Deferred-Decision Trajectory Optimization

TL;DR

This paper introduces DDTO, a trajectory optimization framework that enhances resilience to uncertainties by ensuring candidate target reachability over time, using convex optimization techniques demonstrated on quadrotor motion planning.

Contribution

The paper develops a novel constrained reachability formulation and its equivalence to cardinality minimization, enabling efficient trajectory planning under uncertainties.

Findings

Effective trajectory planning with resilience to uncertainties.

Demonstrated approach on real-world quadrotor applications.

Provides a theoretically sound optimization framework.

Abstract

We present DDTO--deferred-decision trajectory optimization--a framework for trajectory generation with resilience to unmodeled uncertainties and contingencies. The key idea is to ensure that a collection of candidate targets is reachable for as long as possible while satisfying constraints, which provides time to quantify the uncertainties. We propose optimization-based constrained reachability formulations and construct equivalent cardinality minimization problems, which then inform the design of computationally tractable and efficient solution methods that leverage state-of-the-art convex solvers and sequential convex programming (SCP) algorithms. The goal of establishing the equivalence between constrained reachability and cardinality minimization is to provide theoretically-sound underpinnings for the proposed solution methods. We demonstrate the solution methods on real-world optimal control applications encountered in quadrotor motion planning.