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Biologically Inspired Vision and Control for an Autonomous Flying VehicleGarratt, Matthew Adam, m.garratt@adfa.edu.au 17 February 2008 (has links)
This thesis makes a number of new contributions to control and sensing for unmanned vehicles. I begin by developing a non-linear simulation of a small unmanned helicopter and then proceed to develop new algorithms for control and sensing using the simulation. The work is field-tested in successful flight trials of biologically
inspired vision and neural network control for an unstable rotorcraft. The techniques are more robust and more easily implemented on a small flying vehicle than previously attempted methods.¶
Experiments from biology suggest that the sensing of image motion or optic
flow in insects provides a means of determining the range to obstacles and terrain. This biologically inspired approach is applied to control of height in a helicopter, leading to the Worlds first optic flow based terrain following controller for an unmanned helicopter in forward flight. Another novel optic flow based controller is developed for the control of velocity in hover. Using the measurements of height from other sensors, optic flow is used to provide a measure of the helicopters lateral and longitudinal velocities relative to the ground plane. Feedback of these velocity measurements enables automated hover with a drift of only a few cm per second, which is sufficient to allow a helicopter to land autonomously in gusty conditions
with no absolute measurement of position.¶
New techniques for sensor fusion using Extended Kalman Filtering are developed to estimate attitude and velocity from noisy inertial sensors and optic flow measurements. However, such control and sensor fusion techniques can be computationally
intensive, rendering them difficult or impossible to implement on a small
unmanned vehicle due to limitations on computing resources. Since neural networks can perform these functions with minimal computing hardware, a new technique of control using neural networks is presented. First a hybrid plant model consisting of exactly known dynamics is combined with a black-box representation of the unknown dynamics. Simulated trajectories are then calculated for the plant using an optimal controller. Finally, a neural network is trained to mimic the optimal controller. Flight test results of control of the heave dynamics of a helicopter confirm
the neural network controllers ability to operate in high disturbance conditions and suggest that the neural network outperforms a PD controller. Sensor fusion and control of the lateral and longitudinal dynamics of the helicopter are also shown to
be easily achieved using computationally modest neural networks.
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Shooter Localization in a Wireless Sensor Network / Lokalisering av skytt i ett trådlöst sensornätverkWilsson, Olof January 2009 (has links)
<p>Shooter localization systems are used to detect and locate the origin of gunfire. A wireless sensor network is one possible implementation of such a system. A wireless sensor network is sensitive to synchronization errors. Localization techniques that rely on the timing will give less accurate or even useless results if the synchronization errors are too large.</p><p>This thesis focuses on the influence of synchronization errors on the abilityto localize a shooter using a wireless sensor network. A localization algorithm</p><p>is developed and implemented and the effect of synchronization errors is studied. The localization algorithm is evaluated using numerical experiments, simulations, and data from real gunshots collected at field trials.</p><p>The results indicate that the developed localization algorithm is able to localizea shooter with quite good accuracy. However, the localization performance is to a high degree influenced by the geographical configuration of the network as well as the synchronization error.</p> / <p><p>Skottlokaliseringssystem används för att upptäcka och lokalisera ursprunget för avlossade skott. Ett trådlöst sensornätverk är ett sätt att utforma ett sådant system.Trådlösa sensornätverk är känsliga för synkroniseringsfel. Lokaliseringsmetoder som bygger på tidsobservationer kommer med för stora synkroniseringsfel ge dåliga eller helt felaktiga resultat.</p><p>Detta examensarbete fokuserar på vilken inverkan synkroniseringsfel har på möjligheterna att lokalisera en skytt i ett trådlöst sensornätverk. En lokaliseringsalgoritm utvecklas och förmågan att korrekt lokalisera en skytt vid olika synkroniseringsfel undersöks. Lokaliseringsalgoritmen prövas med numeriska experiment, simuleringar och även för data från riktiga skottljud, insamlade vid fältförsök.</p><p>Resultaten visar att lokaliseringsalgoritmen fungerar tillfredställande, men att lokaliseringsförmågan till stor del påverkas av synkroniseringsfel men även av sensornätverkets geografiska utseende.</p></p>
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Fusing Laser and Radar Data for Enhanced Situation Awareness / Fusion av laser- och radardata för ökad omvärldsuppfattningEliasson, Emanuel January 2010 (has links)
<p>With an increasing traffic intensity the demands on vehicular safety is higher than ever before. Active safety systems that have been developed recent years are a response to that. In this master thesis Sensor Fusion is used to combine information from a laser scanner and a microwave radar in order to get more information about the surroundings in front of a vehicle. The Extended Kalman Filter method has been used to fuse the information from the sensors. The process model consists partly of a Constant Turn model to describe the motion of the ego vehicle as well as a tracked object. These individual motions are then put together in a framework for spatial relationships to describe the relationship between them. Two measurement models have been used to describe the two sensors. They have been derived from a general sensor model. This filter approach has been used to estimate the position and orientation of an object relative the ego vehicle. Also velocity, yaw rate and the width of the object have been estimated. The filter has been implemented and simulated in Matlab. The data that has been recorded and used in this work is coming from a scenario where the ego vehicle is following an object in a quite straight line. Where the ego vehicle is a truck and the object is a bus. One important conclusion from this work is that the filter is sensitive to the number of laser beams that hits the object of interest. No qualitative validation has been made though.</p>
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Visual-inertial tracking using Optical Flow measurementsLarsson, Olof January 2010 (has links)
<p> </p><p>Visual-inertial tracking is a well known technique to track a combination of a camera and an inertial measurement unit (IMU). An issue with the straight-forward approach is the need of known 3D points. To by-pass this, 2D information can be used without recovering depth to estimate the position and orientation (pose) of the camera. This Master's thesis investigates the feasibility of using Optical Flow (OF) measurements and indicates the benifits using this approach.</p><p>The 2D information is added using OF measurements. OF describes the visual flow of interest points in the image plane. Without the necessity to estimate depth of these points, the computational complexity is reduced. With the increased 2D information, the 3D information required for the pose estimate decreases.</p><p>The usage of 2D points for the pose estimation has been verified with experimental data gathered by a real camera/IMU-system. Several data sequences containing different trajectories are used to estimate the pose. It is shown that OF measurements can be used to improve visual-inertial tracking with reduced need of 3D-point registrations.</p>
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Nonlinear and distributed sensory estimationSugathevan, Suranthiran 29 August 2005 (has links)
Methods to improve performance of sensors with regard to sensor nonlinearity, sensor noise and sensor bandwidths are investigated and new algorithms are developed. The necessity of the proposed research has evolved from the ever-increasing need for greater precision and improved reliability in sensor measurements. After describing the current state of the art of sensor related issues like nonlinearity and bandwidth, research goals are set to create a new trend on the usage of sensors. We begin the investigation with a detailed distortion analysis of nonlinear sensors. A need for efficient distortion compensation procedures is further justified by showing how a slight deviation from the linearity assumption leads to a very severe distortion in time and in frequency domains. It is argued that with a suitable distortion compensation technique the danger of having an infinite bandwidth nonlinear sensory operation, which is dictated by nonlinear distortion, can be avoided. Several distortion compensation techniques are developed and their performance is validated by simulation and experimental results. Like any other model-based technique, modeling errors or model uncertainty affects performance of the proposed scheme, this leads to the innovation of robust signal reconstruction. A treatment for this problem is given and a novel technique, which uses a nominal model instead of an accurate model and produces the results that are robust to model uncertainty, is developed. The means to attain a high operating bandwidth are developed by utilizing several low bandwidth pass-band sensors. It is pointed out that instead of using a single sensor to measure a high bandwidth signal, there are many advantages of using an array of several pass-band sensors. Having shown that employment of sensor arrays is an economic incentive and practical, several multi-sensor fusion schemes are developed to facilitate their implementation. Another aspect of this dissertation is to develop means to deal with outliers in sensor measurements. As fault sensor data detection is an essential element of multi-sensor network implementation, which is used to improve system reliability and robustness, several sensor scheduling configurations are derived to identify and to remove outliers.
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Visual-inertial tracking using Optical Flow measurementsLarsson, Olof January 2010 (has links)
Visual-inertial tracking is a well known technique to track a combination of a camera and an inertial measurement unit (IMU). An issue with the straight-forward approach is the need of known 3D points. To by-pass this, 2D information can be used without recovering depth to estimate the position and orientation (pose) of the camera. This Master's thesis investigates the feasibility of using Optical Flow (OF) measurements and indicates the benifits using this approach. The 2D information is added using OF measurements. OF describes the visual flow of interest points in the image plane. Without the necessity to estimate depth of these points, the computational complexity is reduced. With the increased 2D information, the 3D information required for the pose estimate decreases. The usage of 2D points for the pose estimation has been verified with experimental data gathered by a real camera/IMU-system. Several data sequences containing different trajectories are used to estimate the pose. It is shown that OF measurements can be used to improve visual-inertial tracking with reduced need of 3D-point registrations.
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Fusing Laser and Radar Data for Enhanced Situation Awareness / Fusion av laser- och radardata för ökad omvärldsuppfattningEliasson, Emanuel January 2010 (has links)
With an increasing traffic intensity the demands on vehicular safety is higher than ever before. Active safety systems that have been developed recent years are a response to that. In this master thesis Sensor Fusion is used to combine information from a laser scanner and a microwave radar in order to get more information about the surroundings in front of a vehicle. The Extended Kalman Filter method has been used to fuse the information from the sensors. The process model consists partly of a Constant Turn model to describe the motion of the ego vehicle as well as a tracked object. These individual motions are then put together in a framework for spatial relationships to describe the relationship between them. Two measurement models have been used to describe the two sensors. They have been derived from a general sensor model. This filter approach has been used to estimate the position and orientation of an object relative the ego vehicle. Also velocity, yaw rate and the width of the object have been estimated. The filter has been implemented and simulated in Matlab. The data that has been recorded and used in this work is coming from a scenario where the ego vehicle is following an object in a quite straight line. Where the ego vehicle is a truck and the object is a bus. One important conclusion from this work is that the filter is sensitive to the number of laser beams that hits the object of interest. No qualitative validation has been made though.
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A Localisation and Navigation System for an Autonomous Wheel LoaderLilja, Robin January 2011 (has links)
Autonomous vehicles are an emerging trend in robotics, seen in a vast range of applications and environments. Consequently, Volvo Construction Equipment endeavour to apply the concept of autonomous vehicles onto one of their main products. In the company’s Autonomous Machine project an autonomous wheel loader is being developed. As an ob jective given by the company; a demonstration proving the possibility of conducting a fully autonomous load and haul cycle should be performed. Conducting such cycle requires the vehicle to be able to localise itself in its task space and navigate accordingly. In this Master’s Thesis, methods of solving those requirements are proposed and evaluated on a real wheel loader. The approach taken regarding localisation, is to apply sensor fusion, by extended Kalman filtering, to the available sensors mounted on the vehicle, including; odometric sensors, a Global Positioning System receiver and an Inertial Measurement Unit. Navigational control is provided through an interface developed, allowing high level software to command the vehicle by specifying drive paths. A path following controller is implemented and evaluated. The main objective was successfully accomplished by integrating the developed localisation and navigational system with the existing system prior this thesis. A discussion of how to continue the development concludes the report; the addition of a continuous vision feedback is proposed as the next logical advancement.
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UKF and EKF with time dependent measurement and model uncertainties for state estimation in heavy duty diesel enginesBerggren, Henrik, Melin, Martin January 2011 (has links)
The continuous challenge to decrease emissions, sensor costs and fuel consumption in diesel engines is battled in this thesis. To reach higher goals in engine efficiency and environmental sustainability the prediction of engine states is essential due to their importance in engine control and diagnosis. Model output will be improved with help from sensors, advanced mathematics and non linear Kalman filtering. The task consist of constructing non linear Kalman Filters and to adaptively weight measurements against model output to increase estimation accuracy. This thesis shows an approach of how to improve estimates by nonlinear Kalman filtering and how to achieve additional information that can be used to acquire better accuracy when a sensor fails or to replace existing sensors. The best performing Kalman filter shows a decrease of the Root Mean Square Error of 75 % in comparison to model output.
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Nonlinear and distributed sensory estimationSugathevan, Suranthiran 29 August 2005 (has links)
Methods to improve performance of sensors with regard to sensor nonlinearity, sensor noise and sensor bandwidths are investigated and new algorithms are developed. The necessity of the proposed research has evolved from the ever-increasing need for greater precision and improved reliability in sensor measurements. After describing the current state of the art of sensor related issues like nonlinearity and bandwidth, research goals are set to create a new trend on the usage of sensors. We begin the investigation with a detailed distortion analysis of nonlinear sensors. A need for efficient distortion compensation procedures is further justified by showing how a slight deviation from the linearity assumption leads to a very severe distortion in time and in frequency domains. It is argued that with a suitable distortion compensation technique the danger of having an infinite bandwidth nonlinear sensory operation, which is dictated by nonlinear distortion, can be avoided. Several distortion compensation techniques are developed and their performance is validated by simulation and experimental results. Like any other model-based technique, modeling errors or model uncertainty affects performance of the proposed scheme, this leads to the innovation of robust signal reconstruction. A treatment for this problem is given and a novel technique, which uses a nominal model instead of an accurate model and produces the results that are robust to model uncertainty, is developed. The means to attain a high operating bandwidth are developed by utilizing several low bandwidth pass-band sensors. It is pointed out that instead of using a single sensor to measure a high bandwidth signal, there are many advantages of using an array of several pass-band sensors. Having shown that employment of sensor arrays is an economic incentive and practical, several multi-sensor fusion schemes are developed to facilitate their implementation. Another aspect of this dissertation is to develop means to deal with outliers in sensor measurements. As fault sensor data detection is an essential element of multi-sensor network implementation, which is used to improve system reliability and robustness, several sensor scheduling configurations are derived to identify and to remove outliers.
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