Thesis (M.S.)--University of Missouri-Columbia, 2003. / Typescript. Vita. Includes bibliographical references (leaves 68-69). Also available on the Internet.

Thesis (M.S.)--University of Missouri-Columbia, 2003. / Typescript. Vita. Includes bibliographical references (leaves 68-69). Also available on the Internet.

Attitude estimation of unmanned aerial vehicles is of great importance as it enables propercontrol of the vehicles. Attitude estimation is typically done by an attitude-heading refer-ence system (ahrs) which utilises different kind of sensors. In this thesis these include agyroscope providing angular rates measurements which can be integrated to describe the at-titude as well as an accelerometer and a magnetometer, both of which can be compared withknown reference vectors to determine the attitude. The sensor measurements are fused usinga gps augmented 7-state Extended Kalman filter (ekf) with a quaternion and gyroscope bi-ases as state variables. It uses differentiated gps velocity measurements to estimate externalaccelerations as reference vector to the accelerometer, which significantly raises robustnessof the solution. The filter is implemented in MatlabTM and in c on an ARM microprocessor.It is compared with an explicit complementary filter solution and is evaluated with flightsusing a fixed-wing uav with satisfactory results.

Agents, by definition, (1) are situated in an environment upon which their actions affect changes and (2) have some level of autonomy from the control of humans or other agents. Being situated requires that the agent have a mechanism for sensing the environment as well as actuators for changing the environment. Autonomy implies that each agent has the freedom to make their own decisions. Rational agents are those agents that decide to execute actions that are in their “best interests” according to their desires, using a model of those desires on which they make those decisions. Action selection is complicated due to uncertainty when operating in a dynamic environment or where other actors (agents) can also influence the environment. This dissertation presents an action selection framework and algorithms that are (1) rational with respect to multiple desires and (2) responsive with respect to changing desires. Agents can use the concept of commitments, and the subsequent communication of those commitments, to coordinate their actions and reduce their uncertainty. Coordination is layered on top of this framework by describing and analyzing how commitments affect the agents’ desires in their action selection models. This research uses the domain of UAV surveillance to experimentally explore the balance between under-commitment and over-commitment. Where previous approaches concentrate on the semantics of commitment, this research concentrates on the pragmatics of commitment, describing how to use utility calculations to enable an agent to decide when making a commitment is in its best interests. / text

Autonomous mobile robots have the potential to be extremely beneficial to society due to their ability to perform tasks that are difficult or dangerous for humans. These robots will necessarily interact with their environment through the two fundamental processes of acting and sensing. Robots learn about the state of the world around them through their sensations, and they influence that state through their actions. However, in order to interact with their environment effectively, these robots must have accurate models of their sensors and actions: knowledge of what their sensations say about the state of the world and how their actions affect that state. A mobile robot’s action and sensor models are typically tuned manually, a brittle and laborious process. The robot’s actions and sensors may change either over time from wear or because of a novel environment’s terrain or lighting. It is therefore valuable for the robot to be able to autonomously learn these models. This dissertation presents a methodology that enables mobile robots to learn their action and sensor models starting without an accurate estimate of either model. This methodology is instantiated in three robotic scenarios. First, an algorithm is presented that enables an autonomous agent to learn its action and sensor models in a class of one-dimensional settings. Experimental tests are performed on a four-legged robot, the Sony Aibo ERS-7, walking forward and backward at different speeds while facing a fixed landmark. Second, a probabilistically motivated model learning algorithm is presented that operates on the same robot walking in two dimensions with arbitrary combinations of forward, sideways, and turning velocities. Finally, an algorithm is presented to learn the action and sensor models of a very different mobile robot, an autonomous car. / text

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ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV / This paper describes the sensor and actuator package for a 6-axis articulated arm which is part of a robotic vehicle entered in the Mars Rover Challenge competition. The robot is intended to perform some of the same duties as a human, but be remotely controlled. It uses an articulated arm for many of these duties. Because of the large number of degrees of freedom, it would be tedious to control each joint individually. A system was developed to measure the state of each joint, transmit this information back to a base station, and semi-autonomously control the arm.

The DARPA Urban Challenge in 2007 showed that fully autonomous vehicles, driven by computers without human intervention on public roads, are technologically feasible with current intelligent vehicle technology [6]. Some researchers predict that within 5-20 years there will be autonomous vehicles for sale on the automobile market. Therefore, the time is right to rethink our current transportation infrastructure, which is primarily designed for human drivers, not autonomous vehicles. The Autonomous Intersection Management (AIM) project at UT Austin aims to propose a large-scale, real-time framework to be a substitute for current traffic light and stop signs. Automobiles in modern urban settings spend a lot of time idling at intersections. In 2007, US drivers wasted 4.16 billion hours of their time and 2.81 billion gallons of gas in congestion, costing a total of 87.2 billion dollars nationwide [18]. A big portion of this waste takes place at intersections. The AIM project is able to utilize the capacity of intersections to minimize time waste and fuel consumption. The fundamental idea of Autonomous Intersection management (AIM) [13] is a reservation system in which cells in space-time will be reserved by the au- tonomous vehicles based on their trajectories. An intersection manager takes care of the reservation as well as communication with the vehicles. This mechanism tries to maximize the usage of the intersection area. It ensures a collision free intersection as well. The main question of this project is what intersection control mechanism is appropriate for reducing an autonomous vehicle's waiting time and improving the throughput of the intersection. Previous work proposed the first-come-first-served (FCFS) policy in which the reservation requests are served as soon as they are received. The results of simulation show that FCFS outperforms the current traffic systems, traffic light and stop sign, by orders of magnitude. We, however, observe that FCFS performs suboptimal in certain traffic patterns that are pretty common in urban settings. In this project, first we study the limitations of FCFS, then develop a more efficient policy to alleviate these limitations. The idea that we examined is a systematic policy of granting reservations that have the objective of minimizing the cost of delaying vehicles. In an attempt to build the system in reality, we used miniature robots called Eco-be. Due to their cost and size, Eco-bes are good candidates for testing a multi-agent system with a large number of agents. In spite of the fact that the physical challenges of Eco-bes do not perfectly match those of full size autonomous vehicles, they are still useful for demonstration and education purposes as well as for the study of collisions for which experiments with full size vehicles are costly and dangerous. / text

published_or_final_version / Earth Sciences / Master / Master of Philosophy

Starting this thesis with the intention to inspire and to be inspired, I have tried to zoom out and look on designing a car from a new perspective. By telling a holistic design story rather than solving a specific problem I want people to imagine a future where one can live a modern and connected life in rural communities, all made possible after a green energy revolution. Design research, brand analysis, sketching, form verification using clay and digital modeling and advanced visualization were the main activities performed during the project. They helped me to explore, understand and successfully propose a complete story of vehicle and context. The result is BMW iMPULSE, a shared and fully autonomous vehicle that is wirelessly powered by clean energy and is always ready to support the spontaneous lifestyle