898 research outputs found
Modeling and System Identification using Extended Kalman Filter for a Quadrotor System
Quadrotor has emerged as a popular testbed for Unmanned Aerial Vehicle (UAV) research due
to its simplicity in construction and maintenance, and its vertical take-off, landing and hovering capabilities.
It is a flying rotorcraft that has four lift-generating propellers; two of the propellers rotate clockwise and the
other two rotate counter-clockwise. This paper presents modeling and system identification for autostabilization
of a quadrotor system through the implementation of Extended Kalman Filter (EKF). EKF has
known to be typical estimation technique used to estimate the state vectors and parameters of nonlinear
dynamical systems. In this paper, two main processes are highlighted; dynamic modeling of the quadrotor
and the implementation of EKF algorithms. The aim is to obtain a more accurate dynamic model by identify
and estimate the needed parameters for the quadrotor. The obtained results demonstrate the performances of
EKF based on the flight test applied to the quadrotor system
A modular software architecture for UAVs
There have been several attempts to create scalable and hardware independent software architectures for Unmanned Aerial Vehicles (UAV). In this work, we propose an onboard architecture for UAVs where hardware abstraction, data storage and communication between modules are efficiently maintained. All processing and software development is done on the UAV while state and mission status of the UAV is monitored from a ground station. The architecture also allows rapid development of mission-specific third party applications on the vehicle with the help of the core module
Data-Driven Meets Navigation: Concepts, Models, and Experimental Validation
The purpose of navigation is to determine the position, velocity, and
orientation of manned and autonomous platforms, humans, and animals. Obtaining
accurate navigation commonly requires fusion between several sensors, such as
inertial sensors and global navigation satellite systems, in a model-based,
nonlinear estimation framework. Recently, data-driven approaches applied in
various fields show state-of-the-art performance, compared to model-based
methods. In this paper we review multidisciplinary, data-driven based
navigation algorithms developed and experimentally proven at the Autonomous
Navigation and Sensor Fusion Lab (ANSFL) including algorithms suitable for
human and animal applications, varied autonomous platforms, and multi-purpose
navigation and fusion approachesComment: 22 pages, 13 figure
Master of Science
thesisThis thesis details the development of the Algorithmic Robotics Laboratory, its experimental software environment, and a case study featuring a novel hardware validation of optimal reciprocal collision avoidance. We constructed a robotics laboratory in both software and hardware in which to perform our experiments. This lab features a netted flying volume with motion capture and two custom quadrotors. Also, two experimental software architectures are developed for actuating both ground and aerial robots within a Linux Robot Operating System environment. The first of the frameworks is based upon a single finite state machine program which managed each aspect of the experiment. Concerns about the complexity and reconfigurability of the finite state machine prompted the development of a second framework. This final framework is a multimodal structure featuring programs which focus on these specific functions: State Estimation, Robot Drivers, Experimental Controllers, Inputs, Human Robot Interaction, and a program tailored to the specifics of the algorithm tested in the experiment. These modular frameworks were used to fulfill the mission of the Algorithmic Robotics Lab, in that they were developed to validate robotics algorithms in experiments that were previously only shown in simulation. A case study into collision avoidance was used to mark the foundation of the laboratory through the proving of an optimal reciprocal collision avoidance algorithm for the first time in hardware. In the case study, two human-controlled quadrotors were maliciously flown in colliding trajectories. Optimal reciprocal collision avoidance was demonstrated for the first time on completely independent agents with local sensing. The algorithm was shown to be robust to violations of its inherent assumptions about the dynamics of agents and the ability for those agents to sense imminent collisions. These experiments, in addition to the mathematical foundation of exponential convergence, submits th a t optimal reciprocal collision avoidance is a viable method for holonomic robots in both 2-D and 3-D with noisy sensing. A basis for the idea of reciprocal dance, a motion often seen in human collision avoidance, is also suggested in demonstration to be a product of uncertainty about the state of incoming agents. In the more than one hundred tests conducted in multiple environments, no midair collisions were ever produced
Event Blob Tracking: An Asynchronous Real-Time Algorithm
Event-based cameras have become increasingly popular for tracking fast-moving
objects due to their high temporal resolution, low latency, and high dynamic
range. In this paper, we propose a novel algorithm for tracking event blobs
using raw events asynchronously in real time. We introduce the concept of an
event blob as a spatio-temporal likelihood of event occurrence where the
conditional spatial likelihood is blob-like. Many real-world objects generate
event blob data, for example, flickering LEDs such as car headlights or any
small foreground object moving against a static or slowly varying background.
The proposed algorithm uses a nearest neighbour classifier with a dynamic
threshold criteria for data association coupled with a Kalman filter to track
the event blob state. Our algorithm achieves highly accurate tracking and event
blob shape estimation even under challenging lighting conditions and high-speed
motions. The microsecond time resolution achieved means that the filter output
can be used to derive secondary information such as time-to-contact or range
estimation, that will enable applications to real-world problems such as
collision avoidance in autonomous driving.Comment: 17 pages, 8 figures, preprint versio
Qualitative Failure Analysis for a Small Quadrotor Unmanned Aircraft System
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106490/1/AIAA2013-4761.pd
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