Design and implementation of an Intelligent path planning controller Using hybrid dragonfly cuckoo search Algorithm for mobile robot navigation

Abstract

Currently, navigating an autonomous mobile robot has been one of the newlinemost significant and complex problems in the areas of computer and control newlineengineering. The advancement of emerging technologies in the field of newlinecomputational engineering has inspired people to utilize cutting-edge newlineapproaches. The techniques of nature and its phenomena have been the newlineprimary source of inspiration for researchers to develop new ideas to solve newlinereal-world complex engineering problems. Natural phenomena such as ants newlineforming straight lines and birds flying in optimal shape (murmuration) are newlineexamples of highly optimized intelligent natural behaviours. The natureinspired newlineoptimization algorithms have a proven history in finding optimal newlinesolutions in several complex engineering tasks and Non-deterministic newlinePolynomial-time (NP-hard) optimization problems. newlineThe mobile robot navigation challenge is considered an NP-hard newlineoptimization problem, and hence this work looks at how nature-inspired newlineoptimization algorithms can be implemented to solve the autonomous mobile newlinerobot navigational challenges. The navigation challenges of autonomous newlinemobile robots are built on crucial interlinked components, namely map newlineconstruction, path planning, and locomotion. Based on these considerations, a newlinepath planning algorithm for a mobile robot is developed to determine the newlineoptimum collision-free path between the starting point and the destination newlinepoint in a highly cluttered/complex environment. newline