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.
Highlights
In robotics, exploration pertains to the process of scanning and mapping out an environment to produce a map, which can be used by a robot or group of robots for further work
As a solution to this issue, this paper proposes an algorithm that enhances the efficiency of the multi-robot exploration by using a multi-objective optimization strategy
The analysis of the MOGWO exploration algorithm takes into consideration two aspects of the objective function: how it explores and how it improves the accuracy of the map
Summary
Exploration pertains to the process of scanning and mapping out an environment to produce a map, which can be used by a robot or group of robots for further work. As a solution to this issue, this paper proposes an algorithm that enhances the efficiency of the multi-robot exploration by using a multi-objective optimization strategy. Despite the number of agents, the metaheuristic algorithms can be classified into single and multi-objective optimization techniques according to the number of objective functions. In our previous study [8], we used the coordinated multi-robot exploration [22] and GWO algorithms together as a hybrid. Using the MOGWO exploration, we defined two objectives for optimization, namely the maximization of the search for new area and the minimization of the inaccuracy of the explored map. This paper is organized as follows: in Section 2, we briefly recall different algorithms of multi-robot exploration and evolutionary optimization techniques used in related works.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
