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

As the United States is on the cusp of returning astronauts to the Moon, it becomes increasingly apparent that the assembly of structures in space will have to rely upon robots to perform the construction process. With a focus on sustaining a presence on the Moon's surface in such a harsh and unforgiving environment, demonstrating the robustness of autonomous assembly and capabilities of robotic manipulators is necessary. Current robotic assembly on Earth consists mainly of inspection or highly controlled environments, and always with a human in the loop to step in and fix issues if a problem occurs. To remove the human element, the robot system then must account for safety as well. Thus, system risk can easily overwhelm project costs. This thesis proposes a combination of hardware and state estimation solutions to improve the feasibility of low-fidelity and low-resolution robots for precision assembly tasks. Doing so reduces the risk to mission success, as the hardware becomes easier to replace or repair. The hardware modifications implement an electro-permanent magnet interface with alignment features to reduce the fidelity needed for the robot end effector. On the state estimation side, an Ensemble Kalman Filter is implemented, along with a scaling system to prevent FASER Lab hardware from becoming stuck due to hardware limitations. Overall, the three modifications improved the test robot's autonomous convergence error by 98.5%, bettering the system sufficiently to make an autonomous assembly process feasible. / Master of Science / With the dawn of new space age nearly upon us, one of the most important aspects to working in space will be robotic assembly, whether on the surface of other planetary bodies like the Moon or in zero-gravity, in order to keep astronauts safe and to reduce spaceship launch costs. Both places have their own difficult problems to deal with, and doing any actions in those locations come with a significant amount of risk involved. To reduce extreme risk, you can spend more money to over-protect the robots, or reduce the consequences of the risk. This thesis describes a way to reduce the impact of risks to a mission by checking whether inexpensive robots can be adapted and modified to be able to perform similar construction actions to a much more expensive robot. It does this by using specialized hardware and software programs to better align the robot to where it needs to go without people needing to step in and help it. The experiments showed a 98.5% improvement to the system from without any of the modifications and validated that the low-cost robot could be improved sufficiently to be useful.

This thesis details the implementation of two adaptive controllers on autonomous underwater vehicle(AUV) attitude dynamics starting from the standard six degree-of-freedom dynamic model. I apply two model reference adaptive control (MRAC) algorithms which make use of kernel functions for learning functional uncertainty present in the system dynamics. The first method extends recent results on model reference adaptive control using reproducing kernel Hilbert space (RKHS) learning techniques for some general cases of multi-input systems. The first controller design is a model reference adaptive controller (MRAC) based on a vector- valued RKHS that is induced by operator-valued kernels. This paper formulates a model reference adaptive control strategy based on a dead zone robust modification, and derives conditions for the ultimate boundedness of the tracking error in this case. The second controller is an implementation of the Gaussian Process MRAC developed by Chowdhary, et al. I discuss the method of each of these algorithms before contrasting the underlying theoretical structure of each algorithm. Finally, I provide a comparison of each algorithm's performance on the six degree-of-freedom dynamic model of the Virginia Tech 690 AUV and provide field trial results for the RKHS based MRAC implementation. / Master of Science / This thesis details the implementation of two algorithms which control the attitude of an autonomous underwater vehicle. Rather than developing detailed dynamic models of the vehicles as is performed in classical control methods, each of these implementations only makes assumptions that the unknown portions of the dynamic models can be represented by a broad class of functions defined by a mathematical structure called a reproducing kernel Hilbert Space. Each algorithm implements learning techniques using the theory of reproducing kernel Hilbert spaces to bound the error between the vehicle attitude and the commanded vehicle attitude. One algorithm, called RKHS MRAC, implements an adaptive update law based on the attitude error to improve the controller performance. The second algorithm, called GP MRAC, uses estimated vehicle rotational accelerations and statistical learning methods to approximate the unknown function. Each of these methods is compared in theory and in a vehicle simulation. The RKHS MRAC is additionally demonstrated in field trial results.

Navigation of autonomous underwater vehicles (AUVs) in the subsea environment is particularly challenging due to the unavailability of GPS because of rapid attenuation of electromagnetic waves in water. As a result, the AUV requires alternative methods for position estimation. This thesis describes a terrain-aided navigation approach for an AUV where, with the help of a prior depth map, the AUV localizes itself using altitude measurements from a multibeam DVL. The AUV simultaneously builds a probabilistic depth map of the seafloor as it moves to unmapped locations. The main contribution of this thesis is a new, scalable, and on-line terrain-aided navigation solution for AUVs which does not require the assistance of a support surface vessel. Simulation results on synthetic data and experimental results from AUV field trials in Panama City, Florida are also presented. / Master of Science / Navigation of autonomous underwater vehicles (AUVs) in subsea environment is particularly challenging due to the unavailability of GPS because of rapid attenuation of electromagnetic waves in water. As a result, the AUV requires alternative methods for position estimation. This thesis describes a terrain-aided navigation approach for an AUV where, with the help of a prior depth map, the AUV localizes itself using altitude measurements from a multibeam DVL. The AUV simultaneously builds a probabilistic depth map of the seafloor as it moves to unmapped locations. The main contribution of this thesis is a new, scalable, and on-line terrain-aided navigation solution for AUVs which does not require assistance of a support surface vessel. Simulation results on synthetic data and experimental results from AUV field trials in Panama City, Florida are also presented.

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.