用点云与能量扩散模型实现复杂环境实时避障路径规划
Real-Time Adaptive Motion Planning via Point Cloud-Guided, Energy-Based Diffusion and Potential Fields
- 直接处理点云数据，无需完整几何建模
- 融合势场动态优化轨迹，支持实时适应
- 适合机器人在部分观测下追逃场景应用
针对追逃问题，我们提出一种结合能量基扩散模型与人工势场的运动规划框架，可在复杂环境中实现鲁棒的实时轨迹生成。该方法直接从点云中获取障碍物信息，无需完整的几何表示即可高效规划。框架采用无分类器引导训练，并在采样过程中引入局部势场以增强避障能力。在动态场景中，系统先用扩散模型生成初始轨迹，再通过势场驱动持续优化，展现了在部分追踪者可观测条件下的有效性能。
原文摘要 · Abstract (English)
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.
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