A Robotic Wheel Locally Transforming the Diameter for the Locomotion on Rough Terrain

Moriya, Naoki; Shigemune, Hiroki; Sawada, Hideyuki

doi:10.1109/icma49215.2020.9233579

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…A similar mechanism was also applied to origami wheels [28], [29]. Another strategy adopts stick-shaped legs, hidden in the wheel or extended out [30], [31]. A wheel comprised of two halfcircle legs can be either folded into a semicircular wheel [32], [33] or deviated radially, forming two legs [34], [35].…”

Section: B Existing Hybrid Locomotion Systemsmentioning

confidence: 99%

$\mathbf{\alpha }$-WaLTR: Adaptive Wheel-and-Leg Transformable Robot for Versatile Multiterrain Locomotion

et al. 2023

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Adaptability is a fundamental yet challenging requirement for mobile robot locomotion. This article presents α-WaLTR, a new adaptive wheel-and-leg transformable robot for versatile multiterrain mobility. The robot has four passively transformable wheels, where each wheel consists of a central gear and multiple leg segments with embedded spring suspension for vibration reduction. These wheels enable the robot to traverse various terrains, obstacles, and stairs while retaining the simplicity in primary control and operation principles of conventional wheeled robots. The chassis dimensions and the center of gravity location were determined by a multiobjective design optimization process aimed at minimizing the weight and maximizing the robot's pitch angle for obstacle climbing. Unity-based simulations guided the selection of the design variables associated with the transformable wheels. Following the design process, α-WaLTR with an embedded sensing and control system was developed. Experiments showed that the spring suspension on the wheels effectively reduced the vibrations when operated in the legged mode and verified that the robot's versatile locomotion capabilities were highly consistent with the simulations. The system-level integration with an embedded control system was demonstrated via autonomous stair detection, navigation, and climbing capabilities.

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