# SMART-3D: Three-Dimensional Self-Morphing Adaptive Replanning Tree

arXiv: https://arxiv.org/abs/2509.16812 · HTML: https://mlsanity.com/paper/2509.16812/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 20 Sep 2025.

## Builds on (most cited first, 8 of 8)

- [CARE: Cooperative Autonomy for Resilience and Efficiency of Robot Teams for Complete Coverage of Unknown Environments under Robot Failures](https://mlsanity.com/paper/1905.12191/) (2019)
- [ε^*+: An Online Coverage Path Planning Algorithm for Energy-constrained Autonomous Vehicles](https://mlsanity.com/paper/2008.13041/) (2020)
- [CT-CPP: Coverage Path Planning for 3D Terrain Reconstruction Using Dynamic Coverage Trees](https://mlsanity.com/paper/2010.09231/) (2020)
- [SMART: Self-Morphing Adaptive Replanning Tree](https://mlsanity.com/paper/2305.06487/) (2023)
- [Rapid Path Planning for Dubins Vehicles under Environmental Currents](https://mlsanity.com/paper/1909.04105/) (2019)
- [Generalized Multi-Speed Dubins Motion Model](https://mlsanity.com/paper/2402.01122/) (2024)
- [C*: A Coverage Path Planning Algorithm for Unknown Environments using Rapidly Covering Graphs](https://mlsanity.com/paper/2505.13782/) (2025)
- [SMART-OC: A Real-time Time-risk Optimal Replanning Algorithm for Dynamic Obstacles and Spatio-temporally Varying Currents](https://mlsanity.com/paper/2508.09508/) (2025)

## Abstract (arXiv)

This paper presents SMART-3D, an extension of the SMART algorithm to 3D environments. SMART-3D is a tree-based adaptive replanning algorithm for dynamic environments with fast moving obstacles. SMART-3D morphs the underlying tree to find a new path in real-time whenever the current path is blocked by obstacles. SMART-3D removed the grid decomposition requirement of the SMART algorithm by replacing the concept of hot-spots with that of hot-nodes, thus making it computationally efficient and scalable to 3D environments. The hot-nodes are nodes which allow for efficient reconnections to morph the existing tree to find a new safe and reliable path. The performance of SMART-3D is evaluated by extensive simulations in 2D and 3D environments populated with randomly moving dynamic obstacles. The results show that SMART-3D achieves high success rates and low replanning times, thus highlighting its suitability for real-time onboard applications.