Forgive & Forget: SelfStabilizing Swarms in Spite of Byzantine Robots In this paper, we consider the case in which a swarm of robots collaborates in a mission, where a few of the robots behave maliciously. These malicious Byzantine robots may be temporally or constantly controlled by an adversary. The scope is synchronized full information robot operations, where a robot that does not follow the programpolicy of the swarm is immediately identified and can be remembered as Byzantine. As robots may be suspected of being Byzantine due to benign temporal malfunctions, it is imperative to forgive and forget, otherwise, a robot cannot assume collaborative actions with any other robot in the swarm. Still, remembering for a while may facilitate a policy of surrounding, isolating and freezing the movement of the misbehaving robots, by several robots, allowing the rest to perform the swarm task with no intervention. We demonstrate the need to periodically forgive and forget to realize swarm several tasks including patrollingcleaning in the presence of possible Byzantine robots. The policy for achieving the task consists of blocking the movement of the Byzantine robot(s) by some of the robots, while the rest patrolclean the plane. We present and use selfstabilizing (nontwo faced) Byzantine pulse and clock synchronizations that are of independent interest.

Plasticity in Collective DecisionMaking for Robots: Creating Global Reference Frames, Detecting Dynamic Environments, and Preventing Lockins Swarm robots operate as autonomous agents and a swarm as a whole gets autonomous by its capability of collective decisionmaking. Despite intensive research on models of collective decisionmaking, the implementation in multirobot systems is still challenging. Here, we advance the state of the art by introducing more plasticity to the decisionmaking process and by increasing the scenario difficulty. Most studies on largescale multirobot decisionmaking are limited to one instance of an iterated explorationdissemination phase followed by successful and permanent convergence. We investigate a dynamic environment that requires constant collective monitoring of option qualities. Once a significant change in qualities is detected by the swarm, it has to collectively reconsider its previous decision accordingly. This is only possible by preventing lockins, a global consensus state of no return (i.e., a dominant majority of robots prevents the swarm from switching to another, possibly better option). In addition, we introduce a scenario of increased difficulty as the robots must locate themselves to assess the quality of an option. Using local communication, swarm robots propagate hopcount information throughout the swarm to form a global reference frame. We successfully validate our implementation in many swarm robot experiments concerning robustness to disruptions of the reference frame, scalability, and adaptivity to a dynamic environment.