The CyCab : Bayesian navigation on sensory – motor trajectories

Autonomous navigation of a mobile robot is a widely studied problem in the robotics community. Most robots designed for this task are equipped with onboard sensor(s) to perceive the external world (sonars, laser telemeters, camera). Two main approaches to autonomous navigation have been proposed: reactive navigation, where the robot uses only its current perceptions to move and explore without colliding (e.g. Arkin [1998] or Bonasso et al. [1995]), and servoed navigation, in which the robot is given a preplanned reference trajectory and uses some closed-loop control law to follow it (e.g. Laumond et al. [1989] or Lamiraux et al. [1999]). In servoed problems, two classes of approaches can again be separated: state-space tracking (e.g. Hermosillo et al. [2003b,a]) and perceptionspace tracking (e.g. Malis et al. [2001] or Chaumette [1994]). State-space tracking implies two capabilities: first, to be given a reference trajectory in the state space, and second, to be able to localize the robot, also in the state space. Conversely, perceptionspace tracking implies that the trajectory is defined with respect to perception only, hence avoiding the need for global localization. A specific application of perception tracking is visual servoing, classically implemented as the convergence of the observed image to a fixed reference image. In this chapter, we are specifically interested in the case of perceptual tracking of a sensory– motor trajectory (SMT) with a mobile robot. We assume that: