Energy Efficient Pronking of a Series Elastic Actuated Quadrupedal Robot Using Trajectory Optimization and Functional Iterative Learning Control

Abstract

Monitoring expeditions in endangered habitats are currently performed by human experts. However, this approach has several disadvantages, including the limited amount of experts, cost-intensive expeditions, and the dangers that are posed by exploring dangerous terrains. Therefore, one can look into using quadrupedal legged robots that would collaborate with the human operators, which would be able to assist them by performing extra measurements in these dangerous habitats. An open problem in quadrupedal legged locomotion is robust periodic forward jumping, a.k.a. pronking, specifically for quadrupedal robots that have flexible joints placed in series. In this paper, we therefore propose a novel framework that generates an energy efficient pronking motion for a quadrupedal series elastic actuated legged robot. This periodic pronking motion is generated using a reduced order model and the serial elasticity of the joints is taken into account using an template-anchor approach.
To minimize the trajectory error we use Functional Iterative Learning Control (fILC) as feedforward control in parallel with a proportional-derivative feedback controller. The advantage of using fILC is that the elasticity of the quadrupedal robot is preserved and that the controller is able to learn the pronking motion in a small number of iterations. This framework is validated on an eight degrees of freedom series elastic actuated robot. In- and outdoor experiments show that this framework is able to work in unknown terrains.