Instrumentation and Algorithms for Posture Estimation in Compliant Framed Modular Mobile Robotic Systems

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 fusion algorithm uses traditional Kalman filters and rigid axle kinematic models to predict the global posture of each axle. A Covariance Intersection filter then fuses disparate axle data using the RPS. The RPS consists of a compliant frame member instrumented with strain gauges and associated algorithms. Modeling and instrumentation of the RPS for large deflections is the remaining focus. While the RPS can predict relative posture, force, and moment, only posture estimates are used here. 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 RPS combined with a variety of standard sensor configurations on varied terrain demonstrate that the derived posture sensing techniques yield accurate relative posture estimates and posture regulation even on rugged terrain, which is a vast improvement over previous results. For my wife and daughters – without whose long suffering and patience this would never have come to be.