用扩散模型生成适配具体问题的运动基元，加速动力学路径规划。
Accelerating db-A* for Kinodynamic Motion Planning Using Diffusion
- 基于扩散模型生成动态适配的运动基元，提升规划效率。
- 在不同机器人动力学下，计算时间与解质量提升最高达30%。
- 适合需要快速高质量路径规划的复杂动态系统应用。
我们提出一种基于扩散模型生成动力学运动基元的新方法。所生成的运动轨迹通过问题特定参数进行调整，从而在不同机器人动力学（如二阶单轮车或带拖车汽车）下显著加快求解速度并提升解的质量。扩散模型在随机截取的解决方案轨迹上训练，这些轨迹由随机生成的问题实例通过动力学运动规划器求解得到。实验表明，该方法在计算时间与解质量方面均实现最高达30%的提升。
原文摘要 · Abstract (English)
We present a novel approach for generating motion primitives for kinodynamic motion planning using diffusion models. The motions generated by our approach are adapted to each problem instance by utilizing problem-specific parameters, allowing for finding solutions faster and of better quality. The diffusion models used in our approach are trained on randomly cut solution trajectories. These trajectories are created by solving randomly generated problem instances with a kinodynamic motion planner. Experimental results show significant improvements up to 30 percent in both computation time and solution quality across varying robot dynamics such as second-order unicycle or car with trailer.
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