Tuning and sensitivity analysis of a hexapod state estimator

Abstract An important envisaged application of legged robots is the exploration and mapping of extreme environments with an unknown terrain. Corin is a hexapod designed at the University of Manchester, which is able to perform motions using footholds on surfaces perpendicular to the ground plane. This allows it to be able to navigate through confined and narrow spaces. The hexapod requires an accurate estimate of its pose in order to be able to perform these motions. Current state-of-the-art state estimators for legged robots that solely use proprioceptive sensors, fuse inertial and leg kinematic measurements through an extended Kalman filter (EKF). This paper describes the implementation and validation of a state estimator on the Corin hexapod whilst performing motions using surface perpendicular to the ground plane. Another novelty of the work is the analysis of the algorithm sensitivity to the filter parameters and motion variables, and the tuning of the EKF using particle swarm optimisation (PSO). The results show that the average error achieved was below 6% for both position and orientation estimates.