A Tree-based Next-best-trajectory Method for 3D UAV Exploration
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Abstract (arXiv)
This work presents a fully integrated tree-based combined exploration-planning algorithm: Exploration-RRT (ERRT). The algorithm is focused on providing real-time solutions for local exploration in a fully unknown and unstructured environment while directly incorporating exploratory behavior, robot-safe path planning, and robot actuation into the central problem. ERRT provides a complete sampling and tree-based solution for evaluating "where to go next" by considering a trade-off between maximizing information gain, and minimizing the distances travelled and the robot actuation along the path. The complete scheme is evaluated in extensive simulations, comparisons, as well as real-world field experiments in constrained and narrow subterranean and GPS-denied environments. The framework is fully ROS-integrated, straight-forward to use, and we open-source it at https://github.com/LTU-RAI/ExplorationRRT.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Jul 2024.
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Citing papers per year: 2024: 1; 2025: 2; 2026: 1.
4 papers.
- 2024Why Sample Space Matters: Keyframe Sampling Optimization for LiDAR-based Place Recognitioncited by 0
- 2025Next-Best-Trajectory Planning of Robot Manipulators for Effective Observation and Explorationcited by 0
- 2025Semantic Exploration and Dense Mapping of Complex Environments using Ground Robot with Panoramic LiDAR-Camera Fusioncited by 0
- 2026REST: Receding Horizon Explorative Steiner Tree for Zero-Shot Object-Goal Navigationcited by 0
- 2026REST: Receding Horizon Explorative Steiner Tree for Zero-Shot Object-Goal Navigation19 Mar 2026
- 2025Semantic Exploration and Dense Mapping of Complex Environments using Ground Robot with Panoramic LiDAR-Camera Fusion28 May 2025
- 2025Next-Best-Trajectory Planning of Robot Manipulators for Effective Observation and Exploration28 Mar 2025
- 2024Why Sample Space Matters: Keyframe Sampling Optimization for LiDAR-based Place Recognition3 Oct 2024