  Practical deployments of coordinated fleets of mobile robots in different
environments have revealed the benefits of maintaining small distances between
robots, especially as they move at higher speeds. However, this is
counter-intuitive in that as speed increases, reducing the amount of space
between robots also reduces the time available to the robots to respond to
sudden motion variations in surrounding robots. However, in certain examples,
the benefits in performance due to traveling at closer distances can outweigh
the potential instability issues, for instance, autonomous trucks on highways
that optimize energy by vehicle ``drafting'' or smaller robots in cluttered
environments that need to maintain close, line of sight communication, etc. To
achieve this kind of closely coordinated fleet behavior, this work introduces a
model predictive optimal control framework that directly takes non-linear
dynamics of the vehicles in the fleet into account while planning motions for
each robot. The robots are able to follow each other closely at high speeds by
proactively making predictions and reactively biasing their responses based on
state information from the adjacent robots. This control framework is naturally
decentralized and, as such, is able to apply to an arbitrary number of robots
without any additional computational burden. We show that our approach is able
to achieve lower inter-robot distances at higher speeds compared to existing
controllers. We demonstrate the success of our approach through simulated and
hardware results on mobile ground robots.
