Description: 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. Platform: |
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Author:高阳 |
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Description: 蚁群优化，寻找最短路径，也可用于机器人规划-Ant colony optimization to find the shortest path can also be used for robot planning Platform: |
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Author:周景 |
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Description: 基于蚁群算法的机器人的路径规划问题蚁群算法，一种与传统的数学规划原理截然不同的，模拟自然生态系统以求解复杂优化问题(如NPC(NP Complete)类问题，典型的有TSP(Traveling Saleman Problem)问题)的仿生优化算法，因其较强分布式计算机制、鲁棒性、易于与其他方法相结合等优点，使得蚁群算法具有较广泛应用领域，为那些最优化技术难以解决的组合优化问题提供了一类新的切实可行的解决方案。从最初的一维的静态优化问题扩展到多维的动态组合优化问题，包括车辆路径规划，工程设计，电力系统，图像处理，通讯系统，机器人系统，以及制造系统等领域。该文所研究的内容是其中之一——机器人的路径规划问题。
机器人路径规划是机器人学的一个重要研究领域，引起了众多研究者的关注。栅格法模型是众多环境建模方法中的一类实时性很强的路径规划模型。该文引入蚁群算法的基本思想，接着在基本蚁群算法上提出改进策略，并通过经典的旅行商问题验证改进蚁群算法的正确性，然后在改进的蚁群算法的基础上使用栅格法的路径规划策略, 并编制相应程序进行验证。
-Ant colony optimization, a mathematical programming with the traditional principle distinct simulate natural ecosystems to solve complex optimization problems (such as the NPC (NP Complete) class of problems, typically a TSP (Traveling Saleman Problem) problem) bionic optimization algorithm , because of its strong distributed computer system, robustness, ease combined with other methods, etc., makes the ant colony algorithm has a wider application areas, for those most difficult to solve optimization combinatorial optimization problems provide a new class of practical solutions. From the initial one-dimensional static optimization problem extended to multi-dimensional dynamic combinatorial optimization problems, including vehicle path planning, engineering design, power systems, image processing, communication systems, robotic systems, and manufacturing systems. In this paper, the contents of the study is one of these- robot path planning issues. Platform: |
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Author:lin |
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Description: 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! Platform: |
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Author:冯丁 |
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Description: 将改进的蚁群算法与路径几何优化相结合,用于解决移动机器人的全局路径规划问题.算法结合机器人的越障性能对移动机器人的环境空间进行建模.通过设置初始信息素加快蚂蚁的搜索速度,同时设置自适应信息素挥发机制,解决特定地图中初始信息素的干扰问题 设置自适应路径长度,筛选规划路径的优劣 提出由路径优劣程度决定的信息素散播策略,并从几何原理出发,对规划路径进行优化处理,加快最优解的收敛速度.仿真结果验证了该算法的有效性和普遍应用性,在随机给定的环境地图中,该算法能够迅速规划出最优路径.-The improved ant colony algorithm and path geometry optimization were applied to solve the global path planning problem of mobile robot．The obstacle performance was combined in the proposed algorithm to establish the workspace model of the robot．By setting the initial pheromone，the ant searching speed was accelerated，and through the adaptive pheromone mechanism，the interference problem of initial pheromone to the specific map was solved．In addition，the pros and cons of the path planning were screened by setting the adaptive path length． It was also proposed that the pheromone spreading strategy was decided by the path length． Meanwhile，according to the principle of geometry，the planning path was optimized to accelerate the convergence speed of the optimal solution．The effectiveness and universal appfication of the proposed algorithm was demonstrated by the simulation results．In the random environment map，the optimal path could be rapidly obtained with the proposed algorithm．
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Description: 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 Platform: |
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Author:路婷 |
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Description: 用MATLAB实现复杂环境移动机器人路径规划算法的研究，分别采用了A星算法，迪杰斯特拉算法，蚁群算法以及蚁群寻径迪杰斯特拉优化路径的混合算法，并通过仿真进行验证。(Research and implementation of complex environment of mobile robot path planning algorithm with MATLAB, respectively, using the A star algorithm, Dijkstra algorithm, ant colony algorithm and ant colony optimization hybrid algorithm to optimize the path of Dijkstra size, and verified by simulation.) Platform: |
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Author:窗外阴雨凉
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Description: 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.) Platform: |
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Author:adkuhd8wy |
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