针对差速机器人运动规划，提出三级框架提升真实性和效率。
Multi-Agent Motion Planning For Differential Drive Robots Through Stationary State Search
- 结合地图匹配与静止状态搜索生成符合物理约束的路径
- 在仓库场景中吞吐量提升最高达400%
- 适合需要长期运行的多机器人系统应用
多智能体运动规划在交通管理、机场调度和仓储自动化等领域有广泛应用。许多场景中使用差速驱动机器人，其运动受速度与加速度限制，仅能原地旋转或沿当前朝向移动。现有基于多智能体路径寻找（MAPF）的方法常采用简化动力学模型，影响实际可行性与真实性。本文提出三级框架MASS，融合MAPF方法与所提出的静止状态搜索规划器，生成高质量的符合运动学动力学约束的路径。进一步引入自适应窗口机制以应对长期运行的多智能体运动规划问题。我们在单次网格地图与长期仓储场景中进行了实验验证，结果表明，本方法相比现有方法在吞吐量上最高提升400%。
原文摘要 · Abstract (English)
Multi-Agent Motion Planning (MAMP) finds various applications in fields such as traffic management, airport operations, and warehouse automation. In many of these environments, differential drive robots are commonly used. These robots have a kinodynamic model that allows only in-place rotation and movement along their current orientation, subject to speed and acceleration limits. However, existing Multi-Agent Path Finding (MAPF)-based methods often use simplified models for robot kinodynamics, which limits their practicality and realism. In this paper, we introduce a three-level framework called MASS to address these challenges. MASS combines MAPF-based methods with our proposed stationary state search planner to generate high-quality kinodynamically-feasible plans. We further extend MASS using an adaptive window mechanism to address the lifelong MAMP problem. Empirically, we tested our methods on the single-shot grid map domain and the lifelong warehouse domain. Our method shows up to 400% improvements in terms of throughput compared to existing methods.
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