  Maximizing the utility of human-robot teams in disaster response and search
and rescue (SAR) missions remains to be a challenging problem. This is due to
the dynamic, uncertain nature of the environment and the variability in
cognitive performance of the human operators. By having an autonomous agent
share control with the operator, we can achieve near-optimal performance by
augmenting the operator's input and compensate for the factors resulting in
degraded performance. What this solution does not consider though is the human
input latency and errors caused by potential hardware failures that can occur
during task completion when operating in disaster response and SAR scenarios.
In this paper, we propose the use of blended shared control (BSC) architecture
to address these issues and investigate the architecture's performance in
constrained, dynamic environments with a differential drive robot that has
input latency and erroneous odometry feedback. We conduct a validation study
(n=12) for our control architecture and then a user study (n=14) in 2 different
environments that are unknown to both the human operator and the autonomous
agent. The results demonstrate that the BSC architecture can prevent collisions
and enhance operator performance without the need of a complete transfer of
control between the human operator and autonomous agent.
