Difference between revisions of "Crossentropy Temporal Logic Motion Planning"
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Revision as of 06:14, 15 May 2016
Scott C. Livingston, Eric M. Wolff, Richard M. Murray
Submitted, 2015 International Conference on Hybrid Systems: Computation and Control (HSCC)
This paper presents a method for optimal trajectory generation for discretetime nonlinear systems with linear temporal logic (LTL) task specifications. Our approach is based on recent advances in stochastic optimization algorithms for optimal trajectory generation. These methods rely on estimation of the rare event of sampling optimal trajectories, which is achieved by incrementally improving a sampling distribution so as to minimize the crossentropy. A key component of these stochastic optimization algorithms is determining whether or not a trajectory is collisionfree. We generalize this collision checking to e�ciently verify whether or not a trajectory satisfies a LTL formula. Interestingly, this verification can be done in time polynomial in the length of the LTL formula and the trajectory. We also propose a method for e�ciently reusing parts of trajectories that only partially satisfy the specification, instead of simply discarding the entire sample. Our approach is demonstrated through numerical experiments involving Dubins car and a generic pointmass model subject to complex temporal logic task specifications.
 Conference Paper: http://www.cds.caltech.edu/~murray/preprints/lwm15hscc_s.pdf
 Project(s): iCyPhy