# dRRT*: Scalable and Informed Asymptotically-Optimal Multi-Robot Motion Planning

arXiv: https://arxiv.org/abs/1903.00994 · HTML: https://mlsanity.com/paper/1903.00994/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2019.

## Built on by (most cited first, 15 of 15)

- [Improving LaCAM for Scalable Eventually Optimal Multi-Agent Pathfinding](https://mlsanity.com/paper/2305.03632/) (2023)
- [Discrete-Guided Diffusion for Scalable and Safe Multi-Robot Motion Planning](https://mlsanity.com/paper/2508.20095/) (2025)
- [CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces](https://mlsanity.com/paper/2201.09467/) (2022)
- [Loosely Synchronized Search for Multi-agent Path Finding with Asynchronous Actions](https://mlsanity.com/paper/2103.04516/) (2021)
- [Accelerating Search-Based Planning for Multi-Robot Manipulation by Leveraging Online-Generated Experiences](https://mlsanity.com/paper/2404.00143/) (2024)
- [Hypergraph-based Multi-Robot Task and Motion Planning](https://mlsanity.com/paper/2210.04333/) (2022)
- [SwarmDiff: Swarm Robotic Trajectory Planning in Cluttered Environments via Diffusion Transformer](https://mlsanity.com/paper/2505.15679/) (2025)
- [Systematic Comparison of Path Planning Algorithms using PathBench](https://mlsanity.com/paper/2203.03092/) (2022)
- [Scalable Multi-robot Motion Planning for Congested Environments With Topological Guidance](https://mlsanity.com/paper/2210.07141/) (2022)
- [Factorization of Multi-Agent Sampling-Based Motion Planning](https://mlsanity.com/paper/2304.00342/) (2023)
- [Pathfinding with Lazy Successor Generation](https://mlsanity.com/paper/2408.15443/) (2024)
- [A Hybrid Evolutionary Approach for Multi Robot Coordinated Planning at Intersections](https://mlsanity.com/paper/2412.01082/) (2024)
- [ScheduleStream: Temporal Planning with Samplers for GPU-Accelerated Multi-Arm Task and Motion Planning & Scheduling](https://mlsanity.com/paper/2511.04758/) (2025)
- [GRACE: A Unified 2D Multi-Robot Path Planning Simulator & Benchmark for Grid, Roadmap, And Continuous Environments](https://mlsanity.com/paper/2603.10858/) (2026)
- [Conflict-Based Lazy Search for Fast Multi-Manipulator Planning](https://mlsanity.com/paper/2607.04124/) (2026)

## Abstract (arXiv)

Many exciting robotic applications require multiple robots with many degrees of freedom, such as manipulators, to coordinate their motion in a shared workspace. Discovering high-quality paths in such scenarios can be achieved, in principle, by exploring the composite space of all robots. Sampling-based planners do so by building a roadmap or a tree data structure in the corresponding configuration space and can achieve asymptotic optimality. The hardness of motion planning, however, renders the explicit construction of such structures in the composite space of multiple robots impractical. This work proposes a scalable solution for such coupled multi-robot problems, which provides desirable path-quality guarantees and is also computationally efficient. In particular, the proposed drrtstar is an informed, asymptotically-optimal extension of a prior sampling-based multi-robot motion planner, drrt. The prior approach introduced the idea of building roadmaps for each robot and implicitly searching the tensor product of these structures in the composite space. This work identifies the conditions for convergence to optimal paths in multi-robot problems, which the prior method was not achieving. Building on this analysis, drrt is first properly adapted so as to achieve the theoretical guarantees and then further extended so as to make use of effective heuristics when searching the composite space of all robots. The case where the various robots share some degrees of freedom is also studied. Evaluation in simulation indicates that the new algorithm, drrtstar, converges to high-quality paths quickly and scales to a higher number of robots where various alternatives fail. This work also demonstrates the planner's capability to solve problems involving multiple real-world robotic arms.