This research presents a simple and novel improved ant colony optimization for path planning of unmanned wheeled robot. Our main concern is to avoid the random deadlock situation and to reach at the destination using the shortest path, to decrease lost ants and improve the efficiency of solutions. The aforementioned reasons, we design an adaptive heuristic function by adopting the Euclidean distance between the ant and the target destination, in order to avoid the initial blindness and later singleness of ant path searching. The historical best path when appropriate to retain the previous effort would supersede the current worst path. Simulation results under random maps show that the improved ant colony optimization considerably increases the number of effective ants. During the searching process, the probability to find the optimal path increases, as well as the search speed. Moreover, we also compare the improved ant colony optimization performance with the simple ant colony optimization.

中文翻译：

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基于改进蚁群优化的轮式无人机器人路径规划

本研究提出了一种用于无人轮式机器人路径规划的简单而新颖的改进蚁群优化方法。我们主要关注的是避免随机死锁情况并使用最短路径到达目的地，减少丢失的蚂蚁并提高解决方案的效率。基于上述原因，我们通过采用蚂蚁与目标目的地之间的欧几里德距离来设计自适应启发式函数，以避免蚂蚁路径搜索的初始盲目性和后期单一性。在适当的情况下保留先前努力的历史最佳路径将取代当前最坏路径。随机地图下的仿真结果表明，改进后的蚁群优化大大增加了有效蚂蚁的数量。在寻找过程中，找到最优路径的概率增加，搜索速度也增加。此外，我们还将改进的蚁群优化性能与简单的蚁群优化进行了比较。