Efficient multi-robot coverage in dynamic environments remains a critical challenge for applications such as warehouse automation, environmental monitoring, inspection in large spaces, SAR, other innovative applications in Industrial Inspections. Traditional methods, such as spanning tree coverage and area-region-based partitioning, as well as market-based approaches such as divide area-based region partition (DARP), are effective under static conditions but suffer from rigidity in handling dynamic obstacles and poor adaptability to real-time changes. This paper introduces a hybrid framework, Robust DARP and A*, which integrates dynamic, region-based partitioning algorithm (DARP) with A* path planning from point-to-point navigation to coverage, with region constraints that enable complete and efficient coverage and path planning in non-stationary environments. Key innovations include: Localized region repair mechanism; a three-tier repair hierarchy (local–regional–global); incremental region repair instead of complete recomputation to minimize computational overhead during dynamic obstacle insertion; workload-balanced partitioning strategy that ensures equitable task allocation; and a turn-minimizing path planner that enhances energy efficiency. Fair task distribution among multiple robots without centralized coordination, extensive simulation trials across diverse terrains and scales (2 to 20 robots) demonstrates that our method achieves: coverage ≥ 97.3% under significant dynamic obstacles. Near-optimal path efficiency (max/min ratio ≤ 1.15 ), and sub-second runtime for large teams. This work presents a robust, scalable solution that provides an algorithmic foundation for real-world autonomous deployment. Evaluation is performed entirely in simulation using Python and Pygame environments; future work will extend the framework to support robots with varying capabilities for real-world deployment and optimization.
Sharma et al. (2026) studied this question.