Polymorphic Control and Trajectory Optimization of an Autonomous Ground Vehicle over Wireless Mobile Networks

Adapting existing autonomous vehicle platforms to operate in new environments with additional capabilities through redesign and hardware augmentation can be expensive and risky; an alternative approach is to cooperatively utilize remote resources available from remote entities concurrently operating in the same environment. Polymorphic Control Systems research investigates highly dynamic control structures that can automatically reconfigure across vehicles to share remote sensors, actuators, and other resources available throughout the system of vehicles. In this paper, we investigate extending this framework to a smart space environment, allowing a ground vehicle without sufficient instrumentation or available onboard resources to navigate a complex indoor environment by coordinating with concurrently operating building control system agents. We develop a complex simulation of the environment, develop polymorphic control laws that restructure the control system over multiple entities during operation, and present a trajectory generation approach concurrently addresses topological optimization and dynamic control optimization of the collaborative control structure. Our results demonstrate viability of solving the two-part polymorphic control optimization problem utilizing pseudo-optimizing solvers that trade optimality for feasibility and real-time performance, and show that control polymorphism provides robust resource sharing between agents in a smart building infrastructure.