Robust Game-theoretic Motion Planning over Extended Time Horizons
September 1, 2026·
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0 min read
Bennet Outland
V Arya
Abstract
Introduces WOLF, an algorithm combining receding-horizon model predictive control with an open-loop differential games solver via sequential convexification to solve nonconvex, game-theoretic motion planning problems with disturbances over extended time horizons. The robustness tube is treated as a dynamic state, co-optimized with the trajectory, with sufficient conditions ensuring nominal trajectories remain feasible despite all admissible disturbances. Validated on two adversarial orbital scenarios with combined translational-attitude dynamics: a jamming game under detection probability constraints and a sun-blocking scenario.
Type
Publication
Forthcoming
Authors
Graduate Researcher
Outland is a doctoral student at the University of Colorado Boulder, Smead Department of Aerospace Engineering Sciences. His research is in differential game theory, safe multi-agent systems, and computational astrodynamics, with a focus on algorithms that are tractable enough to run onboard while providing formal guarantees.