Denoising Multi-Robot Trajectories

2026-09-28

AI Summary

This study addresses the challenges of non-convexity, high dimensionality, and computational complexity in multi-robot trajectory planning by proposing a parallel sampling optimization method based on the D4orm diffusion denoising framework. The research constructs a denoising-deformation iterative optimization paradigm that integrates GPU acceleration with dynamics-aware control, enabling scalable decoupled, online receding-horizon, and distributed planning to effectively overcome the limitations of traditional numerical optimization. Simulation and real-world experiments involving unmanned aerial vehicle and ground robot swarms demonstrate that this approach significantly enhances planning speed and reliability, successfully achieving collision-free coordination for fleets comprising up to one hundred robots.

Abstract

Multi-robot trajectory planning is a fundamental problem in multi-robot coordination but remains computationally challenging due to its nonconvex, multimodal, and high-dimensional nature. This work builds upon D4orm, a dynamics-aware diffusion-denoising framework, and develops a family of planning architectures for diverse operational requirements. Unlike conventional numerical optimization methods, D4orm employs sampling-based optimization to generate solution trajectories through massively parallel sampling, leveraging modern computing architectures such as GPUs. Its diffusion-denoising structure iteratively optimizes \textit{deformations} to candidate control trajectories, providing an efficient and versatile paradigm for generating kinodynamically feasible and conflict-free trajectories. Using D4orm as the building block for advanced planners, we present a decoupled planner for improved scalability, an online receding-horizon planner with feedback control, and a distributed planner for resource-constrained settings. Evaluations with differential-drive and holonomic robots in 2D and 3D environments demonstrate that D4orm-based approaches find high-quality solutions faster and more reliably than other sampling-based optimization methods, such as MPPI, as well as a learned diffusion-model-based method. We further demonstrate zero-shot deployment on ten real quadrotors with obstacles, large-scale deconfliction with 100 simulated robots, and fully onboard distributed `lifelong'operation with six ground robots. Overall, these results establish diffusion denoising as a scalable and reliable framework for multi-robot coordination. Code and video: https://github.com/proroklab/d4orm

Problem

• Multi-robot trajectory planning
• Nonconvex optimization
• High-dimensional
• Multi-robot coordination

Innovation

• Diffusion Denoising
• Multi-Robot Trajectory Planning
• Sampling-Based Optimization
• Dynamics-Aware Framework
• Distributed Planning