Free gait generation with reinforcement learning for a six-legged robot
Free gait generation with reinforcement learning for a six-legged robot
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DOI：
10.1016/j.robot.2007.08.001
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发表时间：
2008-03-31
影响因子：
4.3
通讯作者：
Leblebicioglu, Kemal
中科院分区：
文献类型：
--
作者：
Erden, Mustafa Suphi;Leblebicioglu, Kemal
本文针对六足机器人的自由步态生成和自适应强化学习问题进行了研究。使用开发的自由步态生成算法的机器人保持产生稳定的步态根据命令的速度。在自由步态生成中加入强化学习机制，使机器人选择更稳定的状态，并形成具有更大平均稳定裕度的连续行走模式。机器人在正常情况下行走时，没有外部影响引起不稳定，保证了机器人稳定行走，而强化学习只是提高了稳定性。的学习计划的适应性进行了测试，也为异常情况下的不足之一，后腿。机器人在福尔斯跌倒时得到负强化，在实现稳定过渡时得到正强化。通过这种方式，机器人学会用五条腿实现稳定行走的连续模式。所开发的自由步态生成与强化学习实时应用于实际机器人上的正常步行与不同的速度和学习的五条腿步行在异常情况下。(c)2007 Elsevier B.V.保留所有权利。
In this paper the problem of free gait generation and adaptability with reinforcement learning are addressed for a six-legged robot. Using the developed free gait generation algorithm the robot maintains to generate stable gaits according to the commanded velocity. The reinforcement learning scheme incorporated into the free gait generation makes the robot choose more stable states and develop a continuous walking pattern with a larger average stability margin. While walking in normal conditions with no external effects causing unstability, the robot is guaranteed to have stable walk, and the reinforcement learning only improves the stability. The adaptability of the learning scheme is tested also for the abnormal case of deficiency in one of the rear-legs. The robot gets a negative reinforcement when it falls, and a positive reinforcement when a stable transition is achieved. In this way the robot learns to achieve a continuous pattern of stable walk with five legs. The developed free gait generation with reinforcement learning is applied in real-time on the actual robot both for normal walking with different speeds and learning of five-legged walking in the abnormal case. (c) 2007 Elsevier B.V. All rights reserved.