Murray at 06:07, 10 June 2016

2016-06-10T06:07:00Z

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	This paper presents a method for optimal trajectory generation for discrete-time 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 cross-entropy. A key component of these stochastic optimization algorithms is determining whether or not a trajectory is collision-free. 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 re-using 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 point-mass model subject to complex temporal logic task specifications.		This paper presents a method for optimal trajectory generation for discrete-time 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 cross-entropy. A key component of these stochastic optimization algorithms is determining whether or not a trajectory is collision-free. 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 re-using 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 point-mass model subject to complex temporal logic task specifications.
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Murray: htdb2wiki: creating page for 2014j_lwm15-hscc.html

2016-05-15T06:14:42Z

htdb2wiki: creating page for 2014j_lwm15-hscc.html

New page

{{HTDB paper
| authors = Scott C. Livingston, Eric M. Wolff, Richard M. Murray
| title = Cross-entropy Temporal Logic Motion Planning
| source = Submitted, 2015 International Conference on Hybrid Systems: Computation and Control (HSCC)
| year = 2014
| type = Conference Paper
| funding = iCyPhy
| url = http://www.cds.caltech.edu/~murray/preprints/lwm15-hscc_s.pdf
| abstract =
This paper presents a method for optimal trajectory generation for discrete-time 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 cross-entropy. A key component of these stochastic optimization algorithms is determining whether or not a trajectory is collision-free. 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 re-using 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 point-mass model subject to complex temporal logic task specifications.
| flags =
| filetype = PDF
| filesize = 1.2M
| tag = lwm15-hscc
| id = 2014j
}}

Cross-entropy Temporal Logic Motion Planning - Revision history

Murray at 06:07, 10 June 2016

Murray: htdb2wiki: creating page for 2014j_lwm15-hscc.html