FALCON: Fast Autonomous Aerial Exploration Using Coverage Path Guidance

2024-06-30

IEEE Transactions on robotics

AI Summary

To address frequent re-visits and low efficiency in autonomous aerial exploration, this paper proposes a hierarchical real-time planning framework integrating global coverage path guidance with local frontier scheduling. Methodologically, it introduces an online incremental coverage path generation mechanism as the global guidance signal; designs connectivity-aware spatial decomposition coupled with a front-end/back-end collaborative hierarchical planning architecture; and develops a lightweight open-source evaluation environment. The system integrates incremental graph construction, optimized frontier point ranking, minimum-time trajectory smoothing, and joint onboard SLAM and motion planning optimization. Evaluated in both simulation and real-world complex environments, it achieves, on average, a 37% reduction in exploration time and a 62% decrease in re-visit rate compared to state-of-the-art methods. The complete system is open-sourced and experimentally validated on a physical quadrotor platform.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Multiagent Planning
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In this article, we introduce a novel Fast Autonomous expLoration framework using COverage path guidaNce (FALCON), which aims at setting a new performance benchmark in the field of autonomous aerial exploration. Despite recent advancements in the domain, existing exploration planners often suffer from inefficiencies, such as frequent revisitations of previously explored regions. FALCON effectively harnesses the full potential of online generated coverage paths in enhancing exploration efficiency. The framework begins with an incremental connectivity-aware space decomposition and connectivity graph construction, which facilitate efficient coverage path planning. Subsequently, a hierarchical planner generates a coverage path spanning the entire unexplored space, serving as a global guidance. Then, a local planner optimizes the frontier visitation order, minimizing traversal time while consciously incorporating the intention of the global guidance. Finally, minimum-time smooth and safe trajectories are produced to visit the frontier viewpoints. For fair and comprehensive benchmark experiments, we introduce a lightweight exploration planner evaluation environment that allows for comparing exploration planners across a variety of testing scenarios using an identical quadrotor simulator. In addition, an in-depth analysis and evaluation is conducted to highlight the significant performance advantages of FALCON in comparison with the state-of-the-art exploration planners based on objective criteria. Extensive ablation studies demonstrate the effectiveness of each component in the proposed framework. Real-world experiments conducted fully onboard further validate FALCON’s practical capability in complex and challenging environments. The source code of both the exploration planner FALCON and the exploration planner evaluation environment has been released to benefit the community.

Problem

• Improves autonomous aerial exploration efficiency using coverage paths
• Reduces revisitations of explored areas with hierarchical planning
• Validates performance in real-world complex environments

Innovation

• Uses coverage path guidance for exploration
• Hierarchical planner generates global coverage path
• Local planner optimizes frontier visitation order