SICNav-Diffusion：基于扩散轨迹预测的安全交互式人群导航
SICNav-Diffusion: Safe and Interactive Crowd Navigation with Diffusion Trajectory Predictions
AI总结：
本文提出SICNav-Diffusion方法，将扩散模型生成的多模态人类轨迹预测集成到双层MPC框架中，同时实现机器人规划与预测安全细化，在ETH/UCY基准及仿真和真实机器人实验中验证了安全高效的人群导航性能。
摘要：
为了在人群中无碰撞地导航，机器人必须通过预测人类的未来运动并做出相应反应来与人类互动。虽然基于学习的预测模型在生成可能的人类轨迹预测方面已显示出成功，但将这些随机模型集成到机器人控制器中面临若干挑战。控制器需要考虑规划出的机器人运动与人类预测之间的交互耦合，同时确保预测和机器人动作都是安全的（即无碰撞）。为了解决这些挑战，我们提出了一种适用于单机器人多人类环境的滚动时域人群导航方法。我们首先提出一个扩散模型来生成场景中所有人类的联合轨迹预测。然后，我们将这些多模态预测纳入一个SICNav双层MPC问题中，该问题同时求解机器人规划（上层），并作为安全过滤器来细化预测以实现无碰撞（下层）。将规划与预测细化结合到一个双层问题中，确保了机器人规划与人类预测之间的耦合。我们在常用的ETH/UCY基准上验证了扩散模型的开环轨迹预测性能，并在仿真和大量真实机器人实验中评估了机器人导航方法的闭环性能，展示了安全、高效和反应灵敏的机器人运动。
英文摘要：
To navigate crowds without collisions, robots must interact with humans by forecasting their future motion and reacting accordingly. While learning-based prediction models have shown success in generating likely human trajectory predictions, integrating these stochastic models into a robot controller presents several challenges. The controller needs to account for interactive coupling between planned robot motion and human predictions while ensuring both predictions and robot actions are safe (i.e. collision-free). To address these challenges, we present a receding horizon crowd navigation method for single-robot multi-human environments. We first propose a diffusion model to generate joint trajectory predictions for all humans in the scene. We then incorporate these multi-modal predictions into a SICNav Bilevel MPC problem that simultaneously solves for a robot plan (upper-level) and acts as a safety filter to refine the predictions for non-collision (lower-level). Combining planning and prediction refinement into one bilevel problem ensures that the robot plan and human predictions are coupled. We validate the open-loop trajectory prediction performance of our diffusion model on the commonly used ETH/UCY benchmark and evaluate the closed-loop performance of our robot navigation method in simulation and extensive real-robot experiments demonstrating safe, efficient, and reactive robot motion.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
