Abstract
Intelligent mobile robots belong to advanced material handling systems that are finding increasing applications in modern manufacturing environments. Due to their mobility, mobile robots can adapt to changing environments in manufacturing systems and carry out various tasks such as transportation, inspection, exploration, or manipulation. On the other hand, features such as autonomy, intelligence, flexibility, and the capability to learn allow mobile robots to be widely used for many tasks, including material handling, material transporting, or part feeding tasks. Motion planning and scheduling of an intelligent mobile robot are one of the most vital issues in the field of robotics since these factors are essential for contributing to the efficiency of the overall manufacturing system. The robot scheduling problem belongs to the class of NP-hard problems and numerous efforts have been made to develop methodologies for obtaining optimal solutions to the problem. Therefore, this paper presents a review of the literature sources and gives a comparative analysis of biologically inspired optimization algorithms used to solve this problem. Four different optimization algorithms, namely genetic algorithms (GA), particle swarm optimization algorithm (PSO), chaotic particle swarm optimization algorithm (cPSO), and whale optimization algorithm (WOA) are proposed and implemented in Matlab software package. The experimental verification is carried out by using real-world benchmark examples. The experimental results indicate that all aforementioned algorithms can be successfully used for optimization of single mobile robot scheduling problem.
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Miljković, Z., Petrović, M. (2020). Single Mobile Robot Scheduling Problem: A Survey of Current Biologically Inspired Algorithms, Research Challenges and Real-World Applications. In: Karabegović, I. (eds) New Technologies, Development and Application II. NT 2019. Lecture Notes in Networks and Systems, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-030-18072-0_4
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DOI: https://doi.org/10.1007/978-3-030-18072-0_4
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