# ST-RRT*: Asymptotically-Optimal Bidirectional Motion Planning through Space-Time

arXiv: https://arxiv.org/abs/2203.02176 · HTML: https://mlsanity.com/paper/2203.02176/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2022.

## Built on by (most cited first, 6 of 6)

- [Space-Time Graphs of Convex Sets for Multi-Robot Motion Planning](https://mlsanity.com/paper/2503.00583/) (2025)
- [Multi-Agent Reinforcement Learning and Real-Time Decision-Making in Robotic Soccer for Virtual Environments](https://mlsanity.com/paper/2512.03166/) (2025)
- [Search-Based Spatiotemporal and Multi-Robot Motion Planning on Graphs of Space-Time Convex Sets](https://mlsanity.com/paper/2607.00444/) (2026)
- [MoRe-ERL: Learning Motion Residuals using Episodic Reinforcement Learning](https://mlsanity.com/paper/2508.01409/) (2025)
- [Interleaving Scheduling and Motion Planning with Incremental Learning of Symbolic Space-Time Motion Abstractions](https://mlsanity.com/paper/2603.10651/) (2026)
- [Visual-RRT: Finding Paths toward Visual-Goals via Differentiable Rendering](https://mlsanity.com/paper/2604.16388/) (2026)

## Abstract (arXiv)

We present a motion planner for planning through space-time with dynamic obstacles, velocity constraints, and unknown arrival time. Our algorithm, Space-Time RRT* (ST-RRT*), is a probabilistically complete, bidirectional motion planning algorithm, which is asymptotically optimal with respect to the shortest arrival time. We experimentally evaluate ST-RRT* in both abstract (2D disk, 8D disk in cluttered spaces, and on a narrow passage problem), and simulated robotic path planning problems (sequential planning of 8DoF mobile robots, and 7DoF robotic arms). The proposed planner outperforms RRT-Connect and RRT* on both initial solution time, and attained final solution cost. The code for ST-RRT* is available in the Open Motion Planning Library (OMPL).