基于点云引导、能量扩散与势场的实时自适应运动规划
Real-Time Adaptive Motion Planning via Point Cloud-Guided, Energy-Based Diffusion and Potential Fields
AI总结：
提出一种结合能量扩散模型与人工势场的运动规划框架，直接从点云处理障碍物，实现复杂环境下的实时轨迹生成与动态避障，在部分可观测追逃场景中表现有效。
摘要：
受追逃问题启发，我们提出了一种结合能量扩散模型与人工势场的运动规划框架，用于在复杂环境中实现鲁棒的实时轨迹生成。该方法直接从点云处理障碍物信息，无需完整几何表示即可高效规划。框架采用无分类器引导训练，并在采样过程中集成局部势场以增强避障能力。在动态场景中，系统利用扩散模型生成初始轨迹，并通过基于势场的自适应机制持续优化，在部分追捕者可观测的追逃场景中展现出有效性能。
英文摘要：
Motivated by the problem of pursuit-evasion, we present a motion planning framework that combines energy-based diffusion models with artificial potential fields for robust real time trajectory generation in complex environments. Our approach processes obstacle information directly from point clouds, enabling efficient planning without requiring complete geometric representations. The framework employs classifier-free guidance training and integrates local potential fields during sampling to enhance obstacle avoidance. In dynamic scenarios, the system generates initial trajectories using the diffusion model and continuously refines them through potential field-based adaptation, demonstrating effective performance in pursuit-evasion scenarios with partial pursuer observability.
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。
