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A LEARNING APPROACH TO ENABLE LOCOMOTION OF MULTIPLE ROBOTIC AGENTS OPERATING IN NATURAL TERRAIN ENVIRONMENTS

机译:在自然地形环境中启用多种机器人的运动的学习方法

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This paper presents a methodology that utilizes soft computing approaches to enable locomotion of multiple legged robotic agents operating in natural terrain environments. For individual robotic control, the locomotion strategy consists of a hybrid FSM-GA approach that couples leg orientation states with a genetic algorithm to learn necessary leg movement sequences. To achieve multi-agent formations, locomotion behavior is driven by using a trained neural network to extract relevant distance metrics necessary to realize desired robotic formations while operating in the field. These distance metrics are then fed into local controllers for realizing linear and rotational velocity values for each robotic agent. Details of the methodology are discussed, and experimental results with a team of mobile robots arc presented.
机译:本文提出了一种利用软计算方法来实现在自然地形环境中运行的多腿机器人特工的运动的方法。对于单个机器人控制,运动策略由混合FSM-GA方法组成,该方法将腿部定向状态与遗传算法结合在一起,以学习必要的腿部运动序列。为了实现多主体编队,通过使用训练有素的神经网络来驱动运动行为,以提取在野外作业时实现所需机器人编队所需的相关距离度量。然后将这些距离度量输入到本地控制器中,以实现每个机器人代理的线性和旋转速度值。讨论了该方法的详细信息,并给出了一组移动机器人的实验结果。

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