Online Multi-Robot Coordination and Cooperation with Task Precedence Relationships
Multi-robot task allocation faces three key challenges: task priority constraints, intra-task coordination, and coalition-based collaboration among heterogeneous robots. Method: This paper proposes an online iterative reallocation algorithm that jointly models task dependencies via a task dependency graph and captures coalition-scale efficiency gains through a coalition-size effect model. To address the underlying NP-hard optimization problem, the method employs a network-flow-based approximation, validated via mixed-integer programming and greedy heuristics. High-fidelity simulation integrates realistic robot dynamics and physics-based modeling. Contribution/Results: Compared to offline approaches, the algorithm significantly improves robustness against task failures and model uncertainties. Experimental evaluation—across both stochastic task settings and real-world mission scenarios—demonstrates superior plan quality, enabling effective modeling, dynamic replanning, and coordinated execution of complex multi-robot missions.