TRUST-Planner: Topology-guided Robust Trajectory Planner for AAVs with Uncertain Obstacle Spatial-temporal Avoidance

TL;DR

TRUST-Planner is a hierarchical, topology-guided planning framework for autonomous aerial vehicles that enhances obstacle avoidance in complex dynamic environments, reducing collision risks and replanning time.

Contribution

It introduces a novel combination of DEV-PRM, UTF-MINCO, DDF, and multi-branch trajectory management for robust, efficient spatial-temporal obstacle avoidance.

Findings

Achieves 96% success rate in complex simulations

Operates with millisecond-level computation speed

Successfully validated in real-world experiments

Abstract

Despite extensive developments in motion planning of autonomous aerial vehicles (AAVs), existing frameworks faces the challenges of local minima and deadlock in complex dynamic environments, leading to increased collision risks. To address these challenges, we present TRUST-Planner, a topology-guided hierarchical planning framework for robust spatial-temporal obstacle avoidance. In the frontend, a dynamic enhanced visible probabilistic roadmap (DEV-PRM) is proposed to rapidly explore topological paths for global guidance. The backend utilizes a uniform terminal-free minimum control polynomial (UTF-MINCO) and dynamic distance field (DDF) to enable efficient predictive obstacle avoidance and fast parallel computation. Furthermore, an incremental multi-branch trajectory management framework is introduced to enable spatio-temporal topological decision-making, while efficiently leveraging historical information to reduce replanning time. Simulation results show that TRUST-Planner outperforms baseline competitors, achieving a 96\% success rate and millisecond-level computation efficiency in tested complex environments. Real-world experiments further validate the feasibility and practicality of the proposed method.