Gap Navigation Trees for Discovering Unknown Environments

Abstract

We propose a motion\rplanning gap-based algorithms for mobile robots in an unknown environment for\rexploration purposes. The results are locally optimal and sufficient to\rnavigate and explore the environment. In contrast with the traditional\rroadmap-based algorithms, our proposed algorithm is designed to use minimal\rsensory data instead of costly ones. Therefore, we adopt a dynamic data\rstructure called Gap Navigation Trees (GNT), which keeps track of the depth\rdiscontinuities (gaps) of the local environment. It is incrementally\rconstructed as the robot which navigates the environment. Upon exploring the\rwhole environment, the resulting final data structure exemplifies the roadmap\rrequired for further processing. To avoid infinite cycles, we propose to use\rlandmarks. Similar to traditional roadmap techniques, the resulting algorithm\rcan serve key applications such as exploration and target finding. The\rsimulation results endorse this conclusion. However, our solution is cost\reffective, when compared to traditional roadmap systems, which makes it more\rattractive to use in some applications such as search and rescue in hazardous\renvironments.