Autonomous UAV Exploration of Dynamic Environments via Incremental Sampling and Probabilistic Roadmap

TL;DR

This paper introduces a novel dynamic exploration planner for UAVs that leverages incremental sampling and probabilistic roadmaps to efficiently and safely explore unknown, dynamic environments in real-time.

Contribution

The proposed DEP planner combines incremental sampling with PRM to improve exploration efficiency and safety in dynamic environments, outperforming existing methods.

Findings

Outperforms benchmark planners in exploration time

Reduces path length and computational time

Safely explores dynamic environments in simulations

Abstract

Autonomous exploration requires robots to generate informative trajectories iteratively. Although sampling-based methods are highly efficient in unmanned aerial vehicle exploration, many of these methods do not effectively utilize the sampled information from the previous planning iterations, leading to redundant computation and longer exploration time. Also, few have explicitly shown their exploration ability in dynamic environments even though they can run real-time. To overcome these limitations, we propose a novel dynamic exploration planner (DEP) for exploring unknown environments using incremental sampling and Probabilistic Roadmap (PRM). In our sampling strategy, nodes are added incrementally and distributed evenly in the explored region, yielding the best viewpoints. To further shortening exploration time and ensuring safety, our planner optimizes paths locally and refine them based on the Euclidean Signed Distance Function (ESDF) map. Meanwhile, as the multi-query planner, PRM allows the proposed planner to quickly search alternative paths to avoid dynamic obstacles for safe exploration. Simulation experiments show that our method safely explores dynamic environments and outperforms the benchmark planners in terms of exploration time, path length, and computational time.