Robotic systems are becoming more practical in military applications. In fact, unmanned aerial vehicles (UAVs) are being used for surveillance and reconnaissance missions. Using UAVs for surveying a region of interest can increase situational awareness and decrease human casualty by allowing the operator to view the video feeds from the UAVs. However, current systems utilize complex one (or multiple) operator/one robot interfaces. In addition, human-in-the-loop models create issues because human operators tend to intervene more frequently if they do not trust the system or their expectations of the autonomy are not met. As a result, excessive or inept human intervention could negatively affect workload, situational awareness, and performance. This dissertation is aimed at allowing a single operator to efficiently manage multiple UAVs and interact effectively with higher levels of autonomy. By providing the operator with a mechanism to interact with the autonomy and aid in decision making, the operator becomes a part of the autonomous team. Contributions of this dissertation related to facilitating operator interaction with multi-robot surveillance systems include: (1) knowledge that trust is more than an understanding that the actions of an autonomous team are rational, but related to experiencing the actions' rationale; (2) a novel approach to teaming based on spatial and temporal environmental cues; and (3) the design and implementation of a testbed to measure the effect of the operator teaming with an autonomous system. Studies are used to evaluate the system and elucidate factors that affect operator trust. Results suggest that a human operator can team with multiple robots and effectively interact with higher levels of autonomy by experiencing the autonomous team's rationale using environmental cues. This approach using the spatial and temporal environmental cues was also found to promote trust, lower workload, and increase situational awareness while not degrading task performance.