A. James Clark School of Engineering

Permanent URI for this communityhttp://hdl.handle.net/1903/1654

The collections in this community comprise faculty research works, as well as graduate theses and dissertations.

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    APPLIED AERIAL ROBOTICS FOR LONG RANGE AUTONOMY AND ADVANCED PERCEPTION
    (2024) Cui, Wei; Paley, Derek A; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This dissertation addresses the challenges of conducting autonomous long-distance operations in settings where communication is restricted or unavailable. It involves the development of aerial autonomy software, ground station user interface, and simulation tools. Field experiments are conducted to assess the real-world performance and scalability of the developed autonomous multi-vehicle systems. A search and revisit framework involving multiple UAS engaged in expansive area exploration has been developed. By employing the ARL MAVericks autonomy stack, we have devised three system designs with improving levels of autonomy. This approach is effective in developing autonomous system capabilities for extended-range missions, enhancing effectiveness in reconnaissance, search, and rescue missions. Furthermore, the dissertation introduces an innovative application of enhanced target detection and localization techniques tailored specifically for small UAS deployment. Neural network fine-tuning and AprilTag detector selection are carefully conducted. Augmented by a meticulously designed workflow for performance evaluation and validation, our approach aims to improve the precision of target detection and localization using a single RGB camera module. Additionally, the dissertation presents the implementation of a specialized ground control user interface. Functioning as a centralized command center, the user interface facilitates real-time monitoring and coordination of heterogeneous aerial and ground robotic platforms engaged in collaborative search missions. By streamlining air-ground coordination and human-robot interaction, the custom user interface optimizes the collective capabilities of diverse aerial and ground robotic platforms, enhancing overall mission effectiveness. The experimental results from multi-vehicle autonomous search missions, evaluating centralized and decentralized control in beyond visual line of sight scenarios, are presented, proving the efficacy of the search and revisit framework operating in real-world scenarios. Finally, the dissertation covers the design and implementation of a resilient network link tailored for robotic platforms operating in environments with limited bandwidth. This essential infrastructure enhancement is devised to overcome communication constraints, ensuring reliable data exchange, and strengthening the resilience of autonomous systems in bandwidth-limited environments.
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    Assured Autonomy in Multiagent Systems with Safe Learning
    (2022) Fiaz, Usman Amin; Baras, John S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Autonomous multiagent systems is an area that is currently receiving increasing attention in the communities of robotics, control systems, and machine learning (ML) and artificial intelligence (AI). It is evident today, how autonomous robots and vehicles can help us shape our future. Teams of robots are being used to help identify and rescue survivors in case of a natural disaster for instance. There we understand that we are talking minutes and seconds that can decide whether you can save a person's life or not. This example portrays not only the value of safety but also the significance of time, in planning complex missions with autonomous agents. This thesis aims to develop a generic, composable framework for a multiagent system (of robots or vehicles), which can safely carry out time-critical missions in a distributed and autonomous fashion. The goal is to provide formal guarantees on both safety and finite-time mission completion in real time, thus, to answer the question: “how trustworthy is the autonomy of a multi-robot system in a complex mission?” We refer to this notion of autonomy in multiagent systems as assured or trusted autonomy, which is currently a very sought-after area of research, thanks to its enormous applications in autonomous driving for instance. There are two interconnected components of this thesis. In the first part, using tools from control theory (optimal control), formal methods (temporal logic and hybrid automata), and optimization (mixed-integer programming), we propose multiple variants of (almost) realtime planning algorithms, which provide formal guarantees on safety and finite-time mission completion for a multiagent system in a complex mission. Our proposed framework is hybrid, distributed, and inherently composable, as it uses a divide-and-conquer approach for planning a complex mission, by breaking it down into several sub-tasks. This approach enables us to implement the resulting algorithms on robots with limited computational power, while still achieving close to realtime performance. We validate the efficacy of our methods on multiple use cases such as autonomous search and rescue with a team of unmanned aerial vehicles (UAVs) and ground robots, autonomous aerial grasping and navigation, UAV-based surveillance, and UAV-based inspection tasks in industrial environments. In the second part, our goal is to translate and adapt these developed algorithms to safely learn actions and policies for robots in dynamic environments, so that they can accomplish their mission even in the presence of uncertainty. To accomplish this goal, we introduce the ideas of self-monitoring and self-correction for agents using hybrid automata theory and model predictive control (MPC). Self-monitoring and self-correction refer to the problems in autonomy where the autonomous agents monitor their performance, detect deviations from normal or expected behavior, and learn to adjust both the description of their mission/task and their performance online, to maintain the expected behavior and performance. In this setting, we propose a formal and composable notion of safety and adaptation for autonomous multiagent systems, which we refer to as safe learning. We revisit one of the earlier use cases to demonstrate the capabilities of our approach for a team of autonomous UAVs in a surveillance and search and rescue mission scenario. Despite portraying results mainly for UAVs in this thesis, we argue that the proposed planning framework is transferable to any team of autonomous agents, under some realistic assumptions. We hope that this research will serve several modern applications of public interest, such as autopilots and flight controllers, autonomous driving systems (ADS), autonomous UAV missions such as aerial grasping and package delivery with drones etc., by improving upon the existing safety of their autonomous operation.
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    Planning for Autonomous Operation of Unmanned Surface Vehicles
    (2016) Shah, Brual; Gupta, Satyandra K; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The growing variety and complexity of marine research and application oriented tasks requires unmanned surface vehicles (USVs) to operate fully autonomously over long time horizons even in environments with significant civilian traffic. The autonomous operations of the USV over long time horizons requires a path planner to compute paths over long distances in complex marine environments consisting of hundreds of islands of complex shapes. The available free space in marine environment changes over time as a result of tides, environmental restrictions, and weather. Secondly, the maximum velocity and energy consumption of the USV is significantly influenced by the fluid medium flows such as strong currents. Finally, the USV have to operate in an unfamiliar, unstructured marine environment with obstacles of variable dimensions, shapes, and motion dynamics such as other unmanned surface vehicles, civilian boats, shorelines, or docks poses numerous planning challenges. The proposed Ph.D. dissertation explores the above mentioned problems by developing computationally efficient path and trajectory planning algorithms that enables the long term autonomous operation of the USVs. We have developed a lattice-based 5D trajectory planner for the USVs operating in the environment with the congested civilian traffic. The planner estimates collision risk and reasons about the availability of contingency maneuvers to counteract unpredictable behaviors of civilian vessels. Secondly, we present a computationally efficient and optimal algorithm for long distance path planning in complex marine environments using A* search on visibility graphs defined over quad trees. Finally, we present an A* based path planning algorithm with newly developed admissible heuristics for computing energy efficient paths in environment with significant fluid flows. The effectiveness of the planning algorithms is demonstrated in the simulation environments by using systems identified dynamics model of the wave amplitude modular vessel (WAM-V) USV14.