Frontier-based Exploration of Mobile-robot system using Bio-inspired Metaheuristic Optimization approaches
Creators: name: Kamalova, Albina
Publisher: name: Zenodo
Year: 2020
Subjects: subject: Robotics/methods
subject: Mapping

description: Mobile robot system consists of multiple problem-solving objectives in motion, particularly, path planning, localization, navigation, formation, target tracking, and exploration, in which a map is primarily required for a robot to solve its tasks, whereas exploration aims to produce one. For example, solving the path planning problem requires to set coordinates of start and goal robot positions in a known map and then, to compute a short available path among them. It means that in order to solve the path planning problem, a map should be given or exploration should be implemented in advance. This implies that autonomous exploration is a fundamental research issue in robotics that can be studied individually or paired with other objectives. Some examples of typical applications of the exploration operation are floor-cleaning, planetary exploration, underwater exploration, and other wide-known search and rescue exploration missions, which can be dangerous for humans in terms of emergency occurred situations. This study dedicates to the exploration problem of the mobile robot system.The objective of exploration lies in constantly driving a robot in unknown areas without any initial parameters. While the robot moves to the uncertainties in the environment, the sensor information is gathered for creating a map. The finite constructed map in the end is the main aim of this mission. However, no less important role plays optimal time and trajectories of the exploration process, even though the mission is completed. For example, in case, a robot drives multiple times around an obstacle, it spends much more total time and power consumption, which require to minimize by the computational methods. The selection of the computational algorithms depends on the several properties of the exploration such as map representation, robot vision, and the number of robots.This thesis is proposed the novel methods in the exploration founded on an occupancy grid map and an occupancy probability map, sensory data, single and multi-robot systems properties. The proposed methods guide the exploration process heuristically, which leads the robot or group of robots to test the area by moving in random directions. In order to implement the stochastic exploration algorithm, the metaheuristic optimization algorithms were applied in this study.The metaheuristic optimization (also called the nature-inspired optimization) is a bunch of algorithms, in which the animal behaviors are described into the mathematical models. The manner of the optimal solution guidance was imitated from the hunting, searching for food, or evolution processes in the animal nature. Some well-known optimization algorithms, just a few mentioned here, are based on wolf and whale hunting, searching for food by ants and bats, that were described in mathematic models called Grey Wolf Optimizer (GWO), Whale Optimization (WO), Ant Optimization Colony (ACO) and Bat Optimization (BA) algorithms, respectively. This thesis applied the nature-inspired optimization techniques in the mobile-robot exploration for the effective searching of environment uncertainties.