Multi-Robot Space Exploration: An Augmented Arithmetic Approach

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.

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

“…A possible algorithm for the methodology mentioned above is presented in Algorithm 1, and a possible literature survey is jotted down in Table 8. For a similar approach, another possible direction with the same concept but different optimization technique can be found in [251].…”

Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis

“…The rapidly increasing fleet of UAVs, along with the widening sphere of their utility, therefore presents a serious challenge for the designers to formulate unique optimal control strategies. However, technological advancements in the aviation sector [4][5][6][7] and ground control vehicles [8][9][10][11][12][13][14][15][16][17][18][19] paved the way for the development of hi-fidelity systems. These UAVs help researchers by providing means to collect multi-spectral information with limited resources and data collection times which is critical for time sensitive dynamic data [20].…”

Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model

“…On many topics in artificial intelligence, metaheuristic algorithms have been applied and have led to many solutions [27][28][29]. To solve feature selection problems, metaheuristic algorithms are now widely used [30]. According to parameters governed by the way these algorithms operate, they generate subsets randomly.…”