66 research outputs found
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
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