In this paper, we consider the problem of a swarm traveling between two points as fast as possible in an unknown environment cluttered with obstacles. Potential applications include search-and-rescue operations where damaged environments are typical. We present swarm generalizations, called SwarmCom, SwarmBug1, and SwarmBug2, of the classical path generation algorithms Com, Bug1, and Bug2. These algorithms were developed for unknown environments and require low computational power and memory storage, thereby freeing up resources for other tasks. We show the upper bound of the worst-case travel time for the first agent in the swarm to reach the target point for SwarmBug1. For SwarmBug2, we show that the algorithm underperforms in terms of worst-case travel time compared to SwarmBug1. For SwarmCom, we show that there exists a trivial scene for which the algorithm will not halt, and it thus has no performance guarantees. Moreover, by comparing the upper bound of the travel time for SwarmBug1 with a universal lower bound for any path generation algorithm, it is shown that in the limit when the number of agents in the swarm approaches infinity, no other algorithm has strictly better worst-case performance than SwarmBug1 and the universal lower bound is tight.

翻译：本文研究了群智系统在布满障碍物的未知环境中以最快速度从两点间移动的问题。潜在应用包括典型受损环境下的搜救行动。我们提出了经典路径生成算法Com、Bug1和Bug2的群智泛化版本，分别称为SwarmCom、SwarmBug1和SwarmBug2。这些算法专为未知环境设计，对计算能力和内存存储要求极低，从而释放资源用于其他任务。我们给出了SwarmBug1中群智首台代理到达目标点最差情况旅行时间的上界。对于SwarmBug2，我们表明其最差情况旅行时间性能劣于SwarmBug1。对于SwarmCom，我们证明存在使其无法终止的平凡场景，因此该算法不具备性能保证。进一步地，通过将SwarmBug1旅行时间上界与任意路径生成算法的通用下界进行对比，发现当群智代理数量趋近于无穷时，其他算法在最差情况下的性能均无法严格优于SwarmBug1，且该通用下界是紧的。