Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models
This work addresses the problem of coordinating multiple robots in shared spaces, representing an incremental improvement by combining diffusion models with optimization for better constraint handling.
The paper tackles the challenge of applying diffusion models to multi-robot motion planning by integrating constrained optimization into the diffusion process to enforce collision avoidance and kinematic feasibility, resulting in higher success rates and efficiency compared to existing methods.
Recent advances in diffusion models hold significant potential in robotics, enabling the generation of diverse and smooth trajectories directly from raw representations of the environment. Despite this promise, applying diffusion models to motion planning remains challenging due to their difficulty in enforcing critical constraints, such as collision avoidance and kinematic feasibility. These limitations become even more pronounced in Multi-Robot Motion Planning (MRMP), where multiple robots must coordinate in shared spaces. To address these challenges, this work proposes Simultaneous MRMP Diffusion (SMD), a novel approach integrating constrained optimization into the diffusion sampling process to produce collision-free, kinematically feasible trajectories. Additionally, the paper introduces a comprehensive MRMP benchmark to evaluate trajectory planning algorithms across scenarios with varying robot densities, obstacle complexities, and motion constraints. Experimental results show SMD consistently outperforms classical and other learning-based motion planners, achieving higher success rates and efficiency in complex multi-robot environments.