Abstract
The Bug family algorithms navigate a 2-DOF mobile robot in a completely unknown environment using sensors. TangentBug is a new algorithm in this family, specifically designed for using a range sensor. TangentBug uses the range data to compute a locally short est path, based on a novel structure termed the local tangent graph (LTG). The robot uses the LTG for choosing the locally optimal di rection while moving toward the target, and for making local short- cuts and testing a leaving condition while moving along an obstacle boundary. The transition between these two modes of motion is governed by a globally convergent criterion, which is based on the distance of the robot from the target. We analyze the properties of TangentBug, and present simulation results that show that Tangent Bug consistently performs better than the classical Bug algorithms. The simulation results also show that TangentBug produces paths that in simple environments approach the globally optimal path, as the sensor's maximal detection-range increases. The algorithm can be readily implemented on a mobile robot, and we discuss one such implementation.
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