Autonomous Vehicles are complex modular systems with various inter-dependent safety critical modules, the failure of which leads to failure of the overall system. The Localization system, which estimates the pose of the vehicle in the global coordinate frame with respect to a map, has a drift in error, when operated only based on data from proprioceptive sensors. Current solutions to the problem are computationally heavy SLAM algorithms. An alternate system is proposed in the thesis which eliminates the drift by resetting the global coordinate frame to the local frame at every motion planning update. The system replaces the mission planner with a user interface(UI) onto which the User provides local navigation inputs, thus eliminating the need for maintenance of a Global frame. The User Input is considered in the decision framework of the behavioral planner, which selects a safe and legal maneuver for the vehicle to follow. The path and trajectory planners generate a trajectory to accomplish the maneuver and the controller follows the trajectory until the next motion planning update. A prototype of the system has been built on a wheeled robot and tested for the feasibility of continuous operation in Autonomous Vehicles. / Master of Science / Autonomous Vehicles are complex modular systems with various inter-dependent safety critical modules, the failure of which leads to failure of the overall system. One such module is the Localization system, that is responsible for estimating the pose of the vehicle in the global coordinate frame, with respect to a map. Based on the pose, the vehicle navigates to the goal waypoints, which are points in the global coordinate frame specified in the map by the route or mission planner of the planning module. The Localization system, however, consists of a drift in position error, due to poor GPS signals and high noise in the inertial sensors. This has been tackled by applying computationally heavy Simultaneous Localization and Mapping based methods, which identify landmarks in the environment at every time step and correct the vehicle position, based on the relative change in position of landmarks. An alternate solution is proposed in this thesis, which delegates navigation to the passenger. This system replaces the mission planner from the planning module with a User Interface onto which the passenger provides local Navigation input, which is followed by the vehicle. The system resets the global coordinate frame to the vehicle frame at every motion planning update, thus eliminating the error accumulated between the two updates. The system is also designed to perform default actions in the absence of user Navigation commands, to reduce the number of commands to be provided by the passenger in the journey towards the goal. A prototype of the system is built and tested for feasibility.

In this thesis we demonstrate a technique for robust controller design for an autonomous underwater vehicle (AUV) that explicitly handles the trade-off between reference tracking, agility, and energy efficient performance. AUVs have many sources of modeling uncertainty that impact the uncertainty in maneuvering performance. A robust control design process is proposed to handle these uncertainties while meeting control system performance objectives. We investigate the relationships between linear system design parameters and the control performance of our vehicle in order to inform an H∞ controller synthesis problem with the objective of balancing these tradeoffs. We evaluate the controller based on its reference tracking performance, agility and energy efficiency, and show the efficacy of our control design strategy. / Master of Science / In this thesis we demonstrate a technique for autopilot design for an autonomous underwater vehicle (AUV) that explicitly handles the trade-off between three performance metrics. Mathematical models of AUVs are often unable to fully describe their many physical properties. The discrepancies between the mathematical model and reality impact how certain we can be about an AUV's behavior. Robust controllers are a class of controller that are designed to handle uncertainty. A robust control design process is proposed to handle these uncertainties while meeting vehicle performance objectives. We investigate the relationships between design parameters and the performance of our vehicle. We then use this relationship to inform the design of a controller. We evaluate this controller based on its energy efficiency, agility and ability to stay on course, and thus show the effectiveness of our control design strategy.

With vast improvements in computing power in the last two decades, humans have invested significantly in engineering resources in an attempt to automate labor intensive or dangerous tasks. A particularly dangerous and labor-intensive task is painting lines on roads for facilitating urban mobility. This thesis proposes an approach to automate the process of painting lines on the ground using an autonomous ground vehicle (AGV) fitted with a stabilized painting mechanism. The AGV accepts Global Positioning System (GPS) coordinates for waypoint navigation. A computer vision algorithm is developed to provide vision feedback to stabilize the painting mechanism. The system is demonstrated to follow an input desired trajectory and cancel any high frequency vibrations due to the uneven terrain that the vehicle is traversing. Also, the stabilizing system is able to eliminate the long-term drift (due to inaccurate GPS waypoint navigation) using the complementary vision system. / MS / There is a need to develop an automated system capable of painting lines on the ground with minimal human intervention as the current methods to paint lines on the ground are inefficient, labor intensive, and dangerous. The human input to such a system is limited to the determination of the desired trajectory of the line to be drawn. This thesis presents the design and development of an autonomous line painting system that includes an autonomous ground vehicle (capable of following GPS waypoints) integrated with an automatic line painting mechanism. As the vehicle traverses the ground, it experiences disturbances due to the interaction between the wheels and the ground, and also a long-term drift due to inaccurate tracking of the input GPS coordinates. In order to compensate for these disturbances, a vision system is implemented providing feedback to a stabilizing arm. This automated system is able to demonstrate the capability to follow a square trajectory defined by GPS coordinates while compensating for the disturbances.

This research investigates the possibility of controlling a simple biped having two degrees of freedom only. The biped robot walked on large feet. Having large feet enabled the robot to stand on one leg stably. At any time, the robotà ­s center of gravity remained above the area covered by one of the feet. Two servos actuated the two degrees of freedom tilting the robot to the side or moving the legs forward and backward. The biped moved by alternately tilting and striding. Turns were produced by dragging the feet along the ground. As the feet dragged, the friction generated under the feet created a turning moment that rotated the robot. Thus, the robot was able to step and turn on a flat surface. A control algorithm was developed to attempt trajectory tracking with the biped. Trajectories along a surface can be defined in terms of linear and angular velocities. In this research, it was assumed that a high level controller had transformed a desired trajectory into discrete steps of linear and angular velocities. Motion tests showed how various settings of the servos affected the step length and turning angle of the robot. To produce the desired velocities, a program was created to select the servo commands and set the speed parameters. This program applied knowledge of the expected step length and turning angle and performed feedforward control of the velocities. This investigation identified a trajectory tracking scheme that could be used in an observer feedback scenario to achieve accurate control. / Master of Science

There are two commonly accepted paradigms for organizing intelligence in robotic vehicles, namely reactive and deliberative. A third, a hybrid paradigm called integrated planning and execution, is considered a combination of the original two. Although these paradigms are well known to researchers, there are few published examples directly comparing their application and performance on similar vehicles operating in identical environments. Virginia Tech's participation with two nearly identical vehicles in the DARPA Grand Challenge afforded a practical opportunity for such a case study. Both base vehicles were developed by modifying Club Car Pioneer XRT 1500 on-demand four wheel drive base platforms. Cliff was designed to use the reactive paradigm, while Rocky was designed to use the deliberative paradigm. Both vehicles were initially outfitted with sensor suites and computational capabilities commensurate with the paradigm being employed. The author of this thesis coordinated the activities of the two teams of undergraduate and graduate students who implemented the respective designs and software. Both vehicles proved capable of off-road navigation, including road following and obstacle avoidance in complex desert terrain. In the end, however, the reactive paradigm proved to be smoother and more reliable than the deliberative paradigm under the conditions of our testing. While both vehicles were extensively tested and compared using the competing paradigms, the team modified Rocky to use the more effective reactive paradigm for the Grand Challenge events. The deliberative case shows much promise for complex navigation, but added unnecessary complexity to desert road navigation. This case study, while necessarily limited in scope, may help to shed additional light on the tradeoffs and performance of competing approaches to machine intelligence. / Master of Science

Development of unmanned vehicles will increase as the need to save lives rises. In both military and civilian applications, humans can be taken out of the loop through the implementation of safe and intelligent autonomous vehicles. Although hardware and software development continue to play a large role in the autonomous vehicle industry, validation of these systems will always be necessary. The ability to test these vehicles thoroughly and efficiently will ensure their proper and flawless operation. On November 3, 2007 the Defense Advanced Research Projects Agency held the Urban Challenge to drive the development of autonomous ground vehicles for military use. This event required vehicles built by teams across the world to autonomously navigate a 60 mile course in an urban environment in less than 6 hours. This thesis addresses the testing aspect of autonomous ground vehicles that exhibit the advanced behaviors necessary for operating in such an event. Specifically, the experiences of Team Victor Tango and other Urban Challenge teams are covered in detail. Testing facilities, safety measures, procedures, and validation methods utilized by these teams provide valuable information on the development of their vehicles. Combining all these aspects results in a proposed testing strategy for autonomous ground vehicles. / Master of Science

The ability to detect, monitor, and forecast the movement of airborne plant pathogens in agricultural ecosystems is essential for developing rational approaches to managing these habitats. We developed an autonomous (self-controlling) unmanned aerial vehicle (UAV) platform for aerobiological sampling tens to hundreds of meters above agricultural fields. Autonomous UAVs have the potential to extend the range of aerobiological sampling, improve positional accuracy of sampling paths, and enable coordinated flight with multiple aircraft at different altitudes. We equipped a Senior Telemaster model airplane with two spore-sampling devices and a MicroPilot autonomous system, and we conducted over 60 autonomous microbe-sampling flights at Virginia Tech's Kentland Farm. To determine the most appropriate sampling path for aerobiological sampling, we explored a variety of different sampling patterns for our autonomous UAVs including multiple GPS waypoints plotted over a variety of spatial scales. We conducted a total of 25 autonomous aerobiological sampling flights for five different aerobiological sampling patterns. The pattern of a single waypoint exhibited the best flight characteristics with good positional accuracy and standard deviations in altitude from 1.6 to 2.8 meters. The four point pattern configured as a rectangle also demonstrated good flight characteristics and altitude standard deviations from 1.6 to 4.7 meters. / Master of Science

Autonomous ground vehicle operations, such as those found in the 2007 DARPA Urban Challenge, require a reliable and capable vehicle platform. To meet this requirement, an autonomous ground vehicle platform based on a 2005 Ford Escape Hybrid was developed for operations in urban environments. The vehicle conversion, dubbed Odin, contains a drive-by-wire system that is highly integrated with the OEM systems, providing throttle, steering, shifting, and braking actuation. The vehicle also includes a controller that provides low-level longitudinal using a map-linearized PI controller and lateral curvature control using a bicycle model. The control algorithms proved capable of controlling the vehicle at a level acceptable for autonomous operations. Communications are implemented using the Joint Architecture for Unmanned Systems (JAUS) using custom messages to enhance interoperability potential. The net result is a highly capable autonomous vehicle platform that was validated when Odin successfully completed the 60 mile Urban Challenge. / Master of Science

Helicopters have been used in applications where they need to carry a slung load for years. More recently, unmanned (UAV) helicopters are being used to deliver supplies to military units on the ground in theaters of war. This thesis presents a helicopter slung vehicle used to carry the payload and furthermore, provide a means of actuation for the payload. This provides more control authority to the system and may ultimately allow a helicopter to fly higher with a longer tether. The vehicle designed in this thesis was designed for use with 100kg class helicopters, such as the Yamaha RMAX operated by the Virginia Tech Unmanned Systems Lab. Each system on the vehicle was custom designed — including the propulsion system, wall detection / localization system, and controller. Three shrouded propellers provided thruster actuation. A scanning laser range finder and inertial measurement unit (IMU) were used to provide localization. A first attempt at a linear full state feedback controller with a complementary filter was used to control the vehicle. All of the systems were tested individually for functionality. The shrouded propellers met their design goals and were capable of producing .7lbf of thrust each. The wall detection system was able to detect walls and windows reliably and with repeatability. Results from the controller however were less than ideal, as it was only able to control yaw in an oscillatory motion, most likely due to model deficiencies. A reaction wheel was used to control yaw of the vehicle with more success. / Master of Science

Position in the environment is essential in any autonomous system. As increased accuracy is required, the costs escalate accordingly. This paper presents a simple way to systematically integrate sensory data to provide a drivable and accurate position solution at a low cost. The data fusion is handled by a Kalman filter tracking five states and an undetermined number of asynchronous measurements. This implementation allows the user to define additional adjustments to improve the overall behavior of the filter. The filter is tested using a suite of inexpensive sensors and then compared to a differential GPS position. The output of the filter is indeed a drivable solution that tracks the reference position remarkably well. This approach takes advantage of the short-term accuracy of odometry measurements and the long-term fix of a GPS unit. A maximum error of two meters of deviation from the reference is shown for a complex path over two minutes and 100 meters long. / Master of Science