Instrumentation and Algorithms for Posture Estimation in Compliant Framed Modular Mobile Robots

Abstract

Posture sensing techniques for Compliant Framed Modular Mobile Robots (CFMMR) are presented in this paper using a new Relative Posture Sensor (RPS) combined with standard sensors in a tiered fusion algorithm. The RPS consists of a compliant frame member instrumented with strain gauges and associated algorithms such that the RPS can predict relative posture. The first tier of the fusion algorithm uses traditional Kalman filters and rigid axle kinematic models to predict the global posture of each axle. In the second tier, a Relative Measurement Stochastic Posture Error Correction (RMSPEC) algorithm is introduced to fuse disparate axle data using the RPS. Experimental results are derived from over 60 trials operating the robot on high traction carpet, low traction sand, and sand with rugged rocky terrain. Results comparing the proposed sensory system with standard sensory systems demonstrate that the proposed techniques yield accurate relative posture estimates and posture regulation even on rugged terrain, which is a vast improvement over previous results.