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This research aims at developing path and motion planning algorithms for atethered Unmanned Aerial Vehicle (UAV) to visually assist a teleoperatedprimary robot in unstructured or confined environments. The emerging state ofthe practice for nuclear operations, bomb squad, disaster robots, and otherdomains 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 theperceptual limitations of the sensors by providing an external viewpoint.However, the benefits of using an assistant have been limited for at leastthree reasons: (1) users tend to choose suboptimal viewpoints, (2) only groundrobot assistants are considered, ignoring the rapid evolution of small unmannedaerial systems for indoor flying, (3) introducing a whole crew for the secondteleoperated robot is not cost effective, may introduce further teamworkdemands, and therefore could lead to miscommunication. This dissertationproposes to use an autonomous tethered aerial visual assistant to replace thesecondary robot and its operating crew. Along with a pre-established theory ofviewpoint quality based on affordances, this dissertation aims at defining andrepresenting robot motion risk in unstructured or confined environments. Basedon those theories, a novel high level path planning algorithm is developed toenable risk-aware planning, which balances the tradeoff between viewpointquality and motion risk in order to provide safe and trustworthy visualassistance flight. The planned flight trajectory is then realized on a tetheredUAV platform. The perception and actuation are tailored to fit the tetheredagent in the form of a low level motion suite, including a novel tether-basedlocalization model with negligible computational overhead, motion primitivesfor the tethered airframe based on position and velocity control, and twodifferent

Risk-aware Path and Motion Planning for a Tethered Aerial Visual Assistant in Unstructured or Confined Environments