Abstract: Problems involving coordination and control of multiple autonomous
vehicles have received a great deal of attention from the robotics and
control research community over the last few decades. This interest has
been sparked by important advances in communications (wireless
technology) and accessibility to computational power which have
provided key tools for targeting complex problems involving multiple
interacting systems. In this seminar, we will discuss the application
and theoretical development of model predictive control techniques for
the distributed control and safe coordination of autonomous vehicles
with nonholonomic dynamic models. Model predictive control is an
optimization based control approach often used for stabilizing
constrained linear and nonlinear dynamic systems. This technique is
also relevant to the control of complex systems due to its ability to
achieve desired system performance while handling hard system
constraints at the same time. In a distributed framework, the use of
model predictive control is advantageous because it enhances the
capability of each agent/vehicle to handle new and unexpected events.
These events can be potential conflicts as well as changes of strategy
to accommodate new goals. Within this context, we target the problem of
collision avoidance and safety guarantee for a group of interacting
autonomous vehicles performing individual trajectory tracking over
predefined potentially conflicting paths. In the proposed solution new
model predictive control stability results are presented and combined
with hybrid system tools for addressing the multi-vehicle safe
coordination problem.
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