Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer

Abstract

In this paper, we used multi-objective optimization in the exploration of unknown space. Exploration is the process of generating models of environments from sensor data. The goal of the exploration is to create a finite map of indoor space. It is common practice in mobile robotics to consider the exploration as a single-objective problem, which is to maximize a search of uncertainty. In this study, we proposed a new methodology of exploration with two conflicting objectives: to search for a new place and to enhance map accuracy. The proposed multiple-objective exploration uses the Multi-Objective Grey Wolf Optimizer algorithm. It begins with the initialization of the grey wolf population, which are waypoints in our multi-robot exploration. Once the waypoint positions are set in the beginning, they stay unchanged through all iterations. The role of updating the position belongs to the robots, which select the non-dominated waypoints among them. The waypoint selection results from two objective functions. The performance of the multi-objective exploration is presented. The trade-off among objective functions is unveiled by the Pareto-optimal solutions. A comparison with other algorithms is implemented in the end.

Waypoint Mobile Robot Exploration Based on Biologically Inspired Algorithms

“…In the authors' previous studies [33], [34], coordinated multi-robot exploration and waypoint selection concepts [35], [36] using GWO were introduced in the probabilistic occupancy grid map. The optimization strategies in both studies showed one common drawback of such stochastic optimization methods, which is the aborted simulation runs.…”

“…In the current study, a new concept for the GWO exploration algorithm is presented, which differs from the previous ones [33], [34], that is, the occupancy probabilities of cells are not applied in this study. Also, the previous studies used eight neighbor points around the robot as candidates for the next robot position.…”

Special Issue on Mobile Robots Navigation

“…It tries to discover unknown areas of the environment to add them to the map so that the robot can have a more complete knowledge of the surroundings. Kamalova et al [3] develop a method to explore unknown indoor environments, with the purpose of building a model of them. They approach the problem as a multiple-objective exploration, using the Multi-Objective Grey Wolf Optimizer (MOGWO) algorithm, which employs static waypoints in the process, promoting the efficient exploration of indoor environments.…”

A Review of Control Algorithm for Autonomous Guided Vehicle

“…Bio-inspired techniques are most popular, and researchers are implementing them solving path planning problem for AGV or mobile robots [1]. The popular bio-inspired algorithms are based on the living creature behavior which include birds, bees, ants, whale, bat, fish, wolf [2][3][4][5][6][7][8]. Randomly exploring tree algorithm with pure pursuit controller can be found in [9].…”