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We consider the problem of online trajectory design under time-varying environments. We formulate the general trajectory optimization problem within the framework of time-varying constrained convex optimization and propose a novel version of online gradient ascent algorithm that guarantees sublinear offline regret up to the pathlength of the offline solution, cumulative gradient and error in gradient variations.
