Human and automatic control interaction with a remotely piloted vehicle

In this dissertation, we present results from a study on the performance of humans and automatic controllers in a general remote navigation task. The remote navigation task is defined as driving a vehicle with nonholonomic kinematic constraints around obstacles toward a goal. We conducted experiments with humans and automatic controllers; in these experiments, the number and type of obstacles as well as the feedback delay was varied. Humans showed significantly more robust performance compared to that of a receding horizon controller. Using the human data, we then train a new human-like receding horizon controller which provides goal convergence when there is no uncertainty. We show that paths produced by the trained human-like controller are similar to human paths and that the trained controller improves robustness compared to the original receding horizon controller. We also show the impact of feedback delay on five metrics of human operator performance and for each metric characterize the impact as linear or superlinear. Finally we propose a human-inspired strategy for the automatic controller to robustly handle feedback delay.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Mechanical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Burns, Chad R.

Contributors dc:contributor

• Stipanović, Dušan M.
• Wang, Ranxiao F.
• Hovakimyan, Naira
• Dullerud, Geir E.

• remote navigation
• human obstacle avoidance
• automatic obstacle avoidance
• receding horizon control
• learning human behavior
• time delay

Statement dc:rights

• Copyright 2011 Chad Raymond Burns

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/26288

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/26288