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Cooperative Task Execution in Insect-Inspired Robot Swarms Using Reinforcement Learning
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Author(s): R. Elakya (Sri Venkateswara College of Engineering, India), S. Surya (Saveetha Engineering College, India), G. Abinaya (University of Southern Queensland, Springfield, Australia), T. Manoranjitham (SRM Institute of Science and Technology, Ramapuram, India)and R. Thanga Selvi (Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India)
Copyright: 2025
Pages: 16
Source title:
Exploring the Micro World of Robotics Through Insect Robots
Source Author(s)/Editor(s): U. Vignesh (Vellore Institute of Technology, Chennai, India), Annavarapu Chandra Sekhara Rao (Indian Institute of Technology (ISM), Dhanbad, India), Saleem Raja (University of Technology and Applied Sciences, Shinas, Oman)and P. Chitra (GITAM University, Bangalore, India)
DOI: 10.4018/979-8-3693-6150-4.ch010
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Abstract
This chapter proposes a novel framework for cooperative task execution in a swarm of insect-inspired robots by using Reinforcement Learning (RL) algorithms. Inspired by the collaborative behaviors observed in social insects, such as ants and bees, the proposed framework enables robots to autonomously coordinate their actions to accomplish complex tasks in dynamic environments. Each robot in the swarm acts as an autonomous agent capable of learning and adapting its behavior through interactions with the environment and feedback from other robots. By applying RL algorithms, such as Q-learning or Deep Q-Networks (DQN), robots learn optimal action policies to maximize task performance while considering the collective objectives of the swarm. we demonstrate the effectiveness and scalability of our approach in various cooperative tasks, including exploration, foraging, and object manipulation. This project showcases the potential of RL-based approaches to enhance the autonomy and adaptability of robotic swarms for collaborative task execution in real-world scenarios.
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