Design and Development of a Low-Cost Test-Bed for Undergraduate Education in UAVs

Jung, Dong-Won; Levy, E.; Zhou, De Xin; Fink, Rainer; Moshe, J.; Earl, Anthony; Tsiotras, Panagiotis

doi:10.1109/cdc.2005.1582577

Cited by 30 publications

(20 citation statements)

References 14 publications

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“…The on-board autopilot is based on the Rabbit RCM-3400 micro-controller (30 MHz with 512 KB RAM) and provides data acquisition, processing, and communication with the ground station, in addition to all UAV control functions. Further details about the UAV platform, autopilot and HILS set-up can be found in [6] and [7].…”

Section: A Hardware Overviewmentioning

confidence: 99%

Multiresolution on-line path planning for small unmanned aerial vehicles

Jung

Tsiotras

2008

2008 American Control Conference

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In this article we propose a new online multiresolution path planning algorithm for a small unmanned air vehicle (UAV) with limited on-board computational resources. The proposed approach assumes that the UAV has detailed information of the environment only in the vicinity of its current position. Information about far away obstacles is also available, albeit with less accuracy. The proposed algorithm uses an integer arithmetic implementation of the fast lifting wavelet transform (FLWT) to get a multiresolution cell decomposition of the environment, whose dimension is commensurate to the onboard computational resources. A topological graph representation of the multiresolution cell decomposition is constructed efficiently, directly from the approximation and detail wavelet coefficients. Hardware-in-the-loop simulation (HILS) results validate the applicability of the algorithm on a small UAV autopilot. Comparisons with the standard D * -lite algorithm are also presented. I. INTRODUCTIONAutonomous operation of UAVs requires both trajectory design (planning) and trajectory tracking (control) tasks to be completely automated. Given the short response time scales of modern aerial vehicles, on-board, real-time path planning is particulary challenging for small UAVs, which may not have the on-board computational capabilities (e.g. CPU and memory) to implement some of the sophisticated path planning algorithms developed in the literature.In a typical mission of a UAV, various sensors (e.g., cameras, radars, laser scanners, satellite imagery) having different range and resolution characteristics are employed to collect information about the environment the vehicle operates in. A computationally efficient path planning algorithm, specifically adopted for on-line implementation, should therefore choose the expedient information from all these sensors, and utilize the on-board computational resources on the part of the path (spatial and temporal) that needs it most. In a nutshell, a computationally efficient algorithm suitable for on-line implementation should be characterized by a combination of short term tactics (reaction to unforeseen threats) with long-term strategy (planning towards the ultimate goal).Multiresolution analysis is widely used in practice to mitigate the computational overhead in numerically costly applications, for example, computer graphics, using progressive, view-dependent, meshes [4]. The application of multiresolution methods to path planning problems is relatively recent. In [1], [13] multiresolution, hierarchical algorithms were used to alleviate the computational burden associated

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Section: A Hardware Overviewmentioning

confidence: 99%

Multiresolution on-line path planning for small unmanned aerial vehicles

Jung

Tsiotras

2008

2008 American Control Conference

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“…Developing a UAV research platform to validate these new principles and technologies is of great importance, and so it is no surprise that many UAV testbeds have been developed by various academic groups, e.g., [9,20,25,26,32,36,38,40,42]. The main goal in building our testbed is to develop a lowcost and reliable fixed-wing UAV platform that can be used to implement relatively complex control algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, the UAV research group at the University of Minnesota have developed a low-cost UAV testbed [11,38], which is mostly built of commercial-off-the-shelf (COTS) components and uses the radio-controlled (R/C) fixed-wing UAV, Ultra Stick 25e, as the main test aircraft. Another low-cost UAV platform, based on the R/C Goldberg Decathlon ARF airframe, has been developed at Georgia Tech [26] for educational purposes. The UAV research platform [20] uses the low-cost open source autopilot, Ardupilot Mega, and the Aero Testbed at the University of Illinois uses the Paparazzi autopilot, which is also low-cost and open source.…”

Section: Introductionmentioning

confidence: 99%

“…In [30], the output error method is utilized to estimate stability and control derivatives that capture the roll attitude dynamics from test data. Nonlinear constrained optimization is used in [26] to estimate parameters that minimize the difference between measured and model predicted output data. Both longitudinal and lateral-directional models were obtained using the linearized dynamics for each mode, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Development and Modeling of a Low-Cost Unmanned Aerial Vehicle Research Platform

Arifianto

Farhood

2014

J Intell Robot Syst

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This paper describes the development and modeling of a low-cost and reliable small unmanned aerial vehicle research platform for advanced control implementation. The platform is mostly constructed of low-cost commercial-off-the-shelf (COTS) components. The only non-COTS components are the airdata probes, which are manufactured and calibrated in-house. The airframe used is the commercially available radio-controlled (R/C) 6-foot Telemaster airplane from Hobby Express, chosen mainly for its adequately spacious fuselage and for being reasonably stable and sufficiently agile. One noteworthy feature of this platform is the use of two separate low-cost onboard computers for handling the data management/hardware interfacing and control computation. Specifically, the single board computer, Gumstix Overo Fire, is used to execute the control algorithms, whereas the open source autopilot, Ardupilot Mega, is mostly used to interface the Overo computer with the sensors and actuators. The platform supports multi-vehicle operations through the use of a radio modem that enables multi-point communications. As the goal of this platform is to implement rigorous control algorithms for real-time trajectory tracking and distributed control, it O. Arifianto · M. Farhood ( ) is important to derive an appropriate flight dynamic model of the platform, based on which the controllers will be synthesized. For that matter, the paper provides reasonably accurate models of the vehicle, servomotors, and propulsion system. Namely, the output error method is used to estimate the longitudinal and lateraldirectional aerodynamic parameters from flight test data. The moments of inertia of the platform are determined using the simple pendulum test method, and the frequency response of each servomotor is also obtained experimentally. The Javaprop applet is used to obtain lookup tables relating airspeed to propeller thrust at constant throttle settings.

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“…A simple Rabbit Semiconductor RCM-3400 TM microcontroller is used to perform complementary filtering data fusion using a limited computational power. In the same spirit, in (Jung et al, 2005), a low-cost testbed for UAVs is presented. It is reported that the main advantage of designing such an autopilot from scratch is that, by contrast to commercially available products (Cloud Cap Technologies, 2004;Micro Pilot, 2004), it provides full access to the internal control structures.…”

Section: Introductionmentioning

confidence: 99%

An Embedded System for Small-Scaled Autonomous Vehicles

Vissière

Petit

2008

Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics Service

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Abstract:We consider the problem of designing a modular real-time embedded system for control applications with unmanned vehicles. We propose a simple and low-cost solution. Its computational power can be easily improved, depending on application requirements. To illustrate its performance, we report the implementation of a 75 Hz Extended Kalman Filter used for state estimation on a small-scaled helicopter.

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Design and Development of a Low-Cost Test-Bed for Undergraduate Education in UAVs

Cited by 30 publications

References 14 publications

Multiresolution on-line path planning for small unmanned aerial vehicles

Multiresolution on-line path planning for small unmanned aerial vehicles

Development and Modeling of a Low-Cost Unmanned Aerial Vehicle Research Platform

An Embedded System for Small-Scaled Autonomous Vehicles

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