Legged robots have the advantage of being able to maneuver rough, unstructured terrains unlike their wheeled counterparts. However, many legged robots require multiple sensors and online computations to specify the gait, trajectory or contact forces in real-time for a given terrain, and these methods can break down when sensory information is unreliable or not available. Over the years, underactuated mechanisms have demonstrated great success in object grasping and manipulation tasks due to their ability to passively adapt to the geometry of the objects without sensors. In this paper, we present an application of underactuation in the design of a legged robot with prismatic legs that maneuvers unstructured terrains under open-loop control using only four actuators – one for stance for each half of the robot, one for forward translation, and one for steering. Through experimental results, we show that prismatic legs can support a statically stable stance and can facilitate locomotion over unstructured terrain while maintaining its body posture.
Central pattern generator with inertial feedback for stable locomotion and climbing in unstructured terrain.
: 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.
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