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Adaptive Learning and Self-Organization in Swarm Robotics

Adaptive Learning and Self-Organization in Swarm Robotics

Samik Acharya, Sima Das, Kitmo
Copyright: © 2024 |Pages: 24
ISBN13: 9798369312773|ISBN13 Softcover: 9798369345122|EISBN13: 9798369312780
DOI: 10.4018/979-8-3693-1277-3.ch008
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MLA

Acharya, Samik, et al. "Adaptive Learning and Self-Organization in Swarm Robotics." Bio-inspired Swarm Robotics and Control: Algorithms, Mechanisms, and Strategies, edited by Parijat Bhowmick, et al., IGI Global, 2024, pp. 116-139. https://doi.org/10.4018/979-8-3693-1277-3.ch008

APA

Acharya, S., Das, S., & Kitmo. (2024). Adaptive Learning and Self-Organization in Swarm Robotics. In P. Bhowmick, S. Das, & F. Arvin (Eds.), Bio-inspired Swarm Robotics and Control: Algorithms, Mechanisms, and Strategies (pp. 116-139). IGI Global. https://doi.org/10.4018/979-8-3693-1277-3.ch008

Chicago

Acharya, Samik, Sima Das, and Kitmo. "Adaptive Learning and Self-Organization in Swarm Robotics." In Bio-inspired Swarm Robotics and Control: Algorithms, Mechanisms, and Strategies, edited by Parijat Bhowmick, Sima Das, and Farshad Arvin, 116-139. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-1277-3.ch008

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Abstract

This study explores neural architecture mapping (NAM) in human-swarm interaction (HSI), merging neuroscience, computer science, and robotics to enhance collaboration. It discusses NAM's concepts, challenges, and applications like neuroanatomy mapping, neural interface design, and algorithm development. Despite hurdles like neural complexity, NAM promises real-time monitoring, advanced brain-machine interfaces, and seamless HSI integration. The chapter offers an overview of NAM's aims, methods, and impacts on human-robot symbiosis and swarm systems. Theoretical neuroscience, human factor studies, and swarm robotics inform NAM, integrating neural networks with robotics for AI-driven swarm behavior. Adaptive learning in swarm robotics develops autonomous algorithms, enhancing collaboration. This research aims to improve swarm robotics' efficiency across various domains.

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