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

2025-08-20

AI Summary

In complex dynamic environments, autonomous aerial vehicles often suffer from local minima and deadlock during obstacle avoidance, compromising robustness. To address this, we propose a topology-guided hierarchical trajectory planning framework. Methodologically: (1) we design a Dynamic-Enhanced Visibility Probabilistic Roadmap (DEV-PRM) to generate topologically safe paths; (2) we integrate terminal-unconstrained minimum-control polynomials (UTF-MINCO) with a Dynamic Distance Field (DDF) for spatiotemporally consistent trajectory optimization; and (3) we introduce an incremental multi-branch trajectory management mechanism enabling historical information reuse and parallel re-planning. Experimental results demonstrate that the framework achieves a 96% task success rate in highly dynamic scenarios, with millisecond-level re-planning latency. Both high-fidelity simulations and real-world flight tests validate its effectiveness and engineering practicality.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning
• Intelligent Robots: Motion and Path Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

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.

Problem

• Addresses local minima and deadlock in AAV motion planning
• Enables robust spatio-temporal obstacle avoidance in dynamic environments
• Reduces replanning time through topological decision-making framework

Innovation

• Topology-guided hierarchical planning framework
• Dynamic enhanced visible probabilistic roadmap
• Uniform terminal-free minimum control polynomial