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Research Paper | Computer Science & Engineering | India | Volume 13 Issue 8, August 2024 | Rating: 3.4 / 10
Autonomous Systems in Unstructured Environments: AI Approaches for Robust Operation
Mohammed Saleem Sultan [8] | Mohammed Shahid Sultan [8]
Abstract: Autonomous systems are increasingly being deployed in various environments, but their effectiveness is often limited in unstructured settings, where traditional rule-based approaches struggle with unpredictability and variability. Unstructured environments such as off-road terrains, urban areas with unpredictable human behavior, and disaster-stricken regions present unique challenges that require advanced AI techniques for robust operation. This paper investigates the application of several AI methodologies, including reinforcement learning, deep learning, and sensor fusion, to enhance autonomous systems' adaptability and decision-making capabilities in these complex environments. Through comprehensive literature review and a series of experimental case studies, we demonstrate how these AI techniques enable autonomous systems to effectively perceive, navigate, and respond to dynamic and uncertain conditions. Our experiments in simulated and real-world environments show significant improvements in system robustness and operational efficiency. We discuss limitations of current approaches and propose future research directions, including the development of more sophisticated AI models, the integration of diverse sensor technologies, and the ethical implications of deploying autonomous systems in critical, unstructured environments. This version provides more context on the types of unstructured environments considered, the specific AI techniques analyzed, and the overall contribution of the research, offering a fuller picture of what the paper covers.
Keywords: Autonomous systems, Unstructured environments, Artificial intelligence, Reinforcement learning, Sensor fusion, Path planning, Robotics, AI-driven navigation, Adaptive algorithms, Autonomous vehicles
Edition: Volume 13 Issue 8, August 2024,
Pages: 1348 - 1355