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 generator for map images and corresponding 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 23 times faster for trajectory generation against both search based and data driven path planning algorithms with an average of 87% consistency in producing feasible paths of various length in maps of variable size, and obstacle structure. Website: https://rpl-cs-ucl.github.io/DiPPeR

翻译：摘要：本文提出DiPPeR，一种新颖且快速的四足运动二维路径规划框架，该框架利用扩散驱动技术。我们的贡献包括：一个可扩展的地图图像与对应轨迹数据集生成器；一个面向移动机器人的图像条件扩散规划器；以及一个采用CNN的训练/推理流程。我们在多个迷宫环境中验证了该方法，并在波士顿动力Spot和宇树Go1机器人上进行了实际部署测试。相比基于搜索和数据驱动的路径规划算法，DiPPeR在轨迹生成上平均快23倍，且在生成不同大小地图中各种长度的可行路径时，一致性平均达到87%。网站：https://rpl-cs-ucl.github.io/DiPPeR