The use of graphical processing units (GPUs) for autonomous robots has grown recently due to their efficiency and suitability for data intensive computation. However, the current embedded GPU platforms may lack sufficient real-time capabilities for safety-critical autonomous systems. The GPU driver provides little to no control over the execution of the computational kernels and does not allow multiple kernels to execute concurrently for integrated GPUs. With the development of modern embedded platforms with integrated GPU, many embedded applications are accelerated using GPU. These applications are very computationally intensive, and they often have different criticality levels. In this thesis, we provide a software-based approach to schedule the real-world robotics application with two different scheduling policies: Fixed Priority FIFO Scheduling and Earliest Deadline First Scheduling. We implement several commonly used applications in autonomous mobile robots, such as Path Planning, Object Detection, and Depth Estimation, and improve the response time of these applications. We test our framework on NVIDIA AGX Xavier, which provides high computing power and supports eight different power modes. We measure the response times of all three applications with and without the scheduler on the NVIDIA AGX Xavier platform on different power modes, to evaluate the effectiveness of the scheduler. / Master of Science / Autonomous mobile robots for general human services have increased significantly due to ever-growing technology. The common applications of these robots include delivery services, search and rescue, hotel services, and so on. This thesis focuses on implementing the computational tasks performed by these robots as well as designing the task scheduler, to improve the overall performance of these tasks. The embedded hardware is resource-constrained with limited memory, power, and operating frequency. The use of a graphical processing unit (GPU) for executing the tasks to speed up the operation has increased with the development of the GPU programming framework. We propose a software-based GPU scheduler to execute the functions on GPU and get the best possible performance from the embedded hardware.

In recent years, unmanned aerial vehicles (UAVs) have got a boost in their applications in civilian areas like aerial photography, agriculture, communication, etc. An increasing research effort is being exerted to develop sophisticated trajectory prediction methods for UAVs for collision detection and trajectory planning. The existing techniques suffer from problems such as inadequate uncertainty quantification of predicted trajectories. This work adopts particle filters together with Löwner-John ellipsoid to approximate the highest posterior density region for trajectory prediction and uncertainty quantification. The particle filter is tuned and tested on real-world and simulated data sets and compared with the Kalman filter. A parallel computing approach for particle filter is further proposed. This parallel implementation makes the particle filter faster and more suitable for real-time online applications. / Master of Science / In recent years, unmanned aerial vehicles (UAVs) have got a boost in their applications in civilian areas like aerial photography, agriculture, communication, etc. Over the coming years, the number of UAVs will increase rapidly. As a result, the risk of mid-air collisions grows, leading to property damages and possible loss of life if a UAV collides with manned aircraft. An increasing research effort has been made to develop sophisticated trajectory prediction methods for UAVs for collision detection and trajectory planning. The existing techniques suffer from problems such as inadequate uncertainty quantification of predicted trajectories. This work adopts particle filters, a Bayesian inferencing technique for trajectory prediction. The use of minimum volume enclosing ellipsoid to approximate the highest posterior density region for prediction uncertainty quantification is also investigated. The particle filter is tuned and tested on real-world and simulated data sets and compared with the Kalman filter. A parallel computing approach for particle filter is further proposed. This parallel implementation makes the particle filter faster and more suitable for real-time online applications.

Automotive manufacturers are facing increasing pressure from legislative bodies and consumers to reduce fuel consumption and greenhouse gas emissions of vehicles. This has led to many automotive manufacturers starting production of Plug-in Hybrid Electric Vehicles (PHEV's) and Battery Electric Vehicles (BEV's). Another method that helps to reduce the environmental effect of transportation is EcoRouting. The standard Global Positioning System (GPS) navigation offers route alternatives between user specified origin and destination. This technology provides multiple routes to the user and focuses on reducing the travel time to reach to the destination. EcoRouting is the method to determine a route that minimizes vehicle energy consumption, unlike traditional routing methods that minimize travel time. An EcoRouting system has been developed as a part of this thesis that takes in information such as speed limits, the number of stop lights, and the road grade to calculate the energy consumption of a vehicle along a route. A synthesis methodology is introduced that takes into consideration the distance between the origin and destination, the acceleration rate of the vehicle, cruise speed and jerk rate as inputs to simulate driver behavior on a given route. A new approach is presented in this thesis that weighs the energy consumption for different routes and chooses the route with the least energy consumption, subject to a constraint on travel time. A cost function for quantifying the effect of travel time is introduced that assists in choosing the EcoRoute with an acceptable limit on the travel time required to reach the destination. The analysis of the EcoRouting system with minimum number of conditional stops and maximum number of conditional stops is done in this thesis. The effect on energy consumption with the presence and absence of road-grade information along a route is also studied. A sensitivity study is performed to observe the change in energy consumption of the vehicle with a change in acceleration rates and road grade. Three routing scenarios are presented in this thesis to demonstrate the functionality of EcoRouting. The EcoRouting model presented in this thesis is also validated against an external EcoRouting research paper and the energy consumption along three routes is calculated. The EcoRoute solution is found to vary with the information given to the variable acceleration rate model. The synthesis and the results that are obtained show that parameters such as acceleration, deceleration, and road grade affect the overall energy consumption of a vehicle and are helpful in determining the EcoRoute. / Master of Science

This work addresses applications of search theory where a mobile search agent seeks to find an unknown number of stationary targets randomly distributed in a bounded search domain. We assume that the search mission is subject to a time or distance constraint, and that the local environmental conditions affect sensor performance. Because the environment varies by location, the effectiveness of the search sensor also varies by location. Our contribution to search theory includes new decision-theoretic approaches for generating optimal search plans in the presence of false alarms and uncertain environmental variability. We also formally define the value of environmental information for improving the effectiveness of a search mission, and we develop methods for optimal deployment of assets that can acquire environmental information in order to improve search effectiveness. Finally, we extend our research to the case of multiple cooperating search agents. For the case that inter-agent communication is severely bandwidth-limited, such as in subsea applications, we propose a method for assessing the expected value of inter-agent communication relative to joint search effectiveness. Our results lead to a method for determining when search agents should communicate. Our contributions to search theory address important applications that range from subsea mine-hunting to post-disaster search and rescue applications. / PHD / We address search applications where a mobile search agent seeks to find an unknown number of stationary targets randomly distributed in a bounded search domain. The search agent is equipped with a search sensor that detects the targets at a location. Sensor measurements are often imperfect due to possible missed detections and false alarms. We also consider that the local environmental conditions affect the quality of the data acquired from the search sensor. For instance, if we are searching for a target that has a rocky shape, we expect that it will be harder to find that target in a rocky environment. We consider that the search mission is subject to a time or distance constraint, and thus, search can be performed on only a subset of locations. Our goal in this study is to formally determine where to acquire the search measurements so that the search effectiveness can be maximized. We also formally define the value of acquiring environmental information for improving the effectiveness of a search mission, and we develop methods for optimal deployment of assets that can acquire environmental information in order to improve search effectiveness. Finally, we address the cases where multiple search assets collaboratively search the environment and they can communicate their local information with each other. We are particularly interested in determining when a vehicle should communicate with another vehicle so that the joint search effectiveness can be improved. Our contributions to search theory address important applications that range from subsea mine-hunting to post-disaster search and rescue applications.

Autonomous robot missions in unknown environments are challenging. In many cases, the systems involved are unable to use a priori information about the scene (e.g. road maps). This is especially true in disaster response scenarios, where existing maps are now out of date. Areas without GPS are another concern, especially when the involved systems are tasked with navigating a path planned by a remote base station. Scene understanding via robots' perception data (e.g. images) can greatly assist in overcoming these challenges. This dissertation makes three contributions that help overcome these challenges, where there is a focus on the application of autonomously searching for radiation sources with unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV) in unknown and unstructured environments. The three main contributions of this dissertation are: (1) An approach to overcome the challenges associated with simultaneously trying to understand 2D and 3D information about the environment. (2) Algorithms and experiments involving scene understanding for real-world autonomous search tasks. The experiments involve a UAV and a UGV searching for potentially hazardous sources of radiation is an unknown environment. (3) An approach to the registration of a UGV in areas without GPS using 2D image data and 3D data, where localization is performed in an overhead map generated from imagery captured in the air. / Ph. D.

We study the problem of planning a tour for an energy-limited Unmanned Aerial Vehicle (UAV) to visit a set of sites in the least amount of time. We envision scenarios where the UAV can be recharged along the way either by landing on stationary recharging stations or on Unmanned Ground Vehicles (UGVs) acting as mobile recharging stations. This leads to a new variant of the Traveling Salesperson Problem (TSP) with mobile recharging stations. We present an algorithm that finds not only the order in which to visit the sites but also when and where to land on the charging stations to recharge. Our algorithm plans tours for the UGVs as well as determines the best locations to place stationary charging stations. While the problems we study are NP-Hard, we present a practical solution using Generalized TSP that finds the optimal solution. If the UGVs are slower, the algorithm also finds the minimum number of UGVs required to support the UAV mission such that the UAV is not required to wait for the UGV. We present a calibration routine to identify parameters that are needed for our algorithm as well as simulation results that show the running time is acceptable for reasonably sized instances in practice. We evaluate the performance of our algorithm through simulations and proof-of-concept experiments with a fully autonomous system of one UAV and UGV. / Master of Science / Commercially available Unmanned Aerial Vehicles (UAVs), especially multi-rotor aircrafts, have a flight time of less than 30 minutes. However many UAV applications, such as surveillance, package delivery, and infrastructure monitoring, require much longer flight times. To address this problem, we present a system in which an Unmanned Ground Vehicle (UGV) can recharge the UAV during deployments. This thesis studies the problem of finding when, where, and how much to recharge the battery. We also allow for the UGV to recharge while moving from one site to another. We present an algorithm that finds the paths for the UAV and UGV to visit a set of points of interest in the least time possible. We also present algorithms for cases when the UGV is slower than the UAV, and more than one UGV may be required. We evaluate our algorithms through simulations and proof-of-concept experiments.

In recent years, significant progress has been made in improving the performance of unmanned air vehicles in terms of aerodynamic performance, endurance, autonomy, and the capability of on-board sensor packages. UAVs are now a vital part of both military actions and scientific research efforts. One of the newest classes of UAV is the high altitude long endurance or HALE UAV. This thesis considers the high-level control problem for a unique HALE mission involving cooperative solar powered UAVs. Specifically addressed is energy efficient path planning for vehicles that physically link together in flight to form a larger, more energy efficient HALE vehicle. Energy efficient docking is developed for the case of multiple vehicles at high altitude with negligible wind. The analysis considers a vehicle governed by a kinematic motion model with bounded turn rate in planar constant altitude flight. Docking is demonstrated using a platform-in-the-loop simulator which was developed to allow virtual networked vehicles to perform decentralized path planning and estimation of all vehicle states. Vehicle behavior is governed by a status which is commanded by a master computer and communication between vehicles is intermittent depending on each vehicle's assessment of situational awareness. Docking results in a larger vehicle that consumes energy at 21% of the rate of an individual vehicle and increases vehicle range by a factor of three without considering solar recharging. / Master of Science

Research in the area of autonomous mobile robots has increased over the last several years. Autonomous mobile robots are now being used in a wide variety of applications, including nuclear plant maintenance, interplanetary exploration, military missions and smart highway systems. This thesis is a survey of recent publications, 1990-1996, of wheeled autonomous mobile robots for harsh environments. Various sensing, navigation, and motion control strategies commonly used on autonomous mobile robots are compared. The integration of all three systems in a system architecture is also presented. Following a general discussion of autonomous mobile robot technology, vehicles that have entered the Unmanned Ground Robotics Competition are presented as a focused study of the application of this broad field of research. / Master of Science

With many robots now being developed for indoor settings, an autonomous mobile robot should be capable of reaching multiple targets within a dense, complex environment while maintaining the optimal path taken and avoiding all obstacles. In this thesis, we propose a global path planning algorithm that uses data created from a Generalized Voronoi Diagram (GVD) to traverse complex environments. The global route is made from the skeleton of the diagram that ensures the avoidance of static obstacles. Once this route is determined, dynamic programming is used to determine the optimal route to reach each target location while safely navigating obstacles in the map. A Dynamic Window Approach (DWA) local path planner is integrated into the algorithm to provide collision-free navigation in case of unexpected or dynamic obstacles that may be encountered during traversal. Our comprehensive simulations and comparative analyses highlight the proposed model’s robustness, demonstrating its ability to efficiently navigate to multiple targets through the shortest routes while adeptly circumventing obstacles. These findings validate the model’s effectiveness, confirming its superior performance in complex multi-target navigation scenarios and its capability to dynamically adapt to unforeseen obstacles, thereby illustrating a significant advancement in the field of autonomous indoor navigation.

The realm of autonomous robotics has seen impressive advancements in recent years, with robots taking on essential roles in various sectors, including disaster response, environmental monitoring, agriculture, and healthcare. As these highly intelligent machines continue to integrate into our daily lives, the pressing imperative is to elevate and refine their performance, enabling them to adeptly manage complex tasks with remarkable efficiency, adaptability, and keen decision-making abilities, all while prioritizing safety-aware navigation, mapping, and control systems. Ensuring the safety-awareness of these robotic systems is of paramount importance in their development and deployment. In this research, bio-inspired neural networks, nature-inspired intelligence, deep learning, heuristic algorithm and optimization techniques are developed for safety-aware autonomous robots navigation, mapping and control. A bio-inspired neural network (BNN) local navigator coupled with dynamic moving windows (DMW) is developed in this research to enhance obstacle avoidance and refines safe trajectories. A hybrid model is proposed to optimize trajectory of the global path of a mobile robot that maintains a safe distance from obstacles using a graph-based search algorithm associated with an improved seagull optimization algorithm (iSOA). A Bat-Pigeon algorithm (BPA) is proposed to undertake adjustable speed navigation of autonomous vehicles in light of object detection for safety-aware vehicle path planning, which can automatically adjust the speed in different road conditions. In order to perform effective collision avoidance in multi-robot task allocation, a spatial dislocation scheme is developed by introduction of an additional dimension for UAVs at different altitudes, whereas UAVs avoid collision at the same altitude using a proposed velocity profile paradigm. A multi-layer robot navigation system is developed to explore row-based environment. A directed coverage path planning (DCPP) fused with an informative planning protocol (IPP) method is proposed to efficiently and safely search the entire workspace. A human-autonomy teaming strategy is proposed to facilitate cooperation between autonomous robots and human expertise for safe navigation to desired areas. Simulation, comparison studies and on-going experimental results of optimization algorithms applied for autonomous robot systems demonstrate their effectiveness, efficiency and robustness of the proposed methodologies.