The STL motion-planning problem is reformulated as a shortest-path problem over a graph of convex sets to generate smooth Bézier-spline trajectories satisfying logical, timing, smoothness, and velocity constraints.
Temporal logic motion planning with convex optimization via graphs of convex sets,
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A predicate-erosion framework using contraction-based probabilistic reachable tubes turns chance-constrained STL planning for stochastic nonlinear systems into deterministic trajectory optimization that achieves high-probability specification satisfaction.
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Signal Temporal Logic Motion Planning via Graphs of Convex Sets
The STL motion-planning problem is reformulated as a shortest-path problem over a graph of convex sets to generate smooth Bézier-spline trajectories satisfying logical, timing, smoothness, and velocity constraints.
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Feedback Motion Planning for Stochastic Nonlinear Systems with Signal Temporal Logic Specifications
A predicate-erosion framework using contraction-based probabilistic reachable tubes turns chance-constrained STL planning for stochastic nonlinear systems into deterministic trajectory optimization that achieves high-probability specification satisfaction.