Gap Navigation Trees for Discovering Unknown Environments

Reem Nasir,Ashraf Elnagar

doi:10.4236/ica.2015.64022

Abstract

We propose a motion planning gap-based algorithms for mobile robots in an unknown environment for exploration purposes. The results are locally optimal and sufficient to navigate and explore the environment. In contrast with the traditional roadmap-based algorithms, our proposed algorithm is designed to use minimal sensory data instead of costly ones. Therefore, we adopt a dynamic data structure called Gap Navigation Trees (GNT), which keeps track of the depth discontinuities (gaps) of the local environment. It is incrementally constructed as the robot which navigates the environment. Upon exploring the whole environment, the resulting final data structure exemplifies the roadmap required for further processing. To avoid infinite cycles, we propose to use landmarks. Similar to traditional roadmap techniques, the resulting algorithm can serve key applications such as exploration and target finding. The simulation results endorse this conclusion. However, our solution is cost effective, when compared to traditional roadmap systems, which makes it more attractive to use in some applications such as search and rescue in hazardous environments.

Highlights

The problem of “exploring the environment surrounding a robot” can be divided into different sub-categories based on three parameters
Maximum number of Gaps (MNG): This refers to the theoretical maximum number of gaps, in the given multiply connected environment
Algorithm-computed Number of Gaps (ANG): This refers to the total number of gaps encountered while constructing the Gap Navigation Trees (GNT) by the proposed algorithm

Summary

Introduction

The problem of “exploring the environment surrounding a robot” can be divided into different sub-categories based on three parameters. The first parameter describes the environment itself and it is divided into simple and multiply connected. Simple environments are environments that have no holes (i.e., obstacles) or discontinuity in its walls while multiply connected environments include holes. The second parameter is status of the obstacles whether it is static or dynamic. As more information is made available to the robot, the problem becomes. How to cite this paper: Nasir, R. and Elnagar, A. (2015) Gap Navigation Trees for Discovering Unknown Environments.

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Results

Conclusion