基于神经网络的改进行为协调控制及其在智能轮椅路径规划中的应用

摘要

It is necessary for legged robots to walk stably and smoothly on rough terrain. In this paper, a desired landing points (DLP) walking method based on preview control was proposed in which an off-line foot motion trace and an on-line modification of the trace were used to enable the robot to walk on rough terrain. The on-line modification was composed of speed modification, foot lifting-off height modification, step length modification, and identification and avoidance of unsuitable landing terrain. A planner quadruped robot simulator was used to apply the DLP walking method. The correctness of the method was proven by a series of simulations using the Adams and Simulink.%针对传统的基于行为的智能轮椅的路径规划方法在室外非结构环境下的路径规划效果差的问题,提出一种新的智能轮椅的路径规划算法.该算法利用模糊逻辑设计了基本控制行为,并在此基础上结合大量实际经验使用神经网络设计了行为协调控制器.改进的算法将仲裁机制和命令融合机制2种行为协调方法有效结合起来,并吸收了这2种行为协调方法的优点,从而改善了系统的反应速度,极大提高了控制精确；另一方面,该算法还可以识别陷阱区域并通过自主改变行为的权重方法控制轮椅逃出陷阱区域,因而具备了较强的人工智能特征.仿真和实物实验验证了该算法智能性高且实现简单.适用于室外非结构化环境下的机器人路径规划.