Robust Real-time UAV Replanning Using Guided Gradient-based Optimization and Topological Paths
This addresses safety and navigation issues in real-time UAV replanning, representing an incremental improvement over existing gradient-based methods.
The paper tackles the problem of local minima in gradient-based trajectory optimization for UAV replanning by proposing a method that combines guided optimization and topological path searching, resulting in improved replanning success rates and optimality compared to state-of-the-art methods.
Gradient-based trajectory optimization (GTO) has gained wide popularity for quadrotor trajectory replanning. However, it suffers from local minima, which is not only fatal to safety but also unfavorable for smooth navigation. In this paper, we propose a replanning method based on GTO addressing this issue systematically. A path-guided optimization (PGO) approach is devised to tackle infeasible local minima, which improves the replanning success rate significantly. A topological path searching algorithm is developed to capture a collection of distinct useful paths in 3-D environments, each of which then guides an independent trajectory optimization. It activates a more comprehensive exploration of the solution space and output superior replanned trajectories. Benchmark evaluation shows that our method outplays state-of-the-art methods regarding replanning success rate and optimality. Challenging experiments of aggressive autonomous flight are presented to demonstrate the robustness of our method. We will release our implementation as an open-source package.