Swarms of autonomous agents, through their decentralized and robust nature, show great promise as a future solution to the myriad missions of business, military, and humanitarian relief. The diverse nature of mission sets creates the need for swarm algorithms to be deployed on a variety of hardware platforms. Swarms are currently viable on platforms where collisions between agents are harmless, but on many platforms collisions are prohibited since they would damage the agents involved. The available literature typically assumes that collisions can be avoided by adding a collision avoidance algorithm on top of an existing swarm behavior. Through an illustrative example in our experience replicating a particular behavior, we show that this can be difficult to achieve since the swarm behavior can be disrupted by the collision avoidance. We introduce metrics quantifying the level of disruption in our swarm behavior and propose a technique that is able to assist in tuning the collision avoidance algorithm such that the goal behavior is achieved as best as possible while collisions are avoided. We validate our results through simulation.
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