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Safe corridor-based Trajectory Optimization (TO) presents an appealingapproach for collision-free path planning of autonomous robots, offering globaloptimality through its convex formulation. The safe corridor is constructedbased on the perceived map, however, the non-ideal perception inducesuncertainty, which is rarely considered in trajectory generation. In thispaper, we propose Distributionally Robust Safe Corridor Constraints (DRSCCs) toconsider the uncertainty of the safe corridor. Then, we integrate DRSCCs intothe trajectory optimization framework using Bernstein basis polynomials.Theoretically, we rigorously prove that the trajectory optimization problemincorporating DRSCCs is equivalent to a computationally efficient, convexquadratic program. Compared to the nominal TO, our method enhances navigationsafety by significantly reducing the infeasible motions in presence ofuncertainty. Moreover, the proposed approach is validated through two roboticapplications, a micro Unmanned Aerial Vehicle (UAV) and a quadruped robotUnitree A1.

Wasserstein Distributionally Robust Chance Constrained Trajectory Optimization for Mobile Robots within Uncertain Safe Corridor