A Scalable Low-Cost-UAV Traffic Network (uNet)

This article proposes a new Unmanned Aerial Vehicle (UAV) operation paradigm to enable a large number of relatively low-cost UAVs to fly beyond-line-of-sight without costly sensing and communication systems or substantial human intervention in individual UAV control. Under current free-flight-like paradigm, wherein a UAV can travel along any route as long as it avoids restricted airspace and altitudes. However, this requires expensive on-board sensing and communication as well as substantial human effort in order to ensure avoidance of obstacles and collisions. The increased cost serves as an impediment to the emergence and development of broader UAV applications. The main contribution of this work is to propose the use of pre-established route network for UAV traffic management, which allows: (i) pre- mapping of obstacles along the route network to reduce the onboard sensing requirements and the associated costs for avoiding such obstacles; and (ii) use of well-developed routing algorithms to select UAV schedules that avoid conflicts. Available GPS-based navigation can be used to fly the UAV along the selected route and time schedule with relatively low added cost, which therefore, reduces the barrier to entry into new UAV-applications market. Finally, this article proposes a new decoupling scheme for conflict-free transitions between edges of the route network at each node of the route network to reduce potential conflicts between UAVs and ensuing delays. A simulation example is used to illustrate the proposed uNet approach.Comment: To be submitted to journal, 21 pages, 9 figure

Approximated Stable Inversion for Nonlinear Systems with Nonhyperbolic Internal Dynamics

A technique to achieve output tracking for nonminimum phase nonlinear systems with non- hyperbolic internal dynamics is presented. The present paper integrates stable inversion techniques (that achieve exact-tracking) with approximation techniques (that modify the internal dynamics) to circumvent the nonhyperbolicity of the internal dynamics - this nonhyperbolicity is an obstruction to applying presently available stable inversion techniques. The theory is developed for nonlinear systems and the method is applied to a two-cart with inverted-pendulum example

Guidance of Nonlinear Nonminimum-Phase Dynamic Systems

The first two years research work has advanced the inversion-based guidance theory for: (1) systems with non-hyperbolic internal dynamics; (2) systems with parameter jumps; (3) systems where a redesign of the output trajectory is desired; and (4) the generation of recovery guidance maneuvers

Output-Sampled Model Predictive Path Integral Control (o-MPPI) for Increased Efficiency

The success of the model predictive path integral control (MPPI) approach depends on the appropriate selection of the input distribution used for sampling. However, it can be challenging to select inputs that satisfy output constraints in dynamic environments. The main contribution of this paper is to propose an output-sampling-based MPPI (o-MPPI), which improves the ability of samples to satisfy output constraints and thereby increases MPPI efficiency. Comparative simulations and experiments of dynamic autonomous driving of bots around a track are provided to show that the proposed o-MPPI is more efficient and requires substantially (20-times) less number of rollouts and (4-times) smaller prediction horizon when compared with the standard MPPI for similar success rates. The supporting video for the paper can be found at https://youtu.be/snhlZj3l5CE