Equipping autonomous robots with the ability to navigate safely and efficiently around humans is a crucial step toward achieving trusted robot autonomy. However, generating robot plans while ensuring safety in dynamic multi-agent environments remains a key challenge. Building upon recent work on leveraging deep generative models for robot planning in static environments, this paper proposes CoBL-Diffusion, a novel diffusion-based safe robot planner for dynamic environments. CoBL-Diffusion uses Control Barrier and Lyapunov functions to guide the denoising process of a diffusion model, iteratively refining the robot control sequence to satisfy the safety and stability constraints. We demonstrate the effectiveness of the proposed model using two settings: a synthetic single-agent environment and a real-world pedestrian dataset. Our results show that CoBL-Diffusion generates smooth trajectories that enable the robot to reach goal locations while maintaining a low collision rate with dynamic obstacles.

翻译：赋予自主机器人在人类周围安全高效导航的能力，是实现可信赖机器人自主性的关键一步。然而，在动态多智能体环境中生成机器人规划的同时确保安全性，仍然是一个关键挑战。基于近期利用深度生成模型在静态环境中进行机器人规划的研究，本文提出了CoBL-Diffusion，一种新颖的、基于扩散模型的动态环境安全机器人规划器。CoBL-Diffusion利用控制障碍函数和Lyapunov函数来引导扩散模型的去噪过程，迭代地优化机器人控制序列以满足安全性和稳定性约束。我们通过两种设置验证了所提模型的有效性：一个合成的单智能体环境和一个真实世界的行人数据集。我们的结果表明，CoBL-Diffusion能够生成平滑的轨迹，使机器人能够到达目标位置，同时保持与动态障碍物的低碰撞率。