Robust Real-time UAV Replanning Using Guided Gradient-based Optimization and Topological Paths

TL;DR

This paper introduces a novel UAV replanning method combining path-guided optimization and topological path searching to improve success rate, robustness, and trajectory quality in complex 3D environments.

Contribution

It presents a systematic approach to overcome local minima in gradient-based trajectory optimization using topological paths and path-guided optimization.

Findings

Significantly higher replanning success rate compared to state-of-the-art methods.

Improved trajectory optimality and robustness in aggressive flight scenarios.

Open-source implementation available for community use.

Abstract

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.