GRAB: GRAdient-Based Shape-Adaptive Locomotion Control

Phodapol, Sujet; Chuthong, Thirawat; Leung, Binggwong; Srisuchinnawong, Arthicha; Manoonpong, Poramate; Dilokthanakul, Nat

doi:10.1109/lra.2021.3137555

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…To solve this problem, force feedback control methods [50,51] and compact force sensors [52] will be implemented to realise the adaptive force distribution among the feet and adapt the leg movements online during climbing. In particular, gradient-based shape-adaptive locomotion control (GRAB) with force feedback [53] will be employed to directly shape the CPG dynamics in the CPG layer in order to prolong the robot's stance phase or to change the robot climbing speed when the adhesion force is low. Furthermore, force feedbackbased local leg control and forward models [54] will be used to directly modify motor signals in the motor neuron layer in order to adapt individual legs to search for the ground if the expected adhesion force is missing during the stance phase.…”

Section: Discussionmentioning

confidence: 99%

A gecko-inspired robot with CPG-based neural control for locomotion and body height adaptation

Shao¹,

Wang²,

Ji³

et al. 2022

Bioinspir. Biomim.

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Today’s gecko-inspired robots have shown the ability of omnidirectional climbing on slopes with a low centre of mass. However, such an ability cannot eﬃciently cope with bumpy terrains or terrains with obstacles. In this study, we developed a gecko-inspired robot (Nyxbot) with an adaptable body height to overcome this limitation. Based on an analysis of the skeleton system and kinematics of real geckos, the adhesive mechanism and leg structure design of the robot were designed to endow it with adhesion and adjustable body height capabilities. Neural control with exteroceptive sensory feedback is utilised to realise body height adaptability while climbing on a slope. The locomotion performance and body adaptability of the robot were tested by conducting slope climbing and obstacle crossing experiments. The gecko robot can climb a 30o slope with spontaneous obstacle crossing (maximum obstacle height of 38% of the body height) and can climb even steeper slopes (up to 60o) without an obstacle or bump. Using 3D force measuring platforms for ground reaction force analysis of geckos and the robot, we show that the motions of the developed robot driven by neural control and the motions of geckos are dynamically comparable. To this end, this study provides a basis for developing climbing robots with adaptive bump/obstacle crossing on slopes towards more agile and versatile gecko-like locomotion.

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