Dynamic contact sensing for articulated tracked vehicles

Articulated tracked vehicles (ATVs) are widely used on applications where terrain conditions are difficult and unpredictable, or the environment may be hazardous for humans [3]. Depending on their mechanical design, these robotic platforms can either increase or decrease the contact area of tracks with the ground, providing better traction on harsh terrains and, on the other hand, energy saving on horizontal nearlyflat surfaces. Such flexibility greatly depends on the ability of the controller to accurately adapt the active sub-tracks, namely flippers, to the terrain shape. In principle, the robot can relay on its perceptive capabilities to locally model the terrain and define a trajectory over this approximation. However, the complexity of the whole robot motion, e.g., due to the undefined contact points between the flippers and the terrain, greatly reduces the application of this method in practice. One effective solutions is to allow a compliance interaction between the flipper and the terrain. This approach completely avoids any control and modeling efforts over smooth surfaces, but also reduces the effective traction of the flippers. Still, on terrain discontinuities, care must be taken to avoid trapping the robot or part of it (e.g., on emergency stairs in urban scenarios). Clearly, any control strategy depends on the contact interaction between the flippers and the ground, and thus, the detection or sensing of the contact event may provide valuable information to the control design. By considering any flipper unit as part of a tree-shaped open kinematic chain rigidly attached to a mobile base, we can unveil the contact event by resorting to state-of-the-art dynamic collision detection strategies for robot manipulators, extended to ATV platforms. In other words, we assume that the contact event can be identified as an unexpected collision of the flipper with the unknown terrain. In particular, we are interested into exploit the generalized momenta Fault Detection and Isolation (FDI) [4], where the collision detection is based on unexpected transient perturbations of the failure signal dynamics. The failure signal, or residual, is obtained through a non-linear observer of the arm generalized momenta. Formally, the extension of the FDI approach to our case requires an accurate knowledge of the mobile platform dynamics, and thus, an accurate model of the terrain. In particular, as stated in [1][2], FDI cannot be achieved on one particular input channel in the presence of non structured disturbances acting on the same channel. That is, any unexpected motion of the mobile platform introduces disturbances resembling those introduced by a collision of the flipper. However, by assuming that the platform displacements and terrain fluctuation introduce disturbances that are limited by to some lowband frequency range, we might expect that the residual signal may present some disturbance patterns that can be identified and possible discriminated by those generated by unexpected hight-frequency collisions of the flippers with the ground. Our idea is to model the dynamic of the residual as a linear combination of two disturbance sources, that can be identified by its evolution patterns. To this end, we propose a statistical learning approach to estimate (i) the disturbance patterns and (ii) the mixing model to allow both the decomposition and classification of any residual signal.