Search - ant optimization robot path planning

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[matlab] antljgh DL : 0: 目前己存在许多优化算法用来解决该问题，但不少算法都存在一定局限性，如当算法的约束条件较多时，很难求解复杂环境的路径规划问题等。本文根据机器人路径规划算法的研究现状和向智能化、仿生化发展的趋势，研究了一种基于改进蚁群算法的机器人全局路径规划方法。 -At present, there are many optimization algorithm has been used to solve the problem, many algorithms have certain limitations exist, such as when the algorithm is more restrictive conditions, it is hard to solve complex environmental problems such as path planning. Based Robot Path Planning Algorithm for the status quo and to the intelligence, the development of bionic trends, research which is based on ant colony algorithm to improve robot global path planning method.
Date : 2026-01-11 Size : 2.19mb User : 高阳
[matlab] ACOrout DL : 0: 移动机器人路径规划是机器人学的一个重要研究领域。它要求机器人依据某个或某些优化原则(如最小能量消耗，最短行走路线，最短行走时间等)，在其工作空间中找到一条从起始状态到目标状态的能避开障碍物的最优路径，本代码应用蚁群算法来解决这个问题！-Mobile robot path planning is an important research field of robotics. It requires one or some of the robot based on the principle of optimization (such as the minimum energy consumption and the shortest walking route, the shortest travel time, etc.), to find the path from the initial state to the target state can avoid an obstacle in its optimal working space path, the code ant colony algorithm to solve this problem!
Date : 2026-01-11 Size : 2kb User : 冯丁
[matlab] dual-robot-path-planning DL : 0: 双机器人协调路径规划，局部路径使用蚁群算法，全局路径使用粒子群算法-Double coordinate path planning and local path using ant colony algorithm, the global path using the particle swarm optimization
Date : 2026-01-11 Size : 7.44mb User : 路婷
[matlab] GAforPathPlaning DL : 0: 采用栅格对机器人的工作空间进行划分，再利用优化算法对机器人路径优化，是采用智能算法求最优路径的一个经典问题。目前，采用蚁群算法在栅格地图上进行路径优化取得比较好的效果，而利用遗传算法在栅格地图上进行路径优化在算法显得更加难以实现。利用遗传算法处理栅格地图的机器人路径规划的难点主要包括：1保证路径不间断，2保证路径不穿过障碍。用遗传算法解决优化问题时的步骤是固定的，就是种群初始化，选择，交叉，变异，适应度计算这样，那么下面我就说一下遗传算法求栅格地图中机器人路径规划在每个步骤的问题、难点以及解决办法。(It is a classical problem to divide the workspace of the robot by grids and optimize the path of the robot by using optimization algorithm. At present, the ant colony algorithm is used to optimize the path on the grid map, and the genetic algorithm is used to optimize the path on the grid map, which is more difficult to achieve. The difficulties of using genetic algorithm to deal with the path planning of robot on raster map mainly include: 1. guaranteeing that the path is uninterrupted, 2. guaranteeing that the path does not cross obstacles. The steps of genetic algorithm in solving optimization problems are fixed, that is, population initialization, selection, crossover, mutation, fitness calculation. Then I will talk about the problems, difficulties and solutions of genetic algorithm in each step of robot path planning in raster map.)
Date : 2026-01-11 Size : 5kb User : adkuhd8wy