Author: Dr. Meenal Rajput
Abstract: Swarm robotics is an emerging discipline within robotics that focuses on the coordination of large numbers of relatively simple robots. These systems are inspired by the collective behavior of social insects and animals, aiming to achieve complex global objectives through local interactions. This paper explores adaptive control algorithms tailored for swarm robotics operating in unstructured environments, where traditional path-planning and mapping strategies often fail. We examine bio-inspired control techniques, reinforcement learning methods, and decentralized decision-making frameworks that enhance swarm adaptability, robustness, and scalability. The paper also evaluates the effectiveness of these adaptive algorithms through simulated and real-world case studies. Ultimately, the study underlines the potential of adaptive control in enabling autonomous swarm systems to operate efficiently in unpredictable and dynamic environments.
Keywords: Swarm Robotics, Adaptive Control, Reinforcement Learning, Decentralized Systems, Unstructured Environments, Autonomous Agents.
Full Issue
| View or download the full issue | PDF 34-37 |