We consider the problem of distributed goal conflict resolution in multi-robot systems while remaining resilient to intermittent communication losses between robots. Our proposed approach uses a spatial approximation technique called $\alpha$-shape to represent the regions that have been explored by robots followed by a $O(logn)$ algorithm that incrementally combines and shares the $\alpha$-shape information between robots along the robots’ communication tree and rapidly checks for conflicts of a robot’s selected location. We provide theoretical guarantees of the time complexity of our proposed algorithm along with experimental results with simulated and physical robots in different environments. The results show that our approach can rapidly determine conflicts between goal locations selected by multiple robots as well as reduce message loss and re-transmissions between robots. These result in more efficient inter-robot communications as well as less extraneous distance traveled by robots, as compared to a flooding-based communications approach.

我们考虑了在多机器人系统中解决分布式目标冲突的问题，同时保持了对机器人之间间歇性通信损失的抵御能力。我们提出的方法使用一种称为$\alpha$形状代表机器人已经探索过的区域，然后是 $Ø（日志ñ）$ 逐步合并并共享 $\alpha$沿着机器人的通讯树调整机器人之间的信息，并快速检查机器人所选位置的冲突。我们提供了所提出算法时间复杂度的理论保证，以及在不同环境中使用模拟和物理机器人进行实验的结果。结果表明，我们的方法可以快速确定多个机器人选择的目标位置之间的冲突，并减少消息丢失和机器人之间的重新传输。与基于泛洪的通信方法相比，这些方法可提高机器人之间的通信效率，并减少机器人的外部距离。