Technical Report CIS9517

Authors: I. Kamon, E. Rimon and E. Rivlin
Abstract: We present TangentBug, a new range-sensor based globally convergent navigation algorithm for mobile robots. We incorporate the idea of the locally shortest path, using the tangent path, into the decisions of a local planner which exploits range-data to produce short paths. We adjust the structure of the tangent graph, which was defined for a completely known environment, and introduce a local range-data based version of it, termed the local tangent graph, or LTG. Our algorithm belongs to the Bug family from [12], which combines local planning with global information that guarantees convergence. We re-formulate the basic behaviors of the Bug family, and define new transition conditions for switching between them. The TangentBug algorithm uses the basic behaviors of motion towards the target and obstacle boundary following. Let $d(x,T)$ be the distance of the robot, located at point $x$, from the target $T$. Then during the motion towards the target, $d(x,T)$ decreases monotonically. During the boundary following, the robot attempts to escape from a local minimum of $d(x,T)$. The TangentBug algorithm specifically exploits range-data, and uses it for choosing the locally optimal direction while moving towards the target, for choosing the boundary following direction when boundary following behavior is initiated, and for the transition condition which terminates boundary following. The simulation results show improvement in the path length as the sensor tangent increases. The results indicate a significant advantage of TangentBug relative to the algorithm VisBug from [12], in all the tested scenarios.
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