Feedback Linearization of Inertially Actuated Jumping Robots

Abstract

Inertially Actuated Jumping Robots (IAJR) provide a promising new means of locomotion. The difficulty of IAJR is found in the hybrid nature of the ground contact/flying dynamics. Recent research studies in our Systems Lab have provided a family tree of inertially actuated locomotion systems. The proposed Tapping Robot is the most prompt member of this tree. In this paper, a feedback linearization controller is introduced to provide controllability given the 3-dimensional motion complexity. The research objective is to create a general controller that can regulate the locomotion of Inertially Actuated Jumping Robots. The expected results can specify a desired speed and/or jump height, and the controller ensures the desired values are achieved. The controller can achieve the greatest response for the Basketball Robot at a maximum jump height of 0.25 m, which is greater than the former performance with approximately 0.18 m. The design paradigm used on the Basketball Robot was extended to the Tapping Robot. The Tapping Robot achieved a stable average forward velocity of 0.0773 m/s in simulation and 0.157 m/s in experimental results, which is faster than the forward velocity of former robot, Pony III, with 0.045 m/s.

Modeling, analysis and control of robot–object nonsmooth underactuated Lagrangian systems: A tutorial overview and perspectives

“…• Hopping robots: most of the studies are based on compliant contact [328,329,184,330], hence can be recast into a complementarity framework, see section 3.6.1. In view of the material in the forefoing sections, would it be possible to use of the stability results in [150] for a bouncing ball with moving base, and of the equivalence between the one degree-of-freedom juggler and the vertical hopper, to design a controller for the hopper based on internal sinusoidal excitation ?…”

Artificial Intelligence Technology in Basketball Training Action Recognition

“…The results show that the basketball robot can overcome the uncertainty in the environment and effectively achieve good path planning, which verifies the feasibility of the fuzzy control algorithm and the validity and correctness of the path planning strategy (Zhi and Jiang, 2020 ). Cox et al ( 2021 ) built a generic controller for regulating the motion of an inertia-driven jumping robot. The robot can specify the desired speed and jump height.…”

“…The controller can ensure that the desired value is achieved. The controller can achieve the maximum response of the basketball robot the maximum jump height of 0.25 m (Cox et al, 2021 ). Carnevale et al ( 2021 ) used the emerging distributed constrained aggregation optimization framework for application to basketball robots.…”

On the analysis and control of a bipedal legged locomotion model via partial feedback linearization

“…This uniquely facilitates the optimization of robustness and stability in various locomotion models (Hamzacebi and Morgul 2017). Consequently, partial feedback linearization has been successfully applied to actual robot platforms, thanks to their tractable exact analytical formulation (Terry et al 2016, Cox et al 2021.…”