基于降级模糊算法的爬壁机器人避障控制

针对罐体表面作业过程中避障的问题,提出了改进模糊避障控制算法。为保证爬壁机器人能够缩短到达目标点的时间,除爬壁机器人与障碍物距离量外,增加爬壁机器人与目标点的角度量作为输入变量,速度、角速度作为输出量,确定了各参数的论域与隶属度,建立了模糊规则表,采用重心法解模糊化;考虑罐体环境的复杂多变性,提出优雅降级避障控制策略,在探测传感器受扰失灵的情况下仍能够越过障碍物到达目标点。通过与人工势场避障法仿真比较,改进模糊控制法可更有效地避开障碍物,并在探测传感器异常情况下,也能保证爬壁机器人到达目标点,体现了该避障算法的优越性与可靠性。最后进行了爬壁机器人避障实验,验证了该算法的可行性。

Obstacle Avoidance Control of Wall-climbing Robot Based on Degraded Fuzzy Algorithms

Aiming at the problem of obstacle avoidance during the operation of the tank surface, an improved fuzzy obstacle avoidance control algorithm is proposed. To ensure that the wall climbing robot can shorten the time to reach the target point, in addition to the distance between the wall-climbing robot and the obstacle, increasing angle between the wall-climbing robot and the target point as an input variable. Two variables of speed and angular velocity are used as output. After determining the domain and membership of each parameter, a fuzzy rule table is established. Defuzzification is the center of gravity of the application. Considering the complex variability of the tank environment, proposing a strategy of elegant downgrading obstacle avoidance control. The detection sensor can still cross the obstacle to reach the target point in the event of a temporary environmental failure. By comparing with the artificial potential field obstacle avoidance method in simulation, the improved fuzzy control method can avoid obstacles more effectively. And when the detection sensor is abnormal, the climbing wall robot can also be guaranteed to reach the target point. It demonstrates the superiority and reliability of the obstacle avoidance algorithm. Finally, carry out the obstacle avoidance experiment of the wall climbing robot and verified the feasibility of the algorithm.