7results about How to "Quick planning" patented technology

The invention discloses an optimal path planning method of a pulse coupling neural network based on double constraints. The method comprises the following steps: mapping a path planning environment to a DC-PCNN network; a DC-PCNN neural network model is constructed, and all neurons are initialized; activating a target neuron, and recording the current neuron as a father node; calculating an exponential decay function of the torque deviation, and multiplying the calculated value as a penalty factor by an update item of the internal activity item; calculating a gravitational function value of the flow field constraint, and using the calculated value to update a current neuron dynamic threshold value; comparing the internal activity item with a dynamic threshold; when gt; if yes, activating the neuron, and recording the current neuron as a father node; repeating the steps S4-S6 until the initial neuron is activated; and backtracking all activated nodes, and planning an optimal path. According to the method, the search efficiency is remarkably improved while the path optimality is ensured.

The invention discloses an emergency evacuation planning method based on improved ant colony algorithm and genetic fusion, and belongs to the technical field of emergency management and public safety. The method comprises the following steps: constructing a multi-objective optimization model taking minimization of total evacuation time T, total road section risk R and total personnel panic index P as objectives, and solving the multi-objective optimization model based on fusion of an improved ant colony algorithm and a genetic algorithm to obtain an optimal evacuation path scheme. The evacuation efficiency, safety and personnel psychological factors are comprehensively considered, the state transition probability and the pheromone updating strategy of the ant colony algorithm are improved, the global search mechanism of the genetic algorithm is introduced for deep fusion, and finally, the evacuation efficiency can be improved in a complex evacuation scene. And a global optimal evacuation path giving consideration to timeliness, safety and personnel psychological factors is quickly planned, and the evacuation efficiency is effectively improved.

The application provides an antenna profile precision measurement method based on unmanned aerial vehicle photogrammetry. Firstly, flight and photogrammetry parameters of the unmanned aerial vehicle used for antenna profile precision measurement are preliminarily planned according to antenna parameters. Then, the light incidence angle, the frequency of being photographed and the intersection angle of the to-be-measured points on the main reflector of the antenna are calculated according to the antenna equation and the camera station position. The shooting effect of the unmanned aerial vehicle photogrammetry is evaluated according to the frequency of being photographed and the intersection angle of all the to-be-measured points on the main reflector of the antenna. Then, the camera shooting angle at the camera station coordinate position is planned based on the obtained each unmanned aerial vehicle camera station coordinate. The final obtained camera station position and shooting angle of the unmanned aerial vehicle photogrammetry are converted into a path planning file executable by the unmanned aerial vehicle, and the photogrammetry is automatically performed by the unmanned aerial vehicle photogrammetry system. The application can quickly plan the unmanned aerial vehicle shooting position and shooting angle under the premise of ensuring the measurement precision.

The invention discloses a whole body motion planning system and method based on contact mode switching, and belongs to the technical field of robots. The method comprises the following steps: acquiring a centroid feasibility map representing environmental geometric information and physical attributes; determining a centroid target trajectory from an initial state to a target state according to the centroid feasibility map; and based on the centroid target trajectory and the centroid feasibility map, according to a preset cost function, planning to obtain a contact mode sequence. The system includes modules for performing the method. According to the method, the guiding track is planned in the low-dimension centroid space firstly, then the track serves as a strong constraint to reversely solve the contact sequence, the problem of combinatorial explosion in traditional contact state space search is fundamentally avoided, and the technical problems that in the prior art, planning calculation complexity is high, and the real-time requirement is difficult to meet are solved.

This invention relates to the field of control, and in particular to an edge computing collaborative control method and system for heterogeneous interconnection of multiple devices in an intelligent manufacturing unit. Specifically, by collecting functional metadata and real-time status data of heterogeneous devices, a weighted directed acyclic service graph is constructed based on a unified resource representation framework, and the weights of the edges in the graph are initialized as collaborative costs. Then, the production task is parsed as the target state of the service graph, and multiple candidate collaborative paths are generated through reverse search. A resource occupancy spatiotemporal tensor is constructed for each path and the execution risk coefficient is calculated. The path with the smallest coefficient is selected as the optimal task sequence. Subsequently, control commands are distributed and theoretical state vectors are predicted. The feedback from the collected devices is fused into actual state vectors. The Mahalanobis distance between the two is calculated. If the distance exceeds a threshold, the current sequence is frozen, a compensation path is replanned, and the collaborative costs of the corresponding edges of the service graph are updated nonlinearly based on the distance and abnormal task segments.

This application discloses a route planning method, apparatus, electronic device, and storage medium. This application can acquire a first topographic map; generate an initial road curve for the first topographic map; divide the first topographic map into multiple first topographic blocks according to a preset division rule; determine a first target topographic block from the multiple first topographic blocks, the first target topographic block being a first topographic block containing curve segments of the initial road curve; perform route planning processing on the first target topographic block based on the curve segments contained therein to obtain sub-routes corresponding to the first target topographic block; and determine the target route corresponding to the first topographic map based on all the sub-routes corresponding to the first target topographic blocks. In this application embodiment, the computational resources required to plan the target route corresponding to the first topographic map can be reduced, thereby improving the efficiency of route planning.

The invention discloses a mountainous area environment multi-unmanned aerial vehicle path planning method based on a Q-learning optimized dual-objective eagle algorithm. The method comprises the steps that 1, a dual-objective function with the minimum unmanned aerial vehicle flight distance and the threat cost under the mountainous area background as the dual-objective function is constructed; 2, establishing spatial constraints such as a three-dimensional terrain environment, a mountain obstacle and a radar threat area and multi-machine related constraints; and 3, designing a Q-learning-fused multi-action disturbance strategy to guide adaptive behavior selection of the Balde Eagle algorithm in a search process, thereby obtaining an optimal unmanned aerial vehicle flight path. According to the method, the reinforcement learning strategy is embedded into the bald eagle algorithm, and the precision, stability and safety of unmanned aerial vehicle path planning are improved through adaptive search in a complex three-dimensional terrain, so that reliable support is provided for unmanned aerial vehicle autonomous operation in actual scenes of mountain reconnaissance and electric power inspection.