Design and Control of an Indoor Coaxial Helicopter

Bouabdallah,; Siegwart, Roland; Caprari,

doi:10.1109/iros.2006.282146

Cited by 42 publications

(26 citation statements)

References 4 publications

(4 reference statements)

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“…Others showed that PID controllers are very capable of stabilising helicopters [8], [9]. Nevertheless, determining good PID control parameters can be a challenging task [10].…”

Section: A Autonomous Helicoptermentioning

confidence: 99%

Mitigating the effect of background noise in sound based helicopter control

Passow

Coupland

Góngora

2010

International Conference on Fuzzy Systems

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Abstract-Our research focuses on the use of sound to enhance the control of an autonomous indoor helicopterFlyper. One of the many challenging problems in this project is managing the uncertainty which is present in input data and the control actions; this paper focuses on managing the uncertainty in the input data. We present a fuzzy logic system which infers how much confidence should be placed on a control decision based on the data which was used to make that decision. The input data is a supervised and sound based position estimate of the flying robot. The output of the fuzzy inference system provides us with a confidence parameter used to attenuate the position control of the autonomous helicopter. We performed test flights with and without the fuzzy confidence parameter and with and without artificial disturbance in form of concurrent speech. We employed a motion tracker to capture the helicopter's movement during all test flights. The analysis of the data collected shows encouraging results.

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“…Others showed that PID controllers are very capable of stabilising helicopters [8], [9]. Nevertheless, determining good PID control parameters can be a challenging task [10].…”

Section: A Autonomous Helicoptermentioning

confidence: 99%

Mitigating the effect of background noise in sound based helicopter control

Passow

Coupland

Góngora

2010

International Conference on Fuzzy Systems

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“…Others showed that PID controllers are very capable of stabilising helicopters [23], [5], [6]. Nevertheless, determining good PID control parameters can be a challenging task [24].…”

Section: B Control Architecturementioning

confidence: 99%

Managing uncertainty in sound based control for an autonomous helicopter

Passow

Góngora

Smith

et al. 2009

2009 IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems

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Abstract-In this paper we present our ongoing research using a multi-purpose, small and low cost autonomous helicopter platform (Flyper). We are building on previously achieved stable control using evolutionary tuning. We propose a sound based supervised method to localise the indoor helicopter and extract meaningful information to enable the helicopter to further stabilise its flight and correct its flightpath. Due to the high amount of uncertainty in the data, we propose the use of fuzzy logic in the signal processing of the sound signature. We discuss the benefits and difficulties using type-1 and type-2 fuzzy logic in this real-time systems and give an overview of our proposed system.

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“…There have been many rotorcraft UAV platforms developed, including quad-rotors (Hoffmann et al, 2007;Gurdan et al, 2007) that operate on the same principles as the hex-rotor developed here. Other autonomous rotorcraft morphologies include coaxial vehicles (Bouabdallah et al, 2006;Ng et al, 2004) and conventional helicopter platforms (Bagnell and Schneider, 2001).…”

Section: Related Workmentioning

confidence: 99%

On the Design and Use of a Micro Air Vehicle to Track and Avoid Adversaries

Bachrach

Achtelik³

et al. 2009

The International Journal of Robotics Research

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Abstract-The MAV '08 competition focused on the problem of using air and ground vehicles to locate and rescue hostages being held in a remote building. To execute this mission, a number of technical challenges were addressed, including designing the micro air vehicle (MAV), using the MAV to geo-locate ground targets, and planning the motion of ground vehicles to reach the hostage location without detection.In this paper, we describe the complete system designed for the MAV '08 competition, and present our solutions to three technical challenges that were addressed within this system. First, we summarize the design of our micro air vehicle, focusing on the navigation and sensing payload. Second, we describe the vision and state estimation algorithms used to track ground features, including stationary obstacles and moving adversaries, from a sequence of images collected by the MAV. Third, we describe the planning algorithm used to generate motion plans for the ground vehicles to approach the hostage building undetected by adversaries; these adversaries are tracked by the MAV from the air. We examine different variants of a search algorithm and describe their performance under different conditions. Finally, we provide results of our system's performance during the mission execution.

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Design and Control of an Indoor Coaxial Helicopter

Cited by 42 publications

References 4 publications

Mitigating the effect of background noise in sound based helicopter control

Mitigating the effect of background noise in sound based helicopter control

Managing uncertainty in sound based control for an autonomous helicopter

On the Design and Use of a Micro Air Vehicle to Track and Avoid Adversaries

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