AUTONOMOUS ROBOTICS THROUGH DETERMINISTIC (NON-ITERATIVE) ARTIFICIAL INTELLIGENCE

Artificial intelligence has achieved remarkable success through optimization-based learning, probabilistic reasoning, deep neural networks, and increasingly complex computational architectures. Despite these advances, many autonomous robotic systems continue to rely on iterative search, repeated optimization, and computationally expensive decision-making procedures.

This book presents a complementary deterministic (non-iterative) perspective for autonomous robotics. Rather than viewing perception, decision-making, planning, and autonomous behavior primarily as optimization problems, it investigates whether many robotic tasks can be formulated as processes of structural compatibility, admissibility evaluation, equilibrium discovery, and progressive elimination of incompatible alternatives.

The proposed framework develops deterministic methodologies for object recognition, environmental representation, autonomous action selection, multi-agent coordination, goal selection, robotic continuity, and hardware-oriented parallel filtering architectures. Throughout the book, compatible solutions emerge through constraint-governed elimination rather than exhaustive search, providing an alternative computational interpretation of intelligent robotic behavior.

The objective of this work is not to replace existing machine learning, reinforcement learning, probabilistic robotics, or optimization theory. Instead, it offers a complementary framework that emphasizes interpretability, reproducibility, computational efficiency, operational stability, and energy-conscious autonomous decision-making.

Intended for researchers, graduate students, engineers, and practitioners in robotics and artificial intelligence, this book introduces a unified deterministic framework that may stimulate further research into sustainable, computationally efficient, and structurally interpretable autonomous robotic systems.