Teleoperation of heavy machinery in industry often requires operators
to be in close proximity to the plant and issue commands on a
per-actuator level using multiple joystick input devices. However,
this is non-intuitive and makes achieving desired job properties a
challenging task requiring operators to complete costly and extensive
training. Despite this, operator fatigue is common with implications
for personal safety, project timeliness, cost, and job quality. While
full automation is not yet achievable due to unpredictability and the
dynamic nature of the environment and task, shared control paradigms
allow operators to issue high-level commands in an intuitive,
task-informed control space while having the robot optimize for
achieving desired job properties.In this paper, we compare a number of
modes of teleoperation, exploring both the number of dimensions of the
control input as well as the most intuitive control spaces. Our
experimental evaluations of the performance metrics were based on
quantifying the difficulty of tasks based on the well known Fitts' law
as well as a measure of how well constraints affecting the task
performance were met. Our experiments show that higher performance is
achieved when humans submit commands in low-dimensional task spaces as
opposed to joint space manipulations.