SPARCAS：一种分散、真实的多Agent无冲突路径发现机制
SPARCAS: A Decentralized, Truthful Multi-Agent Collision-free Path Finding Mechanism
摘要：
我们提出了一个分散的碰撞避免机制，一组独立控制的机器人在一个共享的工作空间。现有的算法实现多机器人碰撞避免（a）在一个集中的设置，或（b）在一个分散的设置与协作机器人。我们专注于在分散的环境中与竞争机器人的设置，在那里机器人可能会策略性地透露他们的信息以获得优先级。我们提出的机制SPARCAS在这种情况下，使用机制设计的原则，确保真实的机器人的私人信息的启示，并提供本地有效的移动的机器人。它没有碰撞和死锁，并处理机器人的动态到达。在实践中，这种机制很好地扩展了大量的机器人，其中最佳的避免碰撞的路径查找算法（M*）不缩放。然而，SPARCAS并没有过多地损害路径最优性。我们的机制按照机器人的“真正”更高需求的顺序对它们进行优先级排序，但支付更高的费用。它使用货币转移，与机器人收到的价值相比，这是足够小的。
英文摘要：
We propose a decentralized collision-avoidance mechanism for a group of independently controlled robots moving on a shared workspace. Existing algorithms achieve multi-robot collision avoidance either (a) in a centralized setting, or (b) in a decentralized setting with collaborative robots. We focus on the setting with competitive robots in a decentralized environment, where robots may strategically reveal their information to get prioritized. We propose the mechanism SPARCAS in this setting that, using principles of mechanism design, ensures truthful revelation of the robots' private information and provides locally efficient movement of the robots. It is free from collisions and deadlocks, and handles a dynamic arrival of robots. In practice, this mechanism scales well for a large number of robots where the optimal collision-avoiding path-finding algorithm (M*) does not scale. Yet, SPARCAS does not compromise the path optimality too much. Our mechanism prioritizes the robots in the order of their `true' higher needs, but for a higher payment. It uses monetary transfers which is small enough compared to the value received by the robots.
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