In this work, we present DiPPeR, a novel and fast 2D path planning framework for quadrupedal locomotion, leveraging diffusion-driven techniques. Our contributions include a scalable dataset of map images and corresponding end-to-end trajectories, an image-conditioned diffusion planner for mobile robots, and a training/inference pipeline employing CNNs. We validate our approach in several mazes, as well as in real-world deployment scenarios on Boston Dynamic's Spot and Unitree's Go1 robots. DiPPeR performs on average 70 times faster for trajectory generation against both search based and data driven path planning algorithms with an average of 80% consistency in producing feasible paths of various length in maps of variable size, and obstacle structure.

翻译：本文提出DiPPeR，一种利用扩散驱动技术实现四足机器人快速二维路径规划的新型框架。我们的贡献包括：可扩展的地图图像数据集及其对应的端到端轨迹、面向移动机器人的图像条件扩散规划器，以及采用卷积神经网络（CNN）的训练/推理流水线。我们在多个迷宫环境以及波士顿动力Spot和宇树Go1机器人的实际部署场景中验证了该方法。与基于搜索和数据驱动的路径规划算法相比，DiPPeR在轨迹生成上的速度平均提升70倍，且在不同尺寸和障碍结构的地图中生成可行路径（长度可调）的一致性平均达80%。