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Abstract Bipedal locomotion over compliant terrain is an important and largely underexplored problem in the robotics community. Although robot walking has been achieved on some non-rigid surfaces with existing control methodologies, there is a need for a systematic framework applicable to different bipeds that enables stable locomotion over various compliant terrains. In this work, a novel energy-based framework is proposed that allows the dynamic locomotion of bipeds across a wide range of compliant surfaces. The proposed framework utilizes an extended version of the 3D dual spring-loaded inverted pendulum (Dual-SLIP) model that supports compliant terrains, while a bio-inspired controller is employed to regulate expected perturbations of extremely low ground-stiffness levels. An energy-based methodology is introduced for tuning the bio-inspired controller to enable dynamic walking with robustness to a wide range of low ground-stiffness one-step perturbations. The proposed system and controller are shown to mimic the vertical ground reaction force (GRF) responses observed in human walking over compliant terrains. Moreover, they succeed in handling repeated unilateral stiffness perturbations under specific conditions. This work can advance the field of biped locomotion by providing a biomimetic method for generating stable human-like walking trajectories for bipedal robots over various compliant surfaces. Furthermore, the concepts of the proposed framework could be incorporated into the design of controllers for lower-limb prostheses with adjustable stiffness to improve their robustness over compliant surfaces.
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Soft Foot Sensor Design and Terrain Classification for Dynamic Legged Locomotion
Dynamic legged locomotion is being explored as a means to maneuver on rugged and unstructured terrains. However, limited foot contact sensing capabilities often prohibit bipedal robots from being deployed on complex terrains. Locomotion over cluttered outdoor environments requires the contacting foot to be aware of terrain geometries, stiffness, and granular media properties. To achieve this, we designed a new soft contact pad integrated with a variety of embedded sensors, including tactile, acoustic, capacitive, and temperature sensors, as well as an accelerometer. In addition, we devised a terrain classification algorithm based on features extracted from those sensors and various real-world terrains. The classifier uses these features as inputs and classifies various terrains via Random Forests and a memory function. Our cross-validation tests demonstrate that the proposed classification algorithm achieves an accuracy of about 96.5%, manifesting the applicability of this foot sensing device to bipedal locomotion over diverse terrains.
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- Award ID(s):
- 1924978
- PAR ID:
- 10184199
- Date Published:
- Journal Name:
- 2020 3rd IEEE International Conference on Soft Robotics
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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