This work discusses the algorithms and implementation of a multi-robot system for locating radioactive sources. The estimation algorithm presented in this work is able to fuse measurements collected by γ-ray spectrometers carried by an unmanned aerial and unmanned ground vehicle into a single consistent estimate of the probability distribution over the position of a point source in an environment. By constructing a set of hypotheses on the position of the point source, this method converts a non-linear problem into many independent linear ones. Since the underlying model is probabilistic, candidate paths may be evaluated by their expected reduction in uncertainty, allowing the algorithm to select good paths for vehicles to take. An initial hardware test conducted at Savannah River National Laboratory served as a proof of concept and demonstrated that the algorithm successfully locates a radioactive source in the environment, and moves the vehicle to that location. This approach also demonstrated the capability to utilize radiation data collected from an unmanned aerial vehicle to aid the ground vehicle’s exploration. Subsequent numerical experiments characterized the performance of several reward functions and different exploration algorithms in scenarios covering a range of source strengths and region sizes. These experiments demonstrated the improved performance of planning-based algorithms over the myopic method initially tested in the hardware experiments. / Doctor of Philosophy / This work discusses the use of unmanned aerial and ground vehicles to autonomously locate radioactive materials. Using radiation detectors onboard each vehicle, they are commanded to search the environment using a method that incorporates measurements as they are collected. A mathematical model allows measurements taken from different vehicles in different positions to be combined together. This approach decreases the time required to locate sources by using previously collected measurements to improve the quality of later measurements. This approach also provides a best estimate of the location of a source as data is collected. This algorithm was tested in an experiment conducted at Savannah River National Laboratory. Further numerical experiments were conducted testing different reward functions and exploration algorithms.

In this thesis, we build a robust end-to-end pipeline for the localization of multiple drones in a GPS-denied environment. This pipeline would help us with cooperative formation control, autonomous delivery, search and rescue operations etc. To achieve this we integrate a custom trained YOLO (You Only Look Once) object detection network, for drones, with the ZED2 stereo camera system. With the help of this sensor we obtain a relative vector from the left camera to that drone. After calibrating it from the left camera to that drone's center of mass, we then estimate the location of all the drones in the leader drone's frame of reference. We do this by solving the localization problem with least squares estimation and thus acquire the location of the follower drone's in the leader drone's frame of reference. We present the results with the stereo camera system followed by simulations run in AirSim to verify the precision of our pipeline. / Master of Science / In the recent years, technologies like Deep Learning and Machine Learning have seen many rapid developments. This has lead to the rise of fields such as autonomous drones and their application in fields such as bridge inspection, search and rescue operations, disaster management relief, agriculture, real estate etc. Since GPS is a highly unreliable sensor, we need an alternate method to be able to localize the drones in various environments in real time. In this thesis, we integrate a robust drone detection neural network with a camera which estimates the location. We then use this data to get the relative location of all the follower drones from the leader drone. We run experiments with the camera and in a simulator to show the accuracy of our results.

This thesis describes the development of a new zero-turn-radius (ZTR) differentially driven robotic vehicle hereinafter referred to as NEVEL. The primary objective of this work was to develop a device that could be used as a test-bed for continued autonomous vehicle research at Virginia Tech while meeting the entry requirements of the Annual International Unmanned Ground Robotics Competition. In developing NEVEL, consideration was given to the vehicle's mechanical and electrical design, sensing and computing systems, and navigation strategy. Each of these areas was addressed individually, but always within the context of optimal integration to produce the best overall vehicle system. A constraint that directed much of the design process was the desire to integrate industrially available and proven components rather than creating custom designed systems. This thesis also includes a review of the relevant literature as it pertains to both subsystem and overall vehicle design. NEVEL, the vehicle that was created from this research effort, is novel in several respects. It is one of the few true embodiments of a fully functioning, three-wheel, differential drive autonomous vehicle. Several previous studies have developed this concept for indoor applications, but none has resulted in a working test-bed that can be applied to an unstructured, outdoor environment. NEVEL also appears to be one of the few autonomous vehicle systems to fully incorporate a commercially available laser range finder. These features alone would make NEVEL a useful platform for continued research. In addition, however, by using common, off-the-shelf components and a personal computer platform for all computation and control, NEVEL has been created to facilitate testing of new navigation and control strategies. As testimony to the success of this design, NEVEL was recognized at the Sixth Annual International Unmanned Ground Robotics Competition as the best overall design. / Master of Science

<div>The current technologies employed for autonomous driving provide tremendous performance and results, but the technology itself is far from mature and relatively expensive. Some of the most commonly used components for autonomous driving include LiDAR, cameras, radar, and ultrasonic sensors. Sensors like such are usually high-priced and often require a tremendous amount of computational power in order to process the gathered data. Many car manufacturers consider cameras to be a low-cost alternative to some other costly sensors, but camera based sensors alone are prone to fatal perception errors. In many cases, adverse weather and night-time conditions hinder the performance of some vision based sensors. In order for a sensor to be a reliable source of data, the difference between actual data values and measured or perceived values should be as low as possible. Lowering the number of sensors used provides more economic freedom to invest in the reliability of the components used. This thesis provides an alternative approach to the current autonomous driving methodologies by utilizing acoustic signatures of moving objects. This approach makes use of a microphone array to collect and process acoustic signatures captured for simultaneous localization and mapping (SLAM). Rather than using numerous sensors to gather information about the surroundings that are beyond the reach of the user, this method investigates the benefits of considering the sound waves of different objects around the host vehicle for SLAM. The components used in this model are cost-efficient and generate data that is easy to process without requiring high processing power. The results prove that there are benefits in pursuing this approach in terms of cost efficiency and low computational power. The functionality of the model is demonstrated using MATLAB for data collection and testing.</div>

There is a significant amount of ongoing research on developing multi-agent algorithms for mobile robots. Moving those algorithms beyond simulation and into the real world requires multi-robot testbeds. However, there is currently no easily accessible source of information for guiding the creation of such a testbed. In this thesis, we describe the process of creating a testbed at Purdue University involving a set of unmanned aerial vehicles (UAVs). We discuss the components of the testbed, including the software that is used to interface with the UAVs. We also describe the challenges that we faced during the setup process, and evaluate the UAV platforms that we are using. Finally, we demonstrate the implementation of a multi-agent task allocation algorithm on our testbed.

Deploying robots in unknown and complex areas for inspection tasks is becoming a real need for various application scenarios. Recently, there has been an increasing interest to develop and use autonomous aerial robots in environments such as urban voids and subterranean mine tunnels, aiming to decrease the human presence in dangerous or inaccessible areas. These areas are characterized by complete darkness and narrow tunnels, where the ground can often be rough and not traversible for mobile vehicles, thus the developments focus on Micro Aerial Vehicles (MAVs). MAVs are mechanically simple and agile platforms that can navigate through cluttered areas and have the potential to perform complex exploration tasks when equipped with proper onboard sensors. One of the key milestones in the development of autonomous robots is self-exploration. The definition of self-exploration according to [7] is "the act of moving through an unknown environment while building a map that can be used for subsequent navigation". By reaching this milestone, robots would be freed from the limitation of requiring already existing maps for navigation. In this thesis, a frontier-based exploration algorithm is established and evaluated to understand how such method could be used to reach the self-exploration milestone. By marking the border between what is known and unknown the method is able to determine the next desired position for the robot to expand the map. The resulting algorithm, together with a path planning method and 3-dimensional mapping framework, the method was tested and examined in simulated environments with different levels of complexity.

No description available.

Indiana University-Purdue University Indianapolis (IUPUI) / The current technologies employed for autonomous driving provide tremendous performance and results, but the technology itself is far from mature and relatively expensive. Some of the most commonly used components for autonomous driving include LiDAR, cameras, radar, and ultrasonic sensors. Sensors like such are usually high-priced and often require a tremendous amount of computational power in order to process the gathered data. Many car manufacturers consider cameras to be a low-cost alternative to some other costly sensors, but camera based sensors alone are prone to fatal perception errors. In many cases, adverse weather and night-time conditions hinder the performance of some vision based sensors. In order for a sensor to be a reliable source of data, the difference between actual data values and measured or perceived values should be as low as possible. Lowering the number of sensors used provides more economic freedom to invest in the reliability of the components used. This thesis provides an alternative approach to the current autonomous driving methodologies by utilizing acoustic signatures of moving objects. This approach makes use of a microphone array to collect and process acoustic signatures captured for simultaneous localization and mapping (SLAM). Rather than using numerous sensors to gather information about the surroundings that are beyond the reach of the user, this method investigates the benefits of considering the sound waves of different objects around the host vehicle for SLAM. The components used in this model are cost-efficient and generate data that is easy to process without requiring high processing power. The results prove that there are benefits in pursuing this approach in terms of cost efficiency and low computational power. The functionality of the model is demonstrated using MATLAB for data collection and testing.

No description available.

Self-driving cars used to be a concept of a future society. However, through years of research, testing, and dedication they are becoming a modern day reality. To further expand research and testing capabilities in the field of autonomous vehicles, an Autonomous Vehicle Research Platform (AVRP) can be developed. The purpose of an AVRP is to provide researchers with an autonomous ground vehicle testing platform they can outfit with sensors and equipment to meet their specific research needs. The platform will give researchers the capabilities to test algorithms, new sensors, navigation, new technologies, etc. that they believe would help advance autonomous vehicles. When their testing is done, their equipment can be removed so the next researcher can utilize the platform. The scope of this thesis is to develop the operational specifications for an AVRP that can operate at level 4 autonomy. These specifications include navigation and sensing hardware, such as LIDAR, radar, ultrasonic, cameras, and important specifications that pertain to using each, as well as a review of optimal mounting locations. It will also present benchmarks for computing, design specs for power and communication buses, and modifications for universal mounting racks. / Master of Science