A high speed motion capture method and performance metrics for studying gaits on an insect-scale legged robot

Abstract

This paper develops a custom motion capture system that uses vision-based methods to rapidly and accurately track the body and leg position/orientation of a 1.43g legged microrobot, the Harvard Ambulatory MicroRobot (HAMR). Two new generalized metrics for quantifying locomotion performance are defined: amplitude-normalized stride correlation, and percent ineffective stance. Six different gaits are run on HAMR to validate the experimental setup and establish baseline performance. Furthermore, HAMR is compared with the cockroach, Blaberus Discoidalis, and with other legged robots. Future studies can leverage the experimental setup to study gait selection and transitions for small legged systems.