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Deferred-Decision Trajectory Optimization (DDTO) is a novel deterministic framework for dynamically feasible trajectory generation that is resilient to unmodeled uncertainties and contingencies. The key idea is to retain reachability to a set of candidate targets for as long as possible while satisfying input, state, and system dynamics constraints, without significantly compromising the optimality of the trajectory's cumulative cost, such as power or fuel consumption. This provides the vehicle time to gather new information from sensors or quantify operational uncertainties.
A Mars landing example, where deferred decision-making is illustrated. The black trajectory segments keep a collection of candidate landing sites reachable (colored nodes). Each black node serves as a decision point beyond which reachability to one of the landing sites is lost. While the spacecraft follows the black segment, it can learn more about the terrain to determine the most viable landing site. The background image (taken by the Perseverance rover) shows examples of previously unknown irregularities on the Martian surface that could potentially make landing sites infeasible.
To realize DDTO, AIAA SciTech 22 proposes a heuristic approach based on quasiconvex optimization. On the other hand, arXiv 25 (provisionally accepted to Automatica) proposes optimization-based constrained reachability formulations and constructs equivalent cardinality-minimization problems, informing the design of computationally tractable and efficient solution methods that leverage state-of-the-art convex solvers and sequential convex programming algorithms.
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