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

arXiv: https://arxiv.org/abs/2010.07429 · HTML: https://mlsanity.com/paper/2010.07429/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2020.

## Built on by (most cited first, 14 of 14)

- [DPMPC-Planner: A real-time UAV trajectory planning framework for complex static environments with dynamic obstacles](https://mlsanity.com/paper/2109.07024/) (2021)
- [Vision-aided UAV navigation and dynamic obstacle avoidance using gradient-based B-spline trajectory optimization](https://mlsanity.com/paper/2209.07003/) (2022)
- [A real-time dynamic obstacle tracking and mapping system for UAV navigation and collision avoidance with an RGB-D camera](https://mlsanity.com/paper/2209.08258/) (2022)
- [A vision-based autonomous UAV inspection framework for unknown tunnel construction sites with dynamic obstacles](https://mlsanity.com/paper/2301.08422/) (2023)
- [Semantic-aware Next-Best-View for Multi-DoFs Mobile System in Search-and-Acquisition based Visual Perception](https://mlsanity.com/paper/2404.16507/) (2024)
- [HEADER: Hierarchical Robot Exploration via Attention-Based Deep Reinforcement Learning with Expert-Guided Reward](https://mlsanity.com/paper/2510.15679/) (2025)
- [FARE: Fast-Slow Agentic Robotic Exploration](https://mlsanity.com/paper/2601.14681/) (2026)
- [NavRL++: A System-Level Framework for Improving Sim-to-Real Transfer in Reinforcement Learning-Based Robot Navigation](https://mlsanity.com/paper/2605.15559/) (2026)
- [An Efficient Deep Learning Approach Using Improved Generative Adversarial Networks for Incomplete Information Completion of Self-driving](https://mlsanity.com/paper/2109.02629/) (2021)
- [UAV 3-D path planning based on MOEA/D with adaptive areal weight adjustment](https://mlsanity.com/paper/2308.10307/) (2023)
- [Autonomous 3D Exploration in Large-Scale Environments with Dynamic Obstacles](https://mlsanity.com/paper/2310.17977/) (2023)
- [OTO Planner: An Efficient Only Travelling Once Exploration Planner for Complex and Unknown Environments](https://mlsanity.com/paper/2406.07294/) (2024)
- [HQC-NBV: A Hybrid Quantum-Classical View Planning Approach](https://mlsanity.com/paper/2505.05212/) (2025)
- [Unified Linear Parametric Map Modeling and Perception-aware Trajectory Planning for Mobile Robotics](https://mlsanity.com/paper/2507.09340/) (2025)

## Abstract (arXiv)

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.