AI 中文总结
提出控制器无关的图注意力网络CollisionGAT，为多智能体运动提供单步碰撞筛查，可集成于任意控制器，并通过精确几何检查训练与审计。
AI 中文摘要
在机器人团队移动之前，每个提议的步骤都必须检查与其他机器人和障碍物的碰撞。我们提出了CollisionGAT，一种图注意力网络，它读取移动智能体的当前和提议状态以及局部相关的静态障碍物，并为每个移动智能体返回一个碰撞风险评分。任何控制器都可以使用这些评分来接受、修复、重新规划或推迟提议的步骤。我们将CollisionGAT安装在连续路径跟踪控制器和GATeD（一种使用类型化否决来更新其规划图的障碍盲D* Lite规划器）上。精确的几何检查提供训练标签，并独立审计每个执行的步骤。
英文摘要
Before a team of robots moves, each proposed step must be checked for collisions with other robots and with obstacles. We present CollisionGAT, a graph-attention network that reads the current and proposed states of moving agents together with locally relevant stationary obstacles and returns one collision-risk score per moving agent. Any controller can use these scores to accept, repair, replan, or postpone a proposed step. We mount CollisionGAT on a continuous path-following controller and on GATeD, an obstacle-blind D* Lite planner that uses typed vetoes to update its planning graphs. Exact geometric checks supply the training labels and independently audit every executed step.
Comments5 pages, 4 figures, 1 table, 2 algorithms. Accepted to the 2026 IEEE MIT Undergraduate Research Technology Conference (URTC)
