Model Checking of Safety Properties
Formal Methods in System Design
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FCRC '96/WACG '96 Selected papers from the Workshop on Applied Computational Geormetry, Towards Geometric Engineering
Temporal logic motion planning for dynamic robots
Automatica (Journal of IFAC)
Falsification of LTL Safety Properties in Hybrid Systems
TACAS '09 Proceedings of the 15th International Conference on Tools and Algorithms for the Construction and Analysis of Systems: Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2009,
Receding horizon control for temporal logic specifications
Proceedings of the 13th ACM international conference on Hybrid systems: computation and control
VMCAI'07 Proceedings of the 8th international conference on Verification, model checking, and abstract interpretation
Efficient model checking of safety properties
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Motion planning with dynamics by a synergistic combination of layers of planning
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CAV'11 Proceedings of the 23rd international conference on Computer aided verification
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Proceedings of the tenth ACM international conference on Embedded software
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This paper considers the problem of motion planning for a hybrid robotic system with complex and nonlinear dynamics in a partially unknown environment given a temporal logic specification. We employ a multi-layered synergistic framework that can deal with general robot dynamics and combine it with an iterative planning strategy. Our work allows us to deal with the unknown environmental restrictions only when they are discovered and without the need to repeat the computation that is related to the temporal logic specification. In addition, we define a metric for satisfaction of a specification. We use this metric to plan a trajectory that satisfies the specification as closely as possible in cases in which the discovered constraint in the environment renders the specification unsatisfiable. We demonstrate the efficacy of our framework on a simulation of a hybrid second-order car-like robot moving in an office environment with unknown obstacles. The results show that our framework is successful in generating a trajectory whose satisfaction measure of the specification is optimal. They also show that, when new obstacles are discovered, the reinitialization of our framework is computationally inexpensive.