Internal posture sensing for a flexible frame modular mobile robot

A sensor fusion algorithm for flexible framed modular mobile robots is presented in this paper. This algorithm uses traditional Kalman filters and rigid axle kinematic models to predict the global posture of each axle. A Covariance Intersection filter is then proposed for fusing these axle modules using data provided by the compliant frame module. Modeling and instrumentation of the compliant frame module is the remaining focus. The instrumented frame is required to estimate the relative posture of the axles as well as the force components and moment that the beam exerts on each axle. These estimates must also be valid for large deflections in order to accommodate a reasonable turning radius. To accomplish these goals the beam equations are derived. A linear interpolation of the strain gauge data is used to calculate posture, force, and moment estimates. Experimental results show that the frame module can yield accurate relative posture estimates for large deflection.