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针对传统的基于模糊逻辑的智能轮椅避障方法参数选取依赖设计者经验的问题,提出了一种能够自主学习的模糊神经网络智能轮椅避障控制算法.该算法结合模糊逻辑和神经网络各自的优点,并采用状态控制变量记录全向轮椅的运动状态,解决使用者期望目标方向和轮椅避障方向的选择问题,优化了避障路径,更好地满足用户对智能轮椅的舒适性需求.仿真和实物实验证明:该算法提高了避障的智能性和使用者的乘用舒适性,适用于智能轮椅的避障控制.
Aiming at the problem of traditional parameter selection of wheelchair obstacle avoidance based on fuzzy logic, which is dependent on designer’s experience, a fuzzy neural network intelligent wheelchair obstacle avoidance control algorithm based on fuzzy logic is proposed, which combines the advantages of fuzzy logic and neural network , And state variable is used to record the motion state of omnidirectional wheelchair to solve the user’s choice of target direction and wheelchair obstacle avoidance direction and to optimize obstacle avoidance path so as to better meet the user’s comfort requirements for smart wheelchair.Simulation and Practical experiments show that this algorithm improves the obstacle avoidance intelligence and user comfort, which is suitable for the obstacle avoidance control of intelligent wheelchair.