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Legged robots have shown remarkable advantages in navigating uneven terrain. However, realizing effective loco-motion and manipulation tasks on quadruped robots is still challenging. In addition, object and terrain parameters are generally unknown to the robot in these problems. Therefore, this paper proposes a hierarchical adaptive control framework that enables legged robots to perform loco-manipulation tasks without any given assumption on the object's mass, the friction coefficient, or the slope of the terrain. In our approach, we first present an adaptive manipulation control to regulate the contact force to manipulate an unknown object on unknown terrain. We then introduce a unified model predictive control (MPC) for loco-manipulation that takes into account the manipulation force in our robot dynamics. The proposed MPC framework thus can effectively regulate the interaction force between the robot and the object while keeping the robot balance. Experimental validation of our proposed approach is successfully conducted on a Unitree A1 robot, allowing it to manipulate an unknown time-varying load up to 7 kg (60% of the robot's weight). Moreover, our framework enables fast adaptation to unknown slopes or different surfaces with different friction coefficients.
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Adaptive-Force-Based Control of Dynamic Legged Locomotion Over Uneven Terrain
Agile-legged robots have proven to be highly effective in navigating and performing tasks in complex and challenging environments, including disaster zones and industrial settings. However, these applications commonly require the capability of carrying heavy loads while maintaining dynamic motion. Therefore, this article presents a novel methodology for incorporating adaptive control into a force-based control system. Recent advancements in the control of quadruped robots show that force control can effectively realize dynamic locomotion over rough terrain. By integrating adaptive control into the force-based controller, our proposed approach can maintain the advantages of the baseline framework while adapting to significant model uncertainties and unknown terrain impact models. Experimental validation was successfully conducted on the Unitree A1 robot. With our approach, the robot can carry heavy loads (up to 50% of its weight) while performing dynamic gaits such as fast trotting and bounding across uneven terrains.
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- Award ID(s):
- 2133091
- PAR ID:
- 10534276
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Robotics
- Volume:
- 40
- ISSN:
- 1552-3098
- Page Range / eLocation ID:
- 2462 to 2477
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TRO.2024.3381554
