Artificial Intelligence - Special issue on knowledge representation
Approximate reachability for linear systems
HSCC'03 Proceedings of the 6th international conference on Hybrid systems: computation and control
Hybrid estimation of complex systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Modelling mixed discrete-continuous domains for planning
Journal of Artificial Intelligence Research
Temporal planning in domains with linear processes
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A universal planning system for hybrid domains
Applied Intelligence
COLIN: planning with continuous linear numeric change
Journal of Artificial Intelligence Research
Risk-sensitive plan execution for connected sustainable home
BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Probabilistic planning for continuous dynamic systems under bounded risk
Journal of Artificial Intelligence Research
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Agile autonomous systems are emerging, such as unmanned aerial vehicles (UAVs), that must robustly perform tightly coordinated time-critical missions; for example, military surveillance or search-and-rescue scenarios. In the space domain, execution of temporally flexible plans has provided an enabler for achieving the desired coordination and robustness. We address the challenge of extending plan execution to underactuated systems that are controlled indirectly through the setting of continuous state variables. Our solution is a novel model-based executive that takes as input a temporally flexible state plan, specifying intended state evolutions, and dynamically generates a near-optimal control sequence. To achieve optimality and safety, the executive plans into the future, framing planning as a disjunctive programming problem. To achieve robustness to disturbances and tractability, planning is folded within a receding horizon, continuous planning framework. Key to performance is a problem reduction method based on constraint pruning. We benchmark performance through a suite of UAV scenarios using a hardware-in-the-loop testbed.