Sensor Data Fusion for Body State Estimation in a Hexapod Robot with Dynamical Gaits

Abstract

We report on progress toward a continuous time full 6 DOF translational body state estimator for a hexapod robot executing a jogging gait (with 4 consecutive phases: tripod stance, liftoff transient, aerial, and touchdown transient) on level ground. We use a sequence of dynamical models imported into a standard Kalman Filter to fuse measurements from a novel leg pose sensor and a conventional inertial measurement unit. We implement this estimation procedure on the hexapod robot RHex and evaluate its performance using a visual ground truth measurement system. We also compare the relative performance of different fusion approaches implemented via different model sequences.