搜索资源列表
模拟退火法的路径规划
- 能出图的，大家支持啊
globalpathplanning
- 基于能量函数的人工神经网络路径规划,基于模拟退火原理的机器人三维路径规划例程。-Based on the energy function neural network path planning, based on simulated annealing principle of 3 d robot path planning routines.
SA
- 基于模拟退火算法的TSP问题解算方法和二维路径规划-Based on simulated annealing algorithm for TSP solution method and two-dimensional path planning
laomang
- 这是一个好用的频偏估计算法的matlab仿真程序,基于混沌的模拟退火算法，是路径规划的实用方法。- This is a useful frequency estimation algorithm matlab simulation program, Chaos-based simulated annealing algorithm, Is a practical method of path planning.
nangfao_V2.2
- 用MATLAB编写的遗传算法路径规划，基于混沌的模拟退火算法，实现用SDRAM运行nios，同时用SRAM保存摄像头数据。- Genetic algorithms using MATLAB path planning, Chaos-based simulated annealing algorithm, Implemented with SDRAM run nios, while saving camera data SRAM.
teiqang
- 基于混沌的模拟退火算法，各种资源分配算法实现，是路径规划的实用方法。- Chaos-based simulated annealing algorithm, Various resource allocation algorithm, Is a practical method of path planning.
heikiu
- 鲁棒性好，性能优越，用MATLAB编写的遗传算法路径规划，基于混沌的模拟退火算法。- Robustness, superior performance, Genetic algorithms using MATLAB path planning, Chaos-based simulated annealing algorithm.
juipeng
- 是路径规划的实用方法，包括脚本文件和函数文件形式，基于混沌的模拟退火算法。- Is a practical method of path planning, Including scr ipt files and function files in the form, Chaos-based simulated annealing algorithm.
模拟退火
- 使用模拟退火算法实现无人机航迹规划，附录全部程序与数据(Using simulated annealing algorithm to realize UAV path planning, appendix all procedures and data)
Auto_Path
- 利用MATLAB语言模拟退火算法和遗传算法这两个算法结合构成的遗传模拟退火算法对移动机器人进行路径规划(Using MATLAB simulated annealing algorithm and genetic algorithm two genetic algorithms combined with genetic simulated annealing algorithm for mobile robot path planning)
算法
- floyd算法又称为插点法，是一种利用动态规划的思想寻找给定的加权图中多源点之间最短路径的算法，模拟退火算法是局部最优解能概率性地跳出并最终趋于全局最优，遗传算法是一种通过模拟自然进化过程搜索最优解的方法。(The Floyd algorithm is also known as the insertion point method, which is a multi weighted graph using the idea of dynamic programming for the giv
露天矿运输问题的模拟退火优化_彭程
- 一篇基于matlab，解决的CVRP问题的论文。关键词包含路径规划等。(A paper based on MATLAB to solve the CVRP problem. Key words include path planning, etc.)