Abstract
Space exploration refers to constructing a map with the aid of sensor data. This exploration is achieved utilizing a group of robots in an obstacle cluttered environment and distributing tasks amongst these robot(s). The robotic configuration is equipped with sensors to acquire data from the surroundings and to ensure collision-free motion. This paper presents a framework for the design of a Hybrid Stochastic Optimizer (HSO) for multi-robot space exploration. The proposed algorithm augments deterministic Coordinated Multi-Robot Exploration (CME) and stochastic Arithmetic Optimization (AO) techniques for maximizing the utility. The framework initially utilizes deterministic CME to ascertain the cost and utility values of adjacent cells around robot(s). The overall solution accuracy is then improved utilizing the Arithmetic Optimization algorithm. The proposed utilization of hybrid is interpreted that the algorithm starts with deterministic technique and continues off with stochastic method until the required improved solution with the desired accuracy is achieved. The effectiveness of the proposed Hybrid Stochastic Optimizer is ascertained by training the multi-robotic framework in various complexity maps. The results efficacy is then demonstrated by comparing the results of the HSO algorithm with those achieved from two contemporary techniques namely conventional CME and hybrid CME with whale optimizer. Results demonstrate that the proposed HSO algorithm significantly improved the exploration parameters by enhancing the explored area and reducing the search time.
Highlights
The research in the area of mobile robotics covers a wide variety of subjects [1,2,3,4,5,6,7], with their broaden genre being Unmanned Aerial Vehicles (UAV) [8,9,10,11,12,13,14,15,16,17,18]
This paper presents a framework for the design of a Hybrid Stochastic Optimizer (HSO) for multi-robot space exploration
The results efficacy is demonstrated by comparing the results of the HSO algorithm with those achieved from two contemporary techniques namely conventional Coordinated Multi-Robot Exploration (CME) and hybrid CME with whale optimizer
Summary
The research in the area of mobile robotics covers a wide variety of subjects [1,2,3,4,5,6,7], with their broaden genre being Unmanned Aerial Vehicles (UAV) [8,9,10,11,12,13,14,15,16,17,18]. Exploration’s primary purpose is to construct a finite map [20] in situations such as search and rescue operations, surveillance, data collection, and basic indoor moving applications [21] It aims to use autonomous multirobot system to traverse all of space without the need for controlled navigation. Machine learning has recognized bioinspired optimization algorithms to address the optimal solution to complex scientific and technical problems involving time and space complexities. These problems are inherently nonlinear in nature, and usually subjected to various path/terminal constraints [12]. With the growing research in swarm-based algorithms and their inherent tendency to find solution in unknown/random environment they have become an obvious choice for solving various problems involving local minima / maxima problems [28, 29] and other drawbacks of various competing algorithms [30,31,32].Subsequently, Swarm-based hybridized technique emerged as a potential methodology to resolve existing complications and provide efficient solution [33, 34]
Sign-in/Register to view highlights & summary 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.
