154 results about "Global planning" patented technology

The present disclosure belongs to a field of UAVs exploration technology, discloses a high-efficient autonomous exploration method, system, and terminal for UAVs, comprising: S1, heuristic waypoint generation: setting an exploration scope and waypoint spacing, and generating waypoints through waypoint generation algorithms; S2, global path planning: after generating heuristic waypoints, an A * algorithm is used to generate the global planning path; S3, real-time positioning and mapping: using point clouds for real-time positioning and mapping; S4, local B-spline trajectory generation: using B-spline parameterization method to generate local trajectories; S5, real-time obstacle avoidance and dynamic feasibility constraints: optimizing the trajectories to achieve fast convergence, generating smooth, collision-free, and dynamically feasible trajectories; S6, local real-time replanning: using a time sliding window for local replanning; S7, flight control: Using UAV control algorithms for controlling of UAVs robustly.

The invention discloses a hierarchical multi-agent robot cluster control method and system, and aims to solve the problems of partial observability and environment non-stability of a multi-agent system in a complex environment. According to the method, a three-layer layered reinforcement learning architecture is constructed, a high-layer strategy is responsible for global task decomposition and role allocation, a middle-layer strategy converts tactical intention into a cooperative behavior mode, and a low-layer strategy executes accurate motion control; a graph neural network is adopted for cluster modeling, global graph representation and local neighborhood features are extracted in parallel through graph convolution and an attention mechanism, and hierarchical decision making is supported; a centralized graph enhancement evaluation network is designed to be combined with an MAPPO algorithm for collaborative optimization, and dynamic adversarial training is introduced to improve strategy robustness. According to the method, effective decoupling of global planning and local control is realized, and the cluster cooperation efficiency, the strategy interpretability and the adaptive capacity in a dynamic environment are improved.

The invention discloses a robot dog inspection path intelligent planning and dynamic adjustment method, system and device and a medium, and belongs to the technical field of robot path planning, and the method comprises the steps: calculating a task emergency degree index according to a weight coefficient of an inspection point and a time constraint, and determining a task priority; constructing a hierarchical electronic map containing the terrain difficulty coefficient and the moving cost; performing global path planning through a comprehensive cost function by adopting an A star algorithm; environment information is collected in real time through multiple sensors, and the dynamic obstacle layer is updated; performing real-time trajectory optimization by adopting a local path planning mode; selecting local path correction or global path re-planning according to the path execution state score; energy needed for completing the remaining tasks is predicted, and a charging path is planned when necessary. According to the invention, multi-target adaptive optimization of path planning is realized, a coordination mechanism of global planning and local adjustment is established, and the execution efficiency and reliability of inspection tasks are improved.

The invention discloses an automatic guided vehicle path optimization method and system based on fusion planning, and relates to the field of robot technology, computer science and automation control, and the method comprises the steps: carrying out the global path optimization based on an improved A star algorithm according to a two-dimensional grid map, and obtaining a global optimal path and a key path point set; performing local path planning based on an improved dynamic window algorithm according to the global optimal path and the key path point set, and obtaining a local obstacle avoidance track and an optimal speed control instruction; and based on the optimal speed control instruction and in combination with the local obstacle avoidance trajectory, obtaining an automatic guided vehicle path optimization result. According to the method, on the global planning level, the path length and the number of redundant nodes are remarkably reduced, the steering frequency is effectively reduced, and the path smoothness and the performability are improved; in a local planning level, efficient obstacle avoidance and real-time trajectory optimization in a dynamic obstacle environment are realized.

The invention provides an intelligent pipeline welding method and system based on data decision, and relates to the technical field of intelligent pipeline welding.The intelligent pipeline welding method comprises the steps that a welding request is responded, a groove image of a to-be-welded pipeline is obtained through an active visual sensor, groove morphology characteristic parameters of the to-be-welded pipeline are determined based on the groove image, and the to-be-welded pipeline is welded according to the groove morphology characteristic parameters. Based on the groove morphology characteristic parameters, corresponding basic layer channel arrangement rules and reference process parameters are determined from a preset layer channel arrangement database, a welding process scheme matched with the current groove is generated in combination with the groove morphology characteristic parameters, and the current groove is welded on the basis of the welding process scheme. And the welding operation is executed in the groove of the to-be-welded pipeline, the technical problems that in the prior art, the global planning capacity is deficient, the deviation correction strategy is locally lagged, and the whole-process accurate control is insufficient are solved, and the effect of improving the stability and reliability of the welding quality of the pipeline is achieved.

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 embodiment of the invention relates to the technical field of intelligent driving, and discloses a motorcade passing control method, device and equipment based on global planning, and the method comprises the steps: obtaining the driving related information of vehicles in a motorcade; the motorcade comprises a plurality of vehicles, and the driving related information comprises at least one of road condition information, vehicle driving information and map navigation information; according to the driving related information of at least one vehicle in the motorcade, performing global planning by taking the vehicles in the motorcade not to fall behind or the fallen vehicles to get up to the motorcade as a control target, and determining a target state of a target vehicle in the motorcade; the target state indicates driving information needing to be executed by a target vehicle, and the target vehicle is any vehicle in a motorcade; and sending the target state of the target vehicle to the target vehicle to control the target vehicle to be switched from the current state to the target state. By applying the technical scheme of the invention, the current problems that the formation vehicles are easy to fall behind and are difficult to recover because of being cut off in the complex urban road can be solved.

The invention provides a three-dimensional data generation method and device, and the method comprises the steps: carrying out the planning of a text prompt before generation, optimizing the three-dimensional data in stages, introducing a reflection and correction mechanism in the generation process, recognizing and correcting defects through a multi-modal large model, and introducing a cross-view alignment constraint. And global planning and local fine adjustment of the three-dimensional generation process are realized. According to the method, a complex generation task is decomposed into a closed-loop process of planning-execution-reflection-correction, and multi-view consistency is forced from a feature level, so that geometric consistency and semantic accuracy of generated three-dimensional data can be effectively improved, the problems of structural artifacts and Janus which are easy to occur in the prior art are avoided, and the generation efficiency of the three-dimensional data is improved. And finally, high-quality three-dimensional data with consistent views are generated.

The invention discloses a robot dynamic path planning method and system fused with lightweight LLM. The robot dynamic path planning method comprises the following steps of dynamically mapping a grid map, constructing a semantic analysis model, performing global path planning by adopting an improved A * algorithm, and performing local path optimization by adopting an improved DWA algorithm. Specifically, according to the method, the high-frequency capture and real-time updating of the dynamic obstacle are realized by introducing the fusion of the event flow of the event camera and the depth information of the laser radar; dynamic matching of semantic instructions and planning parameters is achieved by introducing lightweight LLM, meanwhile, sight distance penetration rate optimization path connectivity is fused in global planning, local obstacle avoidance adopts a semantic adaptive weight adjustment mechanism, the high-precision perception advantage of multi-sensor fusion is guaranteed, task adaptability and scene pertinence are improved, and the method is suitable for large-scale popularization and application. And the complex requirements of logistics scenes are met.

The invention provides a long-range visual question and answer and multi-modal reasoning task agent construction method. For input images and natural language questions, generating answers through orderly calling of an external tool set; the construction process of the intelligent agent is divided into three stages: a global planning stage: analyzing user requirements through a global planning navigator, and screening tool subsets; in the tool autonomous execution stage, visual details are supplemented, background knowledge is retrieved and accurate operation is carried out in the multi-step process by surrounding three types of tools through a tool autonomous executor, and a returned tool result is used for assisting the next step of decision making; in the response synthesis stage, the reasoning process is combed through a model response synthesizer, and user-friendly and clear-format responses are sorted; and finally, the model response synthesis module extracts and obtains answers from the reasoning track and outputs the answers. The problems of global planning insufficiency and visual context forgetting generally existing in complex task reasoning of an existing multi-mode large language model are solved.

The invention discloses a vehicle path planning interaction method based on vehicle-road cloud collaboration, and the main conception is that a communication link is decomposed into multiple paths of signals, the signals are dynamically switched according to real-time signal quality, seamless degradation is realized, and based on a three-stage elastic architecture of cloud global planning, marginal layer region collaboration and vehicle end local execution, the vehicle path planning interaction method based on the marginal layer region collaboration is realized. A communication degradation control and computing power resource dynamic allocation strategy is utilized to avoid contradiction generated by communication delay and computing power allocation; besides, based on a vehicle-road cloud collaborative architecture and in combination with a space-time consistency guarantee scheme, on the premise of ensuring that the vehicle end can autonomously change a lane during communication interruption, the safety problems that the deviation between the vehicle end autonomous decision and a global path is too large and rapid correction is difficult after communication recovery are solved. According to the invention, aiming at the specific working condition that the communication link is unstable and the computing power resource is limited, the cost benefit of the automatic driving algorithm is obviously optimized, and the driving safety is effectively improved.

The invention relates to a robot global path planning and local obstacle avoidance method and device and a mobile robot. The robot global path planning and local obstacle avoidance method comprises the following steps: S1, constructing an immunoaffinity function; s2, establishing a two-dimensional plane environment model; s3, global path optimization based on an individual body type top-level food grazing strategy; s4, carrying out local obstacle avoidance detection; s5, obstacle grid evaluation based on a pseudo-virus diffusion algorithm; s6, building an obstacle protection area and controlling fitting motion; and S7, continuously monitoring the state of the obstacle in the obstacle avoidance process, and controlling the robot to adjust the trajectory to return to the original global planning path when detecting that the obstacle is separated from the obstacle avoidance area. According to the invention, global shortest path planning is realized through local area path optimization, and the traffic efficiency is improved.

The invention relates to a steel production-oriented multi-base multi-production-line multi-process global plan optimization method and system. The method comprises the following steps of: receiving and standardizing order demand data; executing hierarchical processing and distribution of multi-base orders, and outputting a base-level order set; deducting and marking the net production demand quantity of the base-level orders; executing multi-production-line order allocation in a single base, and generating a production line level order set; performing cross-process collaborative batch grouping on the production line level order set based on steel making-continuous casting-rolling-post-processing process constraints to generate a cross-process batch plan; constructing a global evaluation model, performing comprehensive evaluation on an intermediate result, and generating a parameter correction instruction based on an evaluation result for adjusting parameters, constraints or distribution strategies of subsequent modules; and finally, generating a production plan scheme with consistent cross-base, cross-production-line and cross-process, and triggering rolling optimization update when disturbance occurs. According to the invention, unified coordination and global optimization of multi-module plan results are realized.

The invention relates to an AGV adaptive path planning and reciprocal obstacle avoidance method in a complex environment, which comprises a global path planning layer, a local obstacle avoidance planning layer and a path adaptive correction layer, and is characterized in that the global path planning layer generates a global planning path position vector by improving A * and combining a JPS jump point algorithm; the local obstacle avoidance planning layer uses an improved RVO model based on a dynamic threat function to solve a local planning path position vector of the AGV through a minimum cost function; and the path adaptive correction layer ensures the coordination and consistency of global and local strategies, triggers the global path planning layer to regenerate a task global path based on the deviation between the local planning path position vector and the global planning path position vector and the obstacle density, and realizes adaptive adjustment to improve the planning efficiency and the obstacle avoidance performance. Compared with the prior art, the AGV obstacle avoidance method has the advantages of being small in model calculation amount, good in AGV obstacle avoidance real-time performance, capable of effectively dealing with high-speed dynamic obstacles and avoiding multiple AGVs from one another and the like.

The invention discloses a multi-vehicle collaborative navigation and obstacle avoidance method and system, a vehicle and electronic equipment, and relates to the technical field of automatic driving. According to the method, a first target vehicle is controlled to run according to a global planning path determined based on urban road network topology and real-time traffic flow data, and environment data and running state data collected by the first target vehicle and a second target vehicle are acquired, so that the vehicle distance and the vehicle speed of the first target vehicle and the second target vehicle are controlled; the second target vehicles are all vehicles which are dynamically networked with the first target vehicle in the communication coverage range of the first target vehicle, and when the first target vehicle travels to a specified scene, an obstacle avoidance decision is generated and executed based on the environment data, the traveling state data and a traveling demand limited in the global planning path. Therefore, the problems that information interaction based on a single road section is only suitable for synergy of vehicles in the same direction and in the same lane, data transmission content only contains basic parameters such as vehicle speed and position, and complex and changeable traffic environments are difficult to deal with are solved.

The invention discloses an intelligent logistics system based on a tilt-rotor unmanned aerial vehicle, and relates to the technical field of tilt-rotor unmanned aerial vehicle control, the system comprises an unmanned aerial vehicle end, a client and a cloud control server, and the unmanned aerial vehicle end is internally provided with an information acquisition unit and a real-time obstacle avoidance unit; an information receiving and transmitting module, a path generation module and a cluster cooperation module are arranged in the cloud monitoring server, through mutual cooperation of all the modules and units, an intelligent logistics system from the cloud to the airborne and from global planning to local obstacle avoidance is formed, and efficient, safe and stable unmanned aerial vehicle distribution can be achieved in congested cities and remote areas.

The invention discloses an automatic driving method based on an improved Astar algorithm and a hierarchical state machine, and the method consists of three parts: road topology abstract graph construction, a lane adaptive Astar algorithm and the hierarchical state machine, and forms an efficient and cooperative'planning-decision 'integrated frame. Firstly, a road topology abstract graph is constructed. Secondly, path planning is carried out by adopting an improved lane adaptive Astar algorithm, and two stages of search strategies are included: in the first stage, road section-level global planning is carried out on a road topology abstract graph, and a macroscopic feasible path is rapidly determined; in the second stage, lane-level refined path generation is carried out in the selected road section, road curvature, width and static and dynamic obstacle distribution are comprehensively considered, and a smooth and safe accurate driving track meeting vehicle dynamics constraints is generated. Finally, a hierarchical state machine is designed, and the top layer is responsible for managing a driving mode and ensuring the global consistency of decisions; and the bottom layer executes fine speed control and lane changing control according to the top layer instruction.

The invention provides an unmanned aerial vehicle autonomous exploration method based on boundary detection and probability sampling fusion, and the method comprises the steps: firstly generating a high-quality viewpoint through a probability sampling model, and providing precise data support for path planning in an exploration stage; secondly, efficient collaboration of global planning and local planning is achieved by means of a layered planning architecture and a topological path search technology; and finally, through a re-planning mechanism, the robustness and task continuity of the unmanned aerial vehicle in the exploration process in a complex unknown environment are ensured. Aiming at the technical defects that the existing autonomous exploration method is easy to fall into local optimum, low in calculation efficiency, insufficient in track smoothness and the like, the invention provides a solution suitable for an unknown environment, and a space exploration task can be efficiently and quickly completed.

The invention discloses an adaptive multi-loop network InSAR phase unwrapping method and device, and relates to the technical field of interferometric synthetic aperture radar measurement. The method comprises the following steps: acquiring an interferometric phase diagram and a topographic map of a complex topographic region; calculating coherence, a topographic gradient map, a phase gradient standard deviation and residual density based on the interferometric phase map and the topographic map; based on the coherence, the terrain gradient map, the phase gradient standard deviation and the residual density, dynamically selecting the size of a ring through a decision function, and constructing a multi-ring network; determining the connectivity of the multi-ring network according to a bridging strategy and a connectivity detection method, and outputting a global connected network; on the basis of the global connected network, layered residual optimization is carried out by adopting a local absorption and global planning double-layer strategy, and an optimized residual is output; and based on the optimized residual error, unwrapping all rings in the global connected network through an in-ring local unwrapping and global optimization strategy, and outputting an unwrapping result. According to the invention, the problem of network connectivity in a complex area can be solved.

The invention relates to the field of artificial intelligence, and discloses a charging robot intelligent scheduling and control method based on visual perception. According to the method, a double-layer architecture integrating global planning and local decision making is constructed; a central scheduling system generates a global conflict-free space-time path; each robot constructs a local three-dimensional semantic map in real time through a visual sensor, and predicts a dynamic target trajectory by using a long and short term memory network fused with a social force model; and when a collision risk is detected, triggering a decentralized negotiation protocol, autonomously re-planning a local obstacle avoidance path according to the dynamic priority, and synchronizing the state to a central system. The system comprises a global path planning module, a visual perception module, a trajectory prediction module, a risk decision module, a conflict negotiation module, a local re-planning module and a state synchronization module. According to the method, millisecond-level local response and global collaborative optimization are realized, and the safety, the task continuity and the system expandability are remarkably improved.

The invention relates to an off-grid hydrogen production system energy management scheduling method based on VMD-MPC, and relates to the field of new energy power generation energy management. According to the technical scheme, a low-frequency trend component and a high-frequency disturbance component in a power signal are stripped through variational mode decomposition VMD, a layered optimization framework of day-ahead global planning-intraday dynamic correction is constructed, and the pain point that an existing single time scale model lags in response to dynamic change of a system is effectively solved. In addition, according to the scheme, the input signal quality is optimized through VMD preprocessing, and the input stability of the model predictive control MPC scheduling model is remarkably improved. Besides, a deviation dynamic self-adaptive penalty mechanism is introduced into an intra-day rolling optimization link, so that a scheduling strategy can automatically adjust the tracking intensity according to the actual fluctuation amplitude and flexibly adapt to the change of working conditions, and the anti-interference ability of the system to deal with the uncertainty of wind and light output and load is greatly enhanced.

The application relates to a robot cluster control method, which collects local environment data through a robot cluster and sends the data to an edge terminal, while the edge terminal acquires global environment data. Based on deep analysis and processing of the global and local environment data, the edge terminal can accurately identify dynamic targets in the environment and their detailed dynamic information. Based on this information, the edge terminal determines a robot task and selects a target robot most suitable for executing the task from the robot cluster, avoiding blind allocation of the task. Further, the edge terminal generates task execution information for the target robot based on the robot task, so that the target robot can act according to the instruction. The application greatly improves the global planning capability of the multi-robot system, so that the robot cluster can quickly and efficiently respond in a complex environment, realize close collaborative work, and significantly improve the ability and execution efficiency of the multi-robot system in dealing with complex tasks.

The invention discloses a multi-agent priority obstacle avoidance path planning method for a dynamic obstacle, and relates to the technical field of mobile agent path planning, the method comprises the following steps: a global planning stage: optimizing an A * algorithm by introducing a heuristic scaling factor, an obstacle and direction penalty term and a neighborhood adaptive extension mechanism, a path is smoothed by combining broken line reconstruction and a Bezier curve; in the local planning stage, the weight of a DWA evaluation function is dynamically adjusted through fuzzy control, and the real-time obstacle avoidance robustness is improved. Meanwhile, a priority obstacle avoidance strategy and a dynamic look-ahead mechanism are designed, multi-agent conflicts are coordinated, and collective stagnation is avoided. Simulation results show that the method shows better path length, search efficiency and obstacle avoidance performance in static, unknown static and dynamic obstacle environments, effectively improves the multi-agent coordination stability, and is suitable for actual scenes such as warehouse logistics and the like.

The invention relates to a reverse navigation method based on a mixed A * algorithm, and belongs to the technical field of autonomous driving path planning and tracking, and the method comprises the following steps: S1, obtaining a global planning path; s2, constructing a real-time grid map and selecting local target points; s3, calculating a local path based on a mixed A * algorithm: taking the current position of the vehicle as a starting point, taking the local target point as an end point, performing local path planning by adopting the mixed A * algorithm, and generating a local path meeting the kinematics constraint of the vehicle; s4, carrying out discretization processing on a local path; s5, realizing path tracking based on a pure tracking algorithm; and S6, circularly updating. According to the reverse navigation method based on the mixed A * algorithm, guidance is provided through a global return path, a local target point matched with the vehicle body size is dynamically selected in combination with a high-precision real-time grid map behind a large distribution vehicle, the consistency of the original return path is guaranteed, and the road condition change of a narrow road section can be quickly responded.

The invention relates to the technical field of path optimization, in particular to an emergency shelter global planning method based on multi-disaster risk coupling, which comprises the following steps: acquiring disaster source and DEM grid coordinates and calculating distance, selecting grids according to influence radius, comparing and connecting grid elevation identification peak points, and determining a propagation restriction area by combining the disaster source and a water diversion line. And matching the positions of the shelters and sorting with the road network. According to the method, differential disaster diffusion ranges are identified in a unified space framework by introducing spatial positions of various disaster sources and combining a grid distance relation, terrain peak points are identified by combining grid elevation differences, correlation analysis is performed by combining a spatial adjacency relation, and the disaster diffusion ranges are identified according to a spatial cross relation between the disaster source positions and water diversion line nodes. A potential disaster diffusion channel is avoided in the shelter screening process, space matching is carried out by combining the shelter boundary position and the road network, and the shelter space layout stability under the terrain and the multi-disaster scene is enhanced according to the road access direction.

This invention discloses a big data-based method for optimizing urban sanitation operations, relating to the field of intelligent sanitation optimization technology. The method includes collecting urban sanitation data to construct a pollution area model, abstracting the detection process of mobile detection equipment as a Poisson stochastic process, deriving the probability distribution function of pollution area detection, calculating the minimum detection frequency for each area, using a simulated annealing algorithm to calculate the optimal detection frequency for each area, and using an improved NSGA-III multi-objective optimization algorithm for global planning and scheduling of sanitation operation paths. By combining the Poisson stochastic process model and the simulated annealing algorithm, the scientific quantification and optimized allocation of detection frequencies are achieved, achieving both accurate detection and effectively improving the utilization rate of detection equipment. The improved NSGA-III algorithm enhances the completion rate of regional detection while reducing resource consumption from ineffective operations, making sanitation task execution more efficient.

The invention relates to the field of medical transportation equipment, in particular to a control method and system based on a medical transportation robot, and the control of the medical transportation robot is realized by adopting a mode of global path planning in advance and a real-time avoidance strategy. Global path planning comprehensively considers multiple costs of distance, congestion, elevator waiting and the like based on a 3D grid map, and ensures the overall optimality of task execution; and on the basis of real-time dynamic perception of depth vision and radar fusion, various intelligent obstacle avoidance behaviors such as detour, waiting and active avoidance are realized by accurately identifying the target type and the motion vector. The traffic efficiency and the path continuity are balanced, and the method is particularly suitable for complex dynamic scenes with dense people flow and many emergencies in a hospital environment. The layered decision-making mechanism not only avoids the heavy overhead of global planning, but also optimizes the obstacle avoidance path through the local cost model, so that the robot can politely, efficiently and reliably complete the medical transportation task, and the hospital logistics automation level and the emergency response capability are remarkably improved.

The invention discloses a walking control method, device and system for a breeding feeding robot. The walking control method comprises the steps that laser positioning data, UWB positioning data and odometer positioning data of the robot are obtained; positioning the current position of the robot according to the laser positioning data, the UWB positioning data and the odometer positioning data to obtain current position data; and controlling the walking path of the robot according to a pre-planned global planning path and the current position data. According to the method, more accurate current positioning data of the robot is comprehensively determined according to the three groups of positioning data, so that the accuracy and reliability of walking path control of the robot are ensured, and the uniformity and accuracy of feed feeding of the robot are ensured.

The invention provides a dynamic path planning method and device for an unmanned mine car in a mining area and a medium, belongs to the technical field of unmanned mine cars, and realizes layered path planning through deep coupling of mixed A * global planning and artificial potential field local deformation. The method comprises the following steps: firstly, constructing an environment model containing terrain gradient and friction coefficient, and generating a global coarse path meeting mine car dynamics constraints; constructing an artificial potential field environment in the local area, and carrying out constrained displacement adjustment on the path points; it is ensured that the path meets steering and braking requirements through dynamic feasibility verification; and finally, splicing the global and local paths and outputting the paths to a drive-by-wire system. And real-time oscillation-free obstacle avoidance under complex terrains and dynamic obstacles is realized. The generated path fully meets the steering characteristics of the mine car, and driving stability is guaranteed. Through global and local collaborative optimization, a complex working environment of a mining area is adapted; the output path comprises complete pose and curvature information, and a sufficient basis is provided for mine car control.

The invention discloses a multi-robot cooperative pecan harvesting method and system based on a central control system, and belongs to the technical field of agricultural automation. The system comprises a central control system, a sensing modeling module, a communication network and an operation cluster composed of a fruit shaking robot, a fruit receiving robot and a transportation robot. The central system is responsible for global task allocation and path planning; the sensing module constructs an orchard three-dimensional map through unmanned aerial vehicle scanning; the robot is provided with various sensors and has local autonomous obstacle avoidance capability. The harvesting method comprises the steps of environment perception modeling, task allocation, path planning, collaborative operation, fruit collection and transportation and the like, global planning is performed by adopting an improved A * algorithm, local real-time obstacle avoidance is realized in combination with a dynamic window method, and a dynamic task adjustment mechanism is introduced. Through cooperative parallel operation of multiple robots, the harvesting efficiency is remarkably improved, the labor cost and the fruit damage rate are reduced, and the robot is suitable for automatic harvesting operation of a large pecan orchard.