2,996 research outputs found
Vision and Learning for Deliberative Monocular Cluttered Flight
Cameras provide a rich source of information while being passive, cheap and
lightweight for small and medium Unmanned Aerial Vehicles (UAVs). In this work
we present the first implementation of receding horizon control, which is
widely used in ground vehicles, with monocular vision as the only sensing mode
for autonomous UAV flight in dense clutter. We make it feasible on UAVs via a
number of contributions: novel coupling of perception and control via relevant
and diverse, multiple interpretations of the scene around the robot, leveraging
recent advances in machine learning to showcase anytime budgeted cost-sensitive
feature selection, and fast non-linear regression for monocular depth
prediction. We empirically demonstrate the efficacy of our novel pipeline via
real world experiments of more than 2 kms through dense trees with a quadrotor
built from off-the-shelf parts. Moreover our pipeline is designed to combine
information from other modalities like stereo and lidar as well if available
Some Applications of Polynomial Optimization in Operations Research and Real-Time Decision Making
We demonstrate applications of algebraic techniques that optimize and certify
polynomial inequalities to problems of interest in the operations research and
transportation engineering communities. Three problems are considered: (i)
wireless coverage of targeted geographical regions with guaranteed signal
quality and minimum transmission power, (ii) computing real-time certificates
of collision avoidance for a simple model of an unmanned vehicle (UV)
navigating through a cluttered environment, and (iii) designing a nonlinear
hovering controller for a quadrotor UV, which has recently been used for load
transportation. On our smaller-scale applications, we apply the sum of squares
(SOS) relaxation and solve the underlying problems with semidefinite
programming. On the larger-scale or real-time applications, we use our recently
introduced "SDSOS Optimization" techniques which result in second order cone
programs. To the best of our knowledge, this is the first study of real-time
applications of sum of squares techniques in optimization and control. No
knowledge in dynamics and control is assumed from the reader
A High Performance Fuzzy Logic Architecture for UAV Decision Making
The majority of Unmanned Aerial Vehicles (UAVs) in operation today are not truly autonomous, but are instead reliant on a remote human pilot. A high degree of autonomy can provide many advantages in terms of cost, operational resources and safety. However, one of the challenges involved in achieving autonomy is that of replicating the reasoning and decision making capabilities of a human pilot. One candidate method for providing this decision making capability is fuzzy logic. In this role, the fuzzy system must satisfy real-time constraints, process large quantities of data and relate to large knowledge bases. Consequently, there is a need for a generic, high performance fuzzy computation platform for UAV applications. Based on Lees’ [1] original work, a high performance fuzzy processing architecture, implemented in Field Programmable Gate Arrays (FPGAs), has been developed and is shown to outclass the performance of existing fuzzy processors
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