(Georgia Institute of Technology, 2012-04-03)
Kingston, Peter
Multiagent coordination problems arise in a variety of applications, from satellite constellations and formation flight, to air traffic control and unmanned vehicle teams. We investigate the coordination of mobile agents using two kinds of approaches. In the first, which takes its inspiration from fluid dynamics and algebraic topology, control authority is split between mobile agents and a network of static infrastructure nodes - like wireless base stations or air traffic control towers - and controllers are developed that distribute their computation throughout this network. In the second, we look at networks of interconnected mechanical systems, and develop novel optimal control algorithms, which involve the computation of optimal deformations of time- and output- spaces, to achieve approximate formation tracking. Finally, we investigate algorithms that optimize these controllers to meet subjective criteria of humans.
(Georgia Institute of Technology, 2011-05)
Kunz, Tobias; Kingston, Peter; Stilman, Mike; Egerstedt, Magnus B.
We introduce and experimentally validate a novel
algorithmic model for physical human-robot interaction with
hybrid dynamics. Our computational solutions are complementary
to passive and compliant hardware. We focus on the case
where human motion can be predicted. In these cases, the
robot can select optimal motions in response to human actions
and maximize safety. By representing the domain as a Markov
Game, we enable the robot to not only react to the human but
also to construct an infinite horizon optimal policy of actions
and responses. Experimentally, we apply our model to simulated
robot sword defense. Our approach enables a simulated 7-DOF
robot arm to block known attacks in any sequence. We generate
optimized blocks and apply game theoretic tools to choose the
best action for the defender in the presence of an intelligent
adversary.