Internal and external frontier‐based algorithm for autonomous mobile robot exploration in unknown environment

Abstract

Navigation in the absence of initial environmental information is a situation in which a robot is faced with the difficulty of traversing an unknown area for exploration with obtaining the environmental information simultaneously. Therefore, to complete and optimize the exploration efficiently, the robot needs an autonomous path-planning algorithm. This work proposes a new autonomous path-planning algorithm for exploration in an unknown environment based on paired frontiers, which we call internal and external frontiers algorithm (IEFA), that defines extended area for navigation of the mobile robot. For each exploration round, the robot defines external frontiers using the maximum range of sensors. Then, the robot generates internal frontiers, that is, pairs of external frontiers by varying the range of sensors. According to the size of each pair of frontiers, the algorithm generates the target point for robot navigation. The frontiers of internal layer are utilized as a main parameter for generation of next exploration point. We evaluated the proposed algorithm in simulation environments using the ROS toolbox of MATLAB and compared it with two previous exploration algorithms. From the experimental results, the proposed algorithm showed from 31% to 85% better performance in the path distance than previous algorithms.

Rmap+: Autonomous Path Planning for Exploration of Mobile Robot Based on Inner Pair of Outer Frontiers

“…After measuring of each frontier is performed, the submodule compares all frontiers with its corresponding pairs. If the size value of the inner frontier is less than or equal to its corresponding outer frontier, then that outer frontier's center will be selected as the next position for exploration [4].…”

“…Frontier cost estimation depends on the structure of frontiers, i.e., we make assumption that if the size of the inner frontier is larger than the size of corresponding pair of outer frontier, then this frontier does not have an informative area, and otherwise this frontier has a closed or non-informative area. An exploration strategy basing on this movement policy minimizes the exploration path distance and travel time [4]. Let F = {F 1 , F 2 , …, F n } is a set of Frontiers // F is frontiers which defined using MaxRange of sensor 3.…”

“…Methods can also be divided according to the completeness of information about the environment: methods with complete information (in this case, they speak of global path planning) and methods with incomplete information (usually we are talking about knowledge of the situation in the immediate vicinity of the robot, in this case we are talking about local route planning). Note that incomplete information on the environment can be a consequence of a changing environment, i.e., in a dynamic environment, path planning is usually local [4].…”