Analysis of Path Finding Techniques for Flying Robots through Intelligent Decision-Making Algorithms in Quantum Inspired Computing Environment

• Published in: Wireless personal communications Vol. 135; no. 3; pp. 1561 - 1580
• Main Authors: Maity, Ritu, Mishra, Ruby, Pattnaik, Prasant Kumar
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial intelligence; Communications Engineering; Computation; Computer Communication Networks; Decision making; Design; Drones; Energy consumption; Engineering; Evacuations & rescues; Flight; Food; Health care industry; Multiple criterion; Networks; Optimization; Parameters; Robots; Signal,Image and Speech Processing; Software; Surveillance; Unmanned aerial vehicles; Vehicles; Quantum PSO flying robot; ABC; Path planning; MCDM; PSO; TOPSIS
• ISSN: 0929-6212; 1572-834X
• DOI: 10.1007/s11277-024-11125-z

Path planning is one of the most significant and challenging parts in the development of unmanned aerial vehicles. Over years many path-planning techniques are proposed and are being successfully used in various fields. Intelligent algorithms can be used for building autonomous drones. Though a number of algorithms haven been proposed in past few years but there is lack of research papers which compares different path planning algorithm and to find the optimal one by considering important parameters required for path planning of flying robot. Here we have used five varieties of algorithms i.e. ABC, ACO, PSO Quantum PSO, and hybrid algorithm which is a combination of ABC and PSO for path planning of our developed fixed-wing type flying robot for operating inside a closed room environment. We have used the quantum-inspired computing method as its search performance is better as compared to classical techniques. Then we tried to compare and find the best algorithm for our flying robot out of the above five algorithms using multi-criteria decision making and TOPSIS where the following parameters like minimum cost, the shortest path traveled, and the least time taken were considered to find the most relevant results for autonomous flying robot path planning.