Optimal path search and control of mobile robot using hybridized sine-cosine algorithm and ant colony optimization technique

Abstract

Purpose This paper aims to incorporate a hybridized advanced sine-cosine algorithm (ASCA) and advanced ant colony optimization (AACO) technique for optimal path search with control over multiple mobile robots in static and dynamic unknown environments. Design/methodology/approach The controller for ASCA and AACO is designed and implemented through MATLAB simulation coupled with real-time experiments in various environments. Whenever the sensors detect obstacles, ASCA is applied to find their global best positions within the sensing range, following which AACO is activated to choose the next stand-point. This is how the robot travels to the specified target point. Findings Navigational analysis is carried out by implementing the technique developed here using single and multiple mobile robots. Its efficiency is authenticated through the comparison between simulation and experimental results. Further, the proposed technique is found to be more efficient when compared with existing methodologies. Significant improvements of about 10.21 per cent in path length are achieved along with better control over these. Originality/value Systematic presentation of the proposed technique attracts a wide readership among researchers where AI technique is the application criteria.