Characterizing Error in Noncommutative Geometric Gait Analysis
This work addresses error reduction in gait optimization for robotics, but it is incremental as it builds on existing variational techniques without introducing a new method.
The paper tackled the problem of error in displacement estimates for robotic gait optimization by characterizing the influence of low-order terms and third-order effects, demonstrating that adjusting parameters like body coordinate and gait diameter can effectively manage these contributions in example systems such as the differential drive car and Purcell swimmer.
A key problem in robotic locomotion is in finding optimal shape changes to effectively displace systems through the world. Variational techniques for gait optimization require estimates of body displacement per gait cycle; however, these estimates introduce error due to unincluded high order terms. In this paper, we formulate existing estimates for displacement, and describe the contribution of low order terms to these estimates. We additionally describe the magnitude of higher (third) order effects, and identify that choice of body coordinate, gait diameter, and starting phase influence these effects. We demonstrate that variation of such parameters on two example systems (the differential drive car and Purcell swimmer) effectively manages third order contributions.