Adaptive Swarm Intelligence with Meta-Learning for Efficient Autonomous Multi-Robot Navigation and Pick-and-Drop Operations in Warehouse Environments

This paper presents an adaptive swarm intelligence-based multi-robot system designed for autonomous navigation and cooperative task execution in a structured environment. The proposed system consists of a master robot and multiple slave robots coordinated using ESP-NOW communication. The master robot performs real-time line following, obstacle detection, and decision-making while dynamically sharing control parameters with slave robots. A meta-learning framework is integrated to improve navigation efficiency by optimizing turn timing, speed control, and obstacle response using historical performance data. Additionally, a web-based dashboard provides real-time monitoring and remote control capabilities, displaying key parameters such as speed variation, turn behavior, cycle completion, and delivery status of the swarm system. Experimental implementation demonstrates improved path stability, reduced navigation error, and enhanced coordination among swarm units, making the system suitable for warehouse automation and smart logistics applications