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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Item 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.Item Autonomous Robot Navigation in Challenging Real-World Indoor and Outdoor Environments(2024) Sathyamoorthy, Adarsh Jagan; Manocha, Dr. Dinesh; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The use of autonomous ground robots for various indoor and outdoor applications has burgeoned over the years. In indoor settings, their applications range from waiters in hotels, helpers in hospitals, cleaners in airports and malls, transporters of goods in warehouses, surveillance robots, etc. In unstructured outdoor settings, they have been used for exploration in off-road environments, search and rescue, package delivery, etc. To successfully accomplish these tasks, robots must overcome several challenges and navigate to their goal. In this dissertation, we present several novel algorithms for learning-based perception combined with model-based autonomous navigation in real-world indoor and outdoor environments. The presented algorithms address the problems of avoiding collisions in dense crowds (< 1 to 2 persons/sq.meter), reducing the occurrence of the freezing robot problem, navigating in a socially compliant manner without being obtrusive to humans, and avoiding transparent obstacles in indoor settings. In outdoor environments, they address challenges in estimating the traversabilityof off-road terrains and vegetation, and understanding explicit social rules (e.g. crossing streets using crosswalks). The presented algorithms are designed to operate in real-time using the limited computational capabilities on-board real wheeled and legged robots such as the Turtlebot 2, Clearpath Husky, and Boston Dynamics Spot. Furthermore, the algorithms have been evaluated in real-world environments with dense crowds, transparent obstacles, off-road terrains, and vegetation such as tall grass, bushes, trees, etc. They have demonstrated significant improvements in terms of several metrics such as increasing success rates by at least 50% (robot avoids collisions and reaches its goal), lowering freezing rates by at least 80% (robot does not halt/oscillate indefinitely), increasing pedestrian friendliness up to 100% higher, reducing vibrations experienced in off-road terrains by up to 22%, etc over the state-of-the-art algorithms in various test scenarios. The first part of this dissertation deals with socially-compliant navigation approaches for crowded indoor environments. The initial methods focus on collision avoidance, handling the freezing robot problem in crowds of varying densities by tracking individual pedestrians, and modeling regions the robot must avoid based on their future positions. Subsequent works expand on these models by considering pedestrian group behaviors. The next part of this dissertation focuses on outdoor navigation methods that estimate the traversability of various terrains, and complex vegetation (e.g. pliable obstacles such as tall grass) using perception inputs to navigate on safe, and stable terrains. The final part of the dissertation elaborates on methods designed for detecting and navigating complex obstacles in indoor and outdoor environments. It also explores a technique leveraging recent advancements in large vision language models for navigation in both settings. All proposed methods have been implemented and evaluated on real wheeled and legged robots.Item Bioinspired Robust Underwater Behaviors Using Fluid Flow Sensing(2017) Ranganathan, Badri Narayanan; Humbert, Sean; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The lateral line sense organ in fish detects fluid flow around its body, and is used to perform a wide variety of behaviors such as rheotaxis, wall-following, prey detection, and obstacle and predator avoidance. Currently there are no equivalent engineering analogues that can sense fluid flow perturbation to determine location of obstacles and demonstrate closed loop obstacle avoidance. In this dissertation we examine the potential and limitations of this sensor system with respect to obstacle detection, avoidance and rheotaxis. This dissertation presents the development of a novel bioinspired flow-based perception scheme for small and wide-field objects, design and development of a strain sensor system and a robust controller for closed loop demonstration of rheotaxis and small and wide field object detection and avoidance. Potential flow based models are developed for the above mentioned problems of interest. As the modeling technique is approximate, the uncertainties due to modeling and effect of rotation rate are accounted for and used in the synthesis of a robust H$_\infty$ control system. The perturbation signals are spatially decomposed using wide and small-field integration techniques to arrive at information regarding objects in the environment. A high-fidelity, computational fluid dynamic closed-loop simulation is carried out by interfacing control codes with an off-the-shelf software to demonstrate behaviors of rheotaxis, wall-following, tunnel centering and unstructured wide-field obstacle avoidance. A bio-inspired hair array sensor and its corresponding signal conditioning electronics were developed for detecting flow perturbations related to the behaviors of interest. The sensors that were manufactured were strain based and involved the use of micro and macro fabrication approaches. An instrumentation amplifier-based system was developed for signal conditioning. The hair array sensors along with the signal conditioning electronics weighed about 10 gms, which allows it to be easily carried on small scale fish robots. These sensors were integrated onto an airfoil-shaped robot and perturbation signals due to the motion of the robot near a wall and cylindrical objects were obtained and analyzed. The signals that have been measured by the sensor array help in quantifying the magnitude and structure of perturbation that is observed due to interaction with objects, and establishes requirements for sensor design for deployment on autonomous underwater vehicles. Closed loop behavior of rheotaxis was demonstrated in a flow tank.