用扩散场动态优化机器人清扫轨迹，实现均匀高效覆盖。
SHIFT Planner: Speedy Hybrid Iterative Field and Segmented Trajectory Optimization with IKD-tree for Uniform Lightweight Coverage
- 基于高斯场建模机器人动作影响，动态调整轨迹与速度。
- 结合环境脏污度等属性，生成速度最优的均匀覆盖路径。
- 适合需要自适应覆盖任务的移动机器人系统使用。
本文提出一种综合规划与导航框架，解决传统方法在语义地图构建、自适应覆盖规划、动态避障及精确轨迹跟踪方面的局限。该框架通过单目相机、IMU与GPS数据对齐，生成全景占据式局部语义地图和精准定位信息，并融合地形点云或预加载地形数据启动规划过程。提出辐射场引导的覆盖规划算法（Radiant Field-Informed Coverage Planning），利用扩散场模型根据环境属性（如脏污程度、干燥度）动态调整机器人的覆盖轨迹与速度。通过高斯场建模机器人行为的空间影响，确保在不同环境下仍能实现速度优化且均匀的覆盖轨迹。
原文摘要 · Abstract (English)
This paper introduces a comprehensive planning and navigation framework that address these limitations by integrating semantic mapping, adaptive coverage planning, dynamic obstacle avoidance and precise trajectory tracking. Our framework begins by generating panoptic occupancy local semantic maps and accurate localization information from data aligned between a monocular camera, IMU, and GPS. This information is combined with input terrain point clouds or preloaded terrain information to initialize the planning process. We propose the Radiant Field-Informed Coverage Planning algorithm, which utilizes a diffusion field model to dynamically adjust the robot's coverage trajectory and speed based on environmental attributes such as dirtiness and dryness. By modeling the spatial influence of the robot's actions using a Gaussian field, ensures a speed-optimized, uniform coverage trajectory while adapting to varying environmental conditions.
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