Achieving dynamic controllability for simple temporal networks with uncertainty and sensing timepoints

Abstract

With the widespread deployment of advanced sensors, executors in real-world planning domains can acquire information about temporal uncertainty through sensing activities, enabling the resolution of previously intractable planning problems. This development underscores the necessity of finding more dynamically controllable plans by leveraging sensing activities that reduce temporal uncertainty, thereby enhancing the solvability of planning problems under uncertainty. This paper addresses the problem of transforming weakly controllable temporal plans into dynamically controllable ones by inserting a minimal set of sensing activities. We propose Simple Temporal Network with Uncertainty and Sensing Timepoints (STNUST), an extension of the traditional Simple Temporal Network with Uncertainty (STNU) model that explicitly incorporates sensing activities. To support this model, we develop ST-BOSA, a novel algorithm composed of four interdependent modules for constraint propagation, redundancy elimination, sensing timepoint selection, and insertion. Extensive experiments on Mars rover-inspired scenarios and randomly generated networks demonstrate that the proposed approach effectively achieves dynamic controllability for networks while minimizing the number of inserted sensing timepoints. This framework shows promise for integration into planning systems in sensor-rich domains such as space exploration. Future work includes improving scalability and extending support to multi-agent settings.