  This research aims at developing path and motion planning algorithms for a
tethered Unmanned Aerial Vehicle (UAV) to visually assist a teleoperated
primary robot in unstructured or confined environments. The emerging state of
the practice for nuclear operations, bomb squad, disaster robots, and other
domains with novel tasks or highly occluded environments is to use two robots,
a primary and a secondary that acts as a visual assistant to overcome the
perceptual limitations of the sensors by providing an external viewpoint.
However, the benefits of using an assistant have been limited for at least
three reasons: (1) users tend to choose suboptimal viewpoints, (2) only ground
robot assistants are considered, ignoring the rapid evolution of small unmanned
aerial systems for indoor flying, (3) introducing a whole crew for the second
teleoperated robot is not cost effective, may introduce further teamwork
demands, and therefore could lead to miscommunication. This dissertation
proposes to use an autonomous tethered aerial visual assistant to replace the
secondary robot and its operating crew. Along with a pre-established theory of
viewpoint quality based on affordances, this dissertation aims at defining and
representing robot motion risk in unstructured or confined environments. Based
on those theories, a novel high level path planning algorithm is developed to
enable risk-aware planning, which balances the tradeoff between viewpoint
quality and motion risk in order to provide safe and trustworthy visual
assistance flight. The planned flight trajectory is then realized on a tethered
UAV platform. The perception and actuation are tailored to fit the tethered
agent in the form of a low level motion suite, including a novel tether-based
localization model with negligible computational overhead, motion primitives
for the tethered airframe based on position and velocity control, and two
different
