# Multi-Agent Motion Planning with B'ezier Curve Optimization under Kinodynamic Constraints

arXiv: https://arxiv.org/abs/2311.14145 · HTML: https://mlsanity.com/paper/2311.14145/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2023.

## Built on by (most cited first, 7 of 7)

- [Multi-Agent Motion Planning For Differential Drive Robots Through Stationary State Search](https://mlsanity.com/paper/2412.13359/) (2024)
- [Advancing MAPF Toward the Real World: A Scalable Multi-Agent Realistic Testbed (SMART)](https://mlsanity.com/paper/2503.04798/) (2025)
- [Optimal and Bounded Suboptimal Any-Angle Multi-agent Pathfinding](https://mlsanity.com/paper/2404.16379/) (2024)
- [Space-Time Graphs of Convex Sets for Multi-Robot Motion Planning](https://mlsanity.com/paper/2503.00583/) (2025)
- [WinkTPG: An Execution Framework for Multi-Agent Path Finding Using Temporal Reasoning](https://mlsanity.com/paper/2508.01495/) (2025)
- [From Discrete Plans to Real-World Execution: A World-Model-Driven Framework for Execution-Aware Multi-Agent Path Finding](https://mlsanity.com/paper/2511.21886/) (2025)
- [Analyzing Planner Design Trade-offs for MAPF under ADG-based Realistic Execution](https://mlsanity.com/paper/2512.09736/) (2025)

## Abstract (arXiv)

Multi-Agent Motion Planning (MAMP) is a problem that seeks collision-free dynamically-feasible trajectories for multiple moving agents in a known environment while minimizing their travel time. MAMP is closely related to the well-studied Multi-Agent Path-Finding (MAPF) problem. Recently, MAPF methods have achieved great success in finding collision-free paths for a substantial number of agents. However, those methods often overlook the kinodynamic constraints of the agents, assuming instantaneous movement, which limits their practicality and realism. In this paper, we present a three-level MAPF-based planner called PSB to address the challenges posed by MAMP. PSB fully considers the kinodynamic capability of the agents and produces solutions with smooth speed profiles that can be directly executed by the controller. Empirically, we evaluate PSB within the domains of traffic intersection coordination for autonomous vehicles and obstacle-rich grid map navigation for mobile robots. PSB shows up to 49.79% improvements in solution cost compared to existing methods.