Randomized Strategy for Walking in Streets for a Simple Robot
This addresses path planning for simple robots in unknown environments, but it is incremental as it builds on prior deterministic strategies.
The paper tackles the problem of navigating an unknown street with a minimally sensing robot, proposing a deterministic strategy that avoids memorization and a randomized strategy that guarantees the robot travels at most 5.33 times the shortest path distance on average.
We consider the problem of walking in an unknown street, for a robot that has a minimal sensing capability. The robot is equipped with a sensor that only detects the discontinuities in depth information (gaps) and can locate the target point as enters in its visibility region. First, we propose an online deterministic search strategy that generates an optimal search path for the simple robot to reach the target t, starting from s. In contrast with previously known research, the path is designed without memorizing any portion of the scene has seen so far. Then, we present a randomized search strategy, based on the deterministic strategy. We prove that the expected distance traveled by the robot is at most a 5.33 times longer than the shortest path to reach the target.