Abstract-We present DistBug, a new navigation algorithm for mobile robots which exploits range data. The algorithm belongs to the Bug family, which combines local planning with global information that guarantees convergence. Most Bug-type algorithms use contact sensors and consist of two reactive modes of motion: moving toward the target between obstacles and following obstacle boundaries. DistBug uses range data in a new "leaving condition" which allows the robot to abandon obstacle boundaries as soon as global convergence is guaranteed, based on the free range in the direction of the target. The leaving condition is tested directly on the sensor readings, thus making the algorithm simple to implement. To further improve performance, local information is utilized for choosing the boundary following direction, and a search manager is introduced for bounding the search area. The simulation results indicate a significant advantage of DistBug relative to the classical Bug2 algorithm.The algorithm was implemented and tested on a real robot, demonstrating the usefulness and applicability of our approach.
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 shortest path, based on a novel structure termed the local tangent graph (LTG). The robot uses the LTG for choosing the locally optimal direction while moving toward the target, and for making local shortcuts 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.
Abstract-We present DistBug, a new navigation algorithm for mobile robots which exploits range data. The algorithm belongs to the Bug family, which combines local planning with global information that guarantees convergence. Most Bug-type algorithms use contact sensors and consist of two reactive modes of motion: moving toward the target between obstacles and following obstacle boundaries. DistBug uses range data in a new "leaving condition" which allows the robot to abandon obstacle boundaries as soon as global convergence is guaranteed, based on the free range in the direction of the target. The leaving condition is tested directly on the sensor readings, thus making the algorithm simple to implement. To further improve performance, local information is utilized for choosing the boundary following direction, and a search manager is introduced for bounding the search area. The simulation results indicate a significant advantage of DistBug relative to the classical Bug2 algorithm.The algorithm was implemented and tested on a real robot, demonstrating the usefulness and applicability of our approach.
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