Central Pattern Generator With Inertial Feedback for Stable Locomotion and Climbing in Unstructured Terrain
Central Pattern Generator With Inertial Feedback for Stable Locomotion and Climbing in Unstructured Terrain
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DOI：
10.1109/icra.2018.8461013
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发表时间：
2018-05
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影响因子：
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通讯作者：
Guillaume Sartoretti;Samuel Shaw;Katie Lam;Naixin Fan;M. Travers;H. Choset
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作者：
Guillaume Sartoretti;Samuel Shaw;Katie Lam;Naixin Fan;M. Travers;H. Choset
受脊椎动物运动神经系统的启发，中枢模式发生器（CPG）模型可用于设计关节型机器人的步态，如爬行，游泳或腿式机器人。在这些模型中引入用于步态适应的感觉反馈可以提高此类机器人在具有挑战性的地形中的运动性能。然而，迄今为止，许多CPG模型已经专门开发用于穿越水平地形的开环步态生成。在本文中，我们提出了一种新的方法，将惯性反馈到CPG框架的控制身体姿势在陡峭的，非结构化的地形上的腿运动。也就是说，我们适应机器人的每一条腿的极限环与时间，同时产生运动和身体姿态控制。我们实验验证我们的方法在六足机器人，在各种陡峭的，具有挑战性的地形（草地，岩石幻灯片，楼梯）。我们展示了如何使用我们的方法来平衡机器人的身体，使其能够以相对恒定的速度移动，即使地形陡峭和复杂性阻止使用开环控制策略。
Inspired by the locomotor nervous system of vertebrates, central pattern generator (CPG) models can be used to design gaits for articulated robots, such as crawling, swimming or legged robots. Incorporating sensory feedback for gait adaptation in these models can improve the locomotive performance of such robots in challenging terrain. However, many CPG models to date have been developed exclusively for open-loop gait generation for traversing level terrain. In this paper, we present a novel approach for incorporating inertial feedback into the CPG framework for the control of body posture during legged locomotion on steep, unstructured terrain. That is, we adapt the limit cycle of each leg of the robot with time to simultaneously produce locomotion and body posture control. We experimentally validate our approach on a hexapod robot, locomoting in a variety of steep, challenging terrains (grass, rocky slide, stairs). We show how our approach can be used to level the robot's body, allowing it to locomote at a relatively constant speed, even as terrain steepness and complexity prevents the use of an open-loop control strategy.