Adaptive Swarm Navigation with Online Multi-Objective Path Optimization using Atta-Bots

Path optimization in swarm robotics is typically validated in simulation, with limited implementation on physical multi-robot platforms. This paper presents a methodology integrating physical robotic swarms with real-time mapping and online multi-objective path optimization using NSGA-II, rarely implemented beyond simulation. A key contribution is incorporating danger (representing a pheromone spatial distribution) as an optimization objective alongside distance, enabling safe navigation in hazardous environments. Validation with up to three Atta-Bot robots demonstrated that coverage increased consistently with robot count and execution time, aligning with multi-robot exploration literature. NSGA-II successfully identified Pareto-optimal paths balancing distance minimization and danger avoidance. Results showed high correlation between distance and danger (R² = 0.96), though both objectives derive from independent spatial factors. This framework enables danger-aware cooperative navigation for applications in disaster response, environmental monitoring, and precision agriculture.