: 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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Position Control and Variable-Height Trajectory Tracking of a Soft Pneumatic Legged Robot
Soft pneumatic legged robots show promise in their ability to traverse a range of different types of terrain, including natural unstructured terrain met in applications like precision agriculture. They can adapt their body morphology to the intricacies of the terrain at hand, thus enabling robust and resilient locomotion. In this paper we capitalize upon recent developments on soft pneumatic legged robots to introduce a closed-loop trajectory tracking control scheme for operation over flat ground. Closed-loop pneumatic actuation feedback is achieved via a compact and portable pneumatic regulation board. Experimental results reveal that our soft legged robot can precisely control its body height and orientation while in quasi-static operation based on a geometric model. The robot can track both straight line and curved trajectories as well as variable-height trajectories. This work lays the basis to enable autonomous navigation for soft legged robots.
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- NSF-PAR ID:
- 10318936
- Date Published:
- Journal Name:
- 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Light has been recently intensively explored to power robots. However, most existing light‐driven robots have limited locomotion modalities, with constrained locomotion capabilities. A light‐powered soft robot with a bioinspired design is demonstrated, which can crawl on ground, squeeze its way through a small channel, and jump over a barrier. The arch‐shaped robot is made up of liquid crystal elastomer–carbon nanotube composite. When a light source with a power intensity of around 1.57 W cm−2is scanned over the surface of the robot, it deforms and crawls forward. With an increase in the light scanning speed, it can deform sufficiently to pass through a channel 25% lower than its body height. Subjected to light irradiation, it can also deform to a closed loop, gradually store elastic energy, and suddenly release it to jump over a wall or onto a step quickly, with a jumping distance around eight times its body length and jumping height around five times its body height. Mathematical models for quantitatively understanding the multimodal locomotion of this light‐powered soft robot are also presented.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IROS51168.2021.9635966
