorithm for coping with an information-limited dynamic environment is designed, information aggregation is realized on the basis of local perception and neighborhood interaction by adjusting weights of nodes and edges in a dynamic heterogeneous graph, and a multi-AGV cooperative path planning algorithm for coping with an information-limited dynamic environment is realized. And the environment change is updated and adapted in real time. According to the method, the path planning efficiency can be improved through an information aggregation mechanism in an information-limited dynamic environment, multi-AGV cooperative path planning is optimized through the graph neural network, and efficient and stable operation of the system is ensured.

The invention relates to the technical field of storage equipment control, and discloses a dynamic cooperative control method and system for digital twin-driven storage equipment. The method comprises the following steps: firstly, constructing a storage equipment digital twin model which covers a storage space three-dimensional model, equipment motion parameters and a real-time operation state; and then collecting sensor data in real time, and generating a collaborative operation path through a dynamic path planning algorithm containing conflict detection and priority allocation. And correcting the equipment motion track and updating the model state by using the space-time consistency verification model, finally inputting the corrected track into an equipment control instruction generation module, and outputting a driving signal to control the storage equipment. The system comprises a digital twinborn model construction module, a sensor data acquisition module, a collaborative operation path generation module, a motion trail correction module, a control instruction generation module, a driving module and the like. The storage space utilization efficiency and the equipment collaborative operation capability are improved, real-time monitoring and fault prevention are achieved, and intelligent upgrading of a storage system is promoted.

The invention relates to the technical field of artificial intelligence, can be applied to the field of medical health, and discloses a navigation method, device and equipment in an unexplored environment and a medium. Analyzing a pre-trained large language model into a structured semantic representation at least comprising target entity information, spatial constraint information and contextual intention information; inputting the target entity into a pre-trained large language model to generate a standardized task description question, inputting the standardized task description question and real-time environment image data into a pre-trained visual language question and answer model, and outputting a semantic answer result about whether the target entity exists or not; and if yes, generating a navigation strategy through a pre-trained large language model based on the structured semantic representation, and controlling the robot to execute a navigation action through a preset path planning algorithm. The method does not need to depend on an environmental map and scene training, realizes navigation in an unexplored environment by utilizing cooperation of a large language model and a visual language question and answer model, solves the problem of strong dependency of a traditional scheme on prior data, and has good generalization ability and environmental adaptability.

The invention relates to a welding seam path planning method and device, computer equipment and a readable storage medium. The method comprises the steps that workpiece surface point clouds of a to-be-welded workpiece are obtained, the workpiece surface point clouds are clustered, a point cloud clustering cluster is obtained, weld joint end points in the point cloud clustering cluster are detected, the number of the detected weld joint end points is determined, a weld joint exists between the weld joint end points, a first weld joint type of the weld joint is detected according to the number of the end points, and a second weld joint type of the weld joint is determined according to the first weld joint type. And detecting a second welding seam type of the welding seam based on the distance between the target vector between the welding seam end points and the mass center of the preset area where the welding seam is located, and performing welding path planning on the welding seam based on the first welding seam type and the second welding seam type. By adopting the method, the welding path can be accurately planned.

The invention provides an orchard agricultural robot navigation method, and belongs to the technical field of agricultural robots. The method comprises the following steps: constructing a priori map of an orchard; carrying out odometer initialization and rear end correction on the robot; carrying out ground segmentation and obstacle identification; constructing a cost map: constructing the cost map through static obstacles of a prior map and dynamic obstacles after ground segmentation, and realizing visualization of the obstacles; and path planning: assigning an initial point and an end point on the cost map, firstly realizing global path planning, bypassing an obstacle by using local path planning to realize an obstacle avoidance function when a dynamic obstacle appears in the advancing process, and performing real-time trajectory optimization and smooth control. According to the method, accurate positioning and autonomous navigation of the robot in a complex orchard environment can be realized; meanwhile, through the combination of global path planning and local path planning, the robot can flexibly process static and dynamic obstacles, and the robustness of path planning and the effectiveness of a navigation task are ensured.

The invention belongs to the technical field of path planning of unmanned equipment, and relates to a medical rescue unmanned aerial vehicle and robot dog cooperative linkage method, which comprises the following steps: integrating LiDAR point cloud, visual images, IMU inertial data and compensated UWB positioning data into a multi-modal data stream subjected to space-time alignment through time alignment and space alignment technologies; respectively extracting LiDAR and visual feature poses through parallel SLAM (Simultaneous Localization and Mapping) calculation, and carrying out graph optimization and joint optimization by utilizing a GTSAM library, so as to construct a globally optimized pose and a global semantic map; the path planning algorithm provides dynamic channel routing for the unmanned aerial vehicle and the robot dog based on a global semantic map, the feature fusion weight is adjusted by considering the environment illumination intensity and the point cloud density, the reverse iris control mechanism detects whether the robot dog enters a shielding area through a UWB signal intensity attenuation value, the unmanned aerial vehicle is switched to a UWB signal follower, and the UWB signal follower is switched to the unmanned aerial vehicle. And through combination with a Kalman filter, penetration positioning in a shielding area is realized, so that stable execution of a rescue task in a complex environment is ensured.

The invention discloses an unmanned aerial vehicle route planning method based on an improved grey wolf optimization algorithm. The method comprises the following steps: 1, environment modeling and parameter setting; 2, designing constraint conditions and a target function; 3, initializing an algorithm and generating a population; 4, improving an algorithm and updating a track; and step 5, iteration termination and result output. The flight path of the unmanned aerial vehicle is efficiently coded in a spherical vector coding mode, and the flexibility and the calculation efficiency of flight path representation are remarkably improved; by introducing the Levy flight strategy, the global search capability of the algorithm is enhanced, and falling into a local optimal solution is avoided; meanwhile, a parameter adaptive adjustment strategy is combined, so that the convergence speed and precision of the algorithm are remarkably improved. According to the method, the problem of flight path planning in a complex mountain environment and a threat area is effectively solved, a safer and more efficient flight path is planned for the unmanned aerial vehicle, and the method has higher practicability and reliability.

The invention discloses an intelligent self-adaptive industrial robot control system and method, and the method comprises the steps: carrying out the automatic recognition and self-inspection of a sensor, an actuator and a controller module through a standardized interface when the system is started; various sensors are used for environment monitoring, acquired data are stored in a cloud end, and a sensor fusion technology is applied for integration; the system state and the environment condition are monitored in real time, machine learning and an artificial intelligence algorithm are used for analyzing the operation state and the task effect, and a corresponding operation strategy is formulated; based on task execution and environment information, an execution path is generated by adopting a path planning algorithm, and an operation strategy and parameters are dynamically adjusted according to real-time feedback; a real-time safety monitoring mechanism is introduced, and a safety threshold value is set; sensor data are analyzed regularly, potential faults are identified, and fault prediction is carried out; according to the intelligent self-adaptive industrial robot control system and method, when the robot is designed, the safety of man-machine cooperation is considered, and safety protection measures are added.

The invention discloses a collaborative operation system and method for a photovoltaic cleaning robot and a carrying robot, relates to the technical field of automatic control, and solves the defects that an existing collaborative operation system is unstable in communication and insufficient in emergency handling capacity. According to the invention, the environmental information of the photovoltaic power station is collected in real time through the environmental perception module, and the reliability of data transmission is ensured through an automatic retransmission request mechanism and error detection and correction codes based on 5G and LoRa communication protocols; communication parameters are adaptively adjusted through the communication self-adjusting module, so that the communication quality is ensured; the path planning module dynamically allocates cleaning tasks and plans an optimal path, the collaborative obstacle avoidance module dynamically adjusts the motion trails of the robots and avoids obstacles, the operation progress tracking module records the operation states of the robots in real time, and comprehensive operation progress information is provided through data processing and visual display. According to the invention, the communication stability, the autonomous decision-making capability and the operation efficiency of the system are obviously improved.

The invention relates to the field of robots, and discloses a multi-robot scheduling method, device and equipment in warehouse management and a storage medium, and the method is used for realizing efficient conflict-free scheduling of multiple robots in warehouse management through fusion algorithm path planning and conflict prediction and processing. The method comprises the following steps: receiving an operation order, and sorting operation tasks in the operation order according to a priority and an overall path optimal principle to obtain an operation task sequence; assigning the operation tasks in the operation task sequence to the to-be-assigned robot according to the sequence, and generating a plurality of robot operation pairs; and a fusion algorithm formed by an improved artificial potential field method and a space-time A * algorithm of a model prediction mechanism is adopted to carry out path planning on the selected robot in each robot operation pair, a CBS algorithm is adopted to carry out conflict prediction, and when conflicts exist, robot operation paths are updated, and the selected robots are scheduled.

The invention relates to the field of intelligent mobile equipment control, and discloses a self-adaptive obstacle avoidance control system and an application method thereof, and the system comprises a data collection module, an environment modeling module, an obstacle segmentation module, a path planning module and a motion control module. The data acquisition module is used for acquiring multi-modal data of the surrounding environment of the vehicle; the environment modeling module fuses the multi-modal data, and generates a dynamic environment map including the spatial position, the dynamic characteristic and the free passing area of the obstacle; the obstacle segmentation module is used for segmenting the dynamic environment map and identifying an obstacle area and a free passing area; the path planning module calculates an optimal path with the minimum cost; the motion control module adjusts the motion state of the vehicle in real time through a PID control algorithm or a model prediction control method, and path tracking and dynamic obstacle avoidance are achieved. The method has high-precision environment perception capability, flexible path planning capability and efficient dynamic obstacle avoidance control capability, and can be widely applied to the fields of industrial automation and intelligent logistics.

The invention discloses a transformer substation inspection unmanned aerial vehicle path planning method based on a fusion A *-grey wolf algorithm, and aims to solve the problem of unmanned aerial vehicle path planning in a transformer substation inspection scene, and the method fully considers coupling of task point traversal and obstacle avoidance, thereby ensuring that an inspection path allocated for unmanned aerial vehicle inspection accords with a real transformer substation scene. A transformer substation virtual environment model is established through a grid map of an A * algorithm, and path implementation of the algorithm is simplified; a population is initialized by improving Tent chaotic mapping, a grey wolf algorithm is improved by introducing an adaptive position updating strategy and an elite grey wolf optimization strategy, and an initial traversal path is generated; and refining through an A * algorithm dynamically weighted by an evaluation function to obtain a final path. Simulation results prove that the feasibility and effectiveness of the improved fusion algorithm in obstacle avoidance and path planning in a substation simulation scene, and the improved algorithm is more suitable for path planning in a complex environment with multiple task targets and multiple obstacles.

The invention discloses a multi-agent collaborative scheduling method and system based on maximum entropy reinforcement learning, and the method comprises the steps: generating an environment state vector according to the first environment data of a production line; generating an initial production task according to the environment state vector; performing task scheduling operation on the initial production task, and generating a path plan; storing the environment state vector, the initial production task and the path planning to obtain empirical data; according to the empirical data, training an initial strategy model through a maximum entropy reinforcement learning algorithm to obtain a target strategy model; and obtaining a multi-agent collaborative target scheduling strategy according to the target strategy model. The multi-agent cooperation efficiency can be improved, so that the production efficiency is improved, and the method can be widely applied to the technical field of agent scheduling.

The invention discloses a path planning and hierarchical cooperative control method and system for an unmanned aerial vehicle cluster. The system comprises a path planning module and a formation motion control module. The method comprises the steps that firstly, an improved RRT * algorithm is adopted by a path planning module, through a multi-strategy heuristic node expansion mechanism fusing target bias and artificial potential field guidance and comprehensively considering path length, channel volume and Z-axis height change, a center path and a three-dimensional safe channel which take into account safety and smoothness are planned for an unmanned aerial vehicle cluster; and then, based on the central path, the formation motion control module adopts a distributed model prediction control framework, designs different optimization targets for a navigator and a follower, and solves an optimal control instruction on line, so that a cluster is guided to complete trajectory tracking, collision avoidance among individuals and self-adaptive formation reconstruction in a secure channel. According to the invention, the navigation problem of the unmanned aerial vehicle cluster in a complex obstacle environment is solved, and the path planning efficiency and the robustness of cooperative control are improved.