No description available.

No description available.

No description available.

No description available.

The use of autonomous aerial vehicles (UAVs) to explore unknown environments is a growing field of research; of particular interest is locating a target that emits a signal within an unknown environment. Several physical processes produce scalar signals that attenuate with distance from their source, such as chemical, biological, electromagnetic, thermal, and radar signals. The natural decay of the signal with increasing distance enables a gradient ascent method to be used to navigate toward the target. The UAVs navigate around obstacles whose positions are initially unknown; a hybrid controller comprised of overlapping control modes enables robust obstacle avoidance in the presence of exogenous inputs by precluding topological obstructions. Limitations of a distributed gradient augmentation approach to obstacle avoidance are discussed, and an alternative algorithm is presented which retains the robustness of the hybrid control while leveraging local obstacle position information to improve non-collision reliability. A heterogeneous swarm of multirotors demonstrates the target locating problem, sharing information over a multicast wireless private network in a fully distributed manner to form an estimate of the signal's gradient, informing the direction of travel toward the target. The UAVs navigate around obstacles, showcasing both algorithms developed for obstacle avoidance. Each UAV performs its own target seeking and obstacle avoidance calculations in a distributed architecture, receiving position data from an OptiTrack motion capture system, illustrating the applicability of the control law to real world challenges (e.g., unsynchronized clocks among different UAVs, limited computational power, and communication latency). Experimental and theoretical results are compared. / Master of Science / In this project, a new method for locating a target using a swarm of unmanned drones in an unknown environment is developed and demonstrated. The drones measure a signal such as a beacon that is being emitted by the target of interest, sharing their measurement information with the other drones in the swarm. The magnitude of the signal increases as the drones move toward the target, allowing the drones to estimate the direction to the target by comparing their measurements with the measurements collected by other drones. While seeking the target in this manner, the drones detect obstacles that they need to avoid. An issue that arises in obstacle avoidance is that drones can get stuck in front of an obstacle if they are unable to decide which direction to travel; in this work, the decision process is managed by combining two control modes that correspond to the two direction options available, using a robust switching algorithm to select which mode to use for each obstacle. This work extends the approach used in literature to include multiple obstacles and allow obstacles to be detected dynamically, enabling the drones to navigate through an unknown environment as they locate the target. The algorithms are demonstrated on unmanned drones in the VT SpaceDrones test facility, illustrating the capabilities and effectiveness of the methods presented in a series of scenarios.

Active suspension technologies are well known for improving ride comfort and handling of ground vehicles relative to passive suspensions. They are ideally suited for mitigating single-event road obstacles. The work presented in this thesis aims to develop a structured approach for finding the peak force and bandwidth requirements of actuators for active suspensions, to mitigate single-event road obstacles. The approach is kept general to allow for application to different vehicle models, ride conditions and performance objectives. The current state-of-art in active suspensions was first evaluated. Based on these findings, the objectives of the simulation models and approach was defined. A quarter-car model was developed in Matlab to simulate the behavior of active suspensions over unilateral boundary conditions due to different road obstacle profiles. The obstacle profiles were obtained from existing standards and literature and then processed to replicate the interaction of tires on road. A least-mean-squares (LMS) algorithm for adaptive filtering, with the help of look-ahead preview was used to determine the ideal control force profile to achieve the performance objective of the active suspension. A case study was conducted to determine the requirements of the actuator in terms of bandwidth and peak force for different single-event road obstacle profiles, vehicle speeds and look-ahead preview distances. The results of the study show that the vehicle velocity and type of road obstacle have a strong influence on the required peak force and bandwidth of the actuator, while look-ahead preview will be much more important for real time controller implementation. / Master of Science

This dissertation aims to address how a change in landing substrate may change landing kinematics. To examine this possibility, mallards (Anas playtrhynchos) were used as a study species and 177 water landings were recorded through the use of two camera systems with photogrammetric capabilities. This enabled the landing trajectory and landing transition kinematics to be tracked in three dimensions. From the resulting position data three questions were pursued. Do mallards regulate landing kinematics through a ̇-constant strategy? With what kinematics do mallards land on water? Do landing kinematics respond to external factors, such as an obstacle to landing? Chapter 2 assesses the presence of a ̇-constant regulatory strategy and compares the implementation to other landing behaviors. Chapter 3 examines the variation observed in the landing kinematics of mallards, identifies the primary kinematic drivers of that variation, and detects differences in kinematic profile. Chapter 4 inspects the landing kinematics combined with the positions of all other waterfowl in the vicinity to test for the presence of obstacle avoidance behavior. / Doctor of Philosophy / Control of landing is an important ability for any flying animal. However, with the exception of perch landing, we know very little about how birds and other flyers land on a variety of different surfaces. Here, we aim to extend our knowledge in this area by focusing on how mallard ducks land on water. This dissertation addresses the following questions. Do mallards regulate landing speed and trajectory the same way as pigeons? At what speeds, angles, and postures do mallards land on water? Can mallards adjust landing behavior to avoid collisions with other birds on the water surface? Chapter 2 determines how mallards regulate landings and how it is similar and different from pigeons and several other flyers. Chapter 3 describes the speeds, angles, and postures used by mallards to land on water. In addition, this chapter finds evidence for at least two different categories of landing performed by mallards. Chapter 4 provides evidence that mallards avoid situations in which a collision with another bird is likely. However, it is unclear if this is an active choice made by the mallard or due to other circumstances related to the landing behavior. Overall, this dissertation illustrates how the landing behavior of mallards is similar to what has been documented in other animals. However there are significant differences such as higher impact speeds, and shallower angles. Both of which are likely related to the ability of water to absorb a greater amount of the impact forces than a perch or the ground would.

In the operation of unmanned vehicles, safety is a primary concern. This thesis focuses on the use of computer vision in the development of a situational awareness system that allows for safe deployment and operation of a ground robot from an unmanned aerial vehicle (UAV). A method for detecting utility cables in 3D range images is presented. This technique finds areas of an image that represent edges in 3D space, and uses the Hough transform to find those edges that take the shape of lines, indicating potential utility cables. A mission plan for stereo image capture is laid out as well for overcoming some weaknesses of the stereo vision system; this helps ensure that all utility cables in a scene are detected. In addition, the system partitions the point cloud into best-fit planes and uses these planes to locate areas of the scene that are traversable by a ground robot. Each plane's slope is tested against an acceptable value for negotiation by the robot, and the drop-off between the plane and its neighbors is examined as well. With the results of this analysis, the system locates the largest traversable region of the terrain using concepts from graph theory. The system displays this region to the human operator with the drop-offs between planes clearly indicated. The position of the robot is also simulated in this system, and real-time feedback regarding dangerous moves is issued to the operator. After a ground robot is deployed to the chosen site, the system must be capable of tracking it in real time as well. To this end, a software routine that uses ARToolkit's marker tracking capabilities is developed. This application computes the distance to the robot, as well as the horizontal distance from camera to the robot; this allows the flight controller to issue the proper commands to keep the robot centered underneath the UAV. / Master of Science

This work focuses on the development and implementation of an autonomous path planning and obstacle avoidance algorithm for an autonomous surface vehicle (ASV) in a riverine environment. The algorithm effectively handles trap situations, which occur when the river bends away from the destination. In addition, the algorithm uses real-time sensor feedback to avoid obstacles. A general global route is proposed based on an a priori shoreline map. Then, local paths are calculated considering both the a priori data and measurements received from an obstacle sensor. These paths roughly follow the global path. The algorithm was tested on an ASV equipped with basic navigational sensors and an omnidirectional camera for obstacle detection, and experimentation verified its effectiveness. / Master of Science

Miniature Autonomous Underwater Vehicles are a major area of research and development today. Because of their size and agility, they are capable of exploring and operating in smaller bodies of water in addition to areas of the ocean that would be out of reach for a larger vehicle. Being autonomous requires that the system must be capable of performing without the need for human supervision, so use of external sensors such as sonar are needed to ensure the safety of the vehicle during missions. However, since all of the onboard instrumentation and external equipment must also be miniature in size, the implementation of a small sonar system is desirable. This thesis contains a brief introduction to sound and sonar, leading into a description of the design and development of a small, inexpensive sonar altimeter. Piezoelectric material is used for transduction in the sonar system while a PIC microcontroller processes the return signals from the water. This altimeter was made to be implemented on a miniature autonomous underwater vehicle developed by the Autonomous Systems and Controls Laboratory at Virginia Polytechnic Institute. In addition to being capable of reporting ocean depths, sonar systems can be used to aid in the navigation of underwater vehicles. A constant altitude controller based on sonar data has been designed, tested, and implemented on the autonomous underwater vehicle. Possibilities for an obstacle avoidance system involving sonar are also discussed in this thesis. / Master of Science