Motion planning for autonomous robots in tight, interaction-rich, and mixed human-robot environments is challenging. State-of-the-art methods typically separate prediction and planning, predicting other agents' trajectories first and then planning the ego agent's motion in the remaining free space. However, agents' lack of awareness of their influence on others can lead to the freezing robot problem. We build upon Interaction-Aware Model Predictive Path Integral (IA-MPPI) control and combine it with learning-based trajectory predictions, thereby relaxing its reliance on communicated short-term goals for other agents. We apply this framework to Autonomous Surface Vessels (ASVs) navigating urban canals. By generating an artificial dataset in real sections of Amsterdam's canals, adapting and training a prediction model for our domain, and proposing heuristics to extract local goals, we enable effective cooperation in planning. Our approach improves autonomous robot navigation in complex, crowded environments, with potential implications for multi-agent systems and human-robot interaction.

翻译：在紧凑、交互密集且人机混合的环境中，自主机器人的运动规划极具挑战性。现有方法通常将预测与规划分离，即先预测其他智能体的轨迹，再在剩余自由空间中规划本体的运动。然而，由于智能体缺乏对自身影响他人的认知，可能导致"机器人冻结问题"。本研究在交互感知模型预测路径积分（IA-MPPI）控制基础上，结合基于学习的轨迹预测方法，从而降低了对其他智能体通信短周期目标的依赖性。我们将该框架应用于城市运河航行的自主水面船只（ASVs）。通过在阿姆斯特丹运河真实航段生成人工数据集、适配并训练领域专用预测模型，以及提出提取局部目标的启发式方法，实现了规划中的有效协作。该方法提升了自主机器人在复杂拥挤环境中的导航能力，对多智能体系统与人机交互具有潜在应用价值。