Shortest walks in graphs of convex sets, guided by SDP-computed cost-to-go lower bounds, provide a unified approximate planner for robot motion, skill chaining, and hybrid control.
Strong Formulations for Hybrid System Control
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abstract
We study the mixed-integer quadratic programming formulation of an $n$-period hybrid control problem with a convex quadratic cost function and linear dynamics. We first give the convex hull description of the single-period, two-mode problem in the original variable space through two new classes of valid cuts. These cuts are then generalized to the single-period, multi-mode, multi-dimensional case and applied to solve the general $n$-period hybrid control problem. Computational experiments demonstrate the effectiveness of the proposed strong formulations derived through the cut generation process in the original variable space. These formulations yield a substantial reduction in computational effort for synthetic test instances and instances from the energy management problem of a power-split hybrid electric vehicle.
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Mixed Discrete and Continuous Planning using Shortest Walks in Graphs of Convex Sets
Shortest walks in graphs of convex sets, guided by SDP-computed cost-to-go lower bounds, provide a unified approximate planner for robot motion, skill chaining, and hybrid control.