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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.

Approved for public release; distribution in unlimited. / This thesis represents a cross Systems Command (NAVSEA/NAVAIR) developed product. The product - the Sunset Supply Base (SSB) system - provides a complete system for addressing the risks and supportability issues involved with Commercial Off the Shelf (COTS) products in Navy combat and support systems. The SSB system was implemented on three Navy combat weapon systems at various phases of the product development life cycle. The main body provides to the Program Management Offices (PMO) and other decision makers, a high level summary of performance expectations. Appendix A - The Sunset Supply Base Architecture - identifies at a high level of abstraction a collaborative architecture providing a roadmap for design and development of the SSB system. Appendix B - The Systems Engineering Development and Implementation (SEDI) plan - is a prescriptive or "How to" manual describing activities that have been used to successfully implement the SSB system. Appendix C - Business Case Analysis (BCA) - presents the data collected as a result of SEDI plan implementation then addresses the business/programmatic attributes showing the viability and value proposition possible through the SSB system. Appendix D - The Marketing Plan for the SSB system - defines methods and practices necessary to establish the SSB system as the alternative of choice. / Chemical Engineer, United States Navy / Systems Engineer, United States Navy