Distributed incremental arc-consistency reasoning method for temporal network in multi-probe collaborative mission planning

Multi-probe collaborative exploration represents a future trend in asteroid exploration. Significant improvements in onboard mission planning efficiency rely on rapidly inspecting the consistency of temporal constraints during probe mission planning. This research study proposes an innovative algorithm to address the dynamic temporal constraint problem in multi-probe collaborative mission planning. A Distributed Incremental Arc-Consistency (DIAC) algorithm, which limits the scope of constraint propagation through localized reasoning, is therefore proposed. A dynamic strategy for processing different constraints is designed to reduce the number of constraint checks. In its process of dynamically updating the temporal network, the algorithm avoids unnecessary global computations, thereby improving the efficiency of temporal consistency reasoning in planning. Simulations conducted on benchmark datasets demonstrate the effectiveness of the proposed algorithm. The DIAC algorithm effectively circumvents the privacy concerns of agents inherent in path-consistency algorithms. Compared with arc-consistency-based algorithms such as DisACSTP and GDAC, the proposed DIAC algorithm demonstrates superior performance. It reduces constraint reasoning time by 77.2 % and planning time by 23.69 % compared to DisACSTP. Against GDAC, DIAC consistently exhibits lower running time and fewer constraint checks, with its advantages remaining stable even as the number of agents increases or external constraints are introduced. In addition, DIAC significantly reduces redundant computation and inter-agent communication. These results highlight DIAC's potential to advance multi-probe mission planning in complex environments, particularly in scenarios requiring privacy preservation and real-time responsiveness.