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

Abstract

<jats:sec> <jats:title content-type="abstract-subheading">Purpose</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Findings</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Originality/value</jats:title> <jats:p>Systematic presentation of the proposed technique attracts a wide readership among researchers where AI technique is the application criteria.</jats:p> </jats:sec>

Novel Implementation of Multi-Robot Space Exploration Utilizing Coordinated Multi-Robot Exploration and Frequency Modified Whale Optimization Algorithm

“…Nicola et al [55] presented the integration of Ant Colony Optimization technique with Whale Optimization method for finding the optimal path for a mobile robot by achieving multi objectives of a) finding the optimal path, b) path smoothness. Kumar et al [56] propose the hybridization of advanced sine cosine algorithm (ASCA) with advanced ant colony optimization algorithm (AACO) for searching the optimal path. The method was implemented in real-time with sensors, where the sensors detect the obstacles and find the global best position and in the next phase, the ACO algorithm is programmed in such a way that it evaluates and selects the next best value.…”

Static and dynamic path optimization of multiple mobile robot using hybridized fuzzy logic-whale optimization algorithm

“…Low et al 32 have explained improved Q-learning for optimal path planning. Kumar et al 33 have presented hybrid controller of sine-cosine and ant colony algorithm for path optimization. Muni et al [34][35][36] have presented different AI technique, hybridized with fuzzy controller for navigational control of humanoid robots.…”

Hybrid IWD-GA: An Approach for Path Optimization and Control of Multiple Mobile Robot in Obscure Static and Dynamic Environments

“…Ni et al 47 have proposed an improved shuffled frog leaping algorithm for motion control of mobile robots. Kumar et al 48 have proposed an hybrid SCA-ACO algorithm for path optimization of mobile robots. Rosas et al 49 have used an hybrid algorithm for path planning of mobile robots.…”