全局静态环境下机器人路径规划的紧凑遗传算法在8位微控制器上的硬件实现

Mian Ali, Omer Farooq, M. D. Khan, S. Haxha
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引用次数: 0

摘要

本文给出了全局静态环境下机器人路径规划的遗传算法的硬件实现。对遗传算法进行了改进,并在8位微控制器PIC18F452上实现。遗传算法采用预定义的优先级来生成父路径,而不是随机生成父路径,从而减少迭代次数和处理能力。设计了一种无人地面车辆(UGV),它无线接收起始节点、最终目的地和障碍物,然后使用不同的优先级创建多个父路径,交叉创建新的子路径,以距离作为适应度函数确定最优路径或最短路径,同时避开障碍物,并使用具有三维运动的步进电机到达目的地。环境是5×5静态网格图,在路径规划之前已知障碍物。MCU确定无障碍物的最优路径,并以最小的距离到达目的地。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hardware Implementation of Compact Genetic Algorithm for Robot Path Planning in Globally Static Environment in 8-bit Microcontroller
In this paper, hardware implementation of genetic algorithm for Robot path planning in Globally static Environment is presented. Genetic algorithm is modified and implemented in 8-bit Microtontroller (MCU) PIC18F452. The genetic algorithm is designed to decrease number of iteration and processing power by using predefined priorities for parenti initial path generation rather than creating parent paths randomly. The unmanned ground vehicle (UGV) is designed which receives starting node, final destination and obstacles wirelessly, it then create multiple parent paths by using different priorities, cross over to create new child paths, uses distance as fitness function for determining Optimal or shortest path while avoiding the obstacles and uses stepper motors with three- dimensional movements to reach its destination. The environment is 5×5 static Grid Map in which obstacles are known before path planning. The MCU determines optimal path with no obstacles and require minimum distance to reach its destination.
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