Optimized cuckoo search algorithm using tournament selection function for robot path planning. (26th May 2021)

• Record Type: Journal Article
• Authors: Sharma, Kaushlendra; Singh, Shikha; Doriya, Rajesh
• Is Part Of: International journal of advanced robotic systems. Volume 18:Number 3(2021)
• Publication Date: 2021-05-26
• Subjects: Cuckoo search -- path length -- path planning -- tournament function; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881421996136
• Languages: English
• ISSNs: 1729-8806

Abstract

Acceptability of mobile robots in various applications has led to an increase in mobile robots' research areas. Path planning is one of the core areas which needs to be improvised at a higher level. Optimization is playing a more prominent role these days. The nature-inspired algorithm is contributing to a greater extent in achieving optimization. This article presents the modified cuckoo search algorithm using tournament selection function for robot path planning. Path length and Path time are the algorithm's parameters to validate the effectiveness and acceptability of the output. The cuckoo search algorithm's fundamental working principle is taken as the baseline, and the tournament selection function is adapted to calculate the optimum path for robots while navigating from its initial position to final position. The tournament selection function is replacing the concept of random selection done by the cuckoo search algorithm. The use of tournament selection overcomes local minima for robots while traversing in the configuration space and increases the probability of giving more optimum results. The conventional cuckoo search algorithm whose random selection mechanism may lead to premature convergence may fall into the local minima. The use of tournament selection function increases the probability of giving better results as it allows all the possible solution to take part in the tournament. The results are analysed and compared with other relevant work like cuckoo search algorithm and particle swarm optimization technique and presented in the article. The proposed method produced a better output in terms of path length and path time.