Intelligent robotic navigation has become an important research area for autonomous systems operating in dynamic and uncertain environments. Before 2019, advancements in robotics, artificial intelligence, embedded systems, and wireless communication improved autonomous robots used in healthcare, agriculture, transportation, military, and service applications. Traditional single-sensor systems faced challenges such as low localization accuracy and poor environmental perception. To address these issues, Hybrid Wireless Sensor Networks (HWSNs) combined sensors like LiDAR, GPS, IMUs, cameras, ultrasonic sensors, and RFID to enhance navigation, obstacle detection, mapping, and reliability. Research focused on sensor fusion, SLAM, Kalman filtering, fuzzy logic, neural networks, machine learning, and real-time obstacle avoidance. The proposed hybrid framework supports accurate localization, autonomous mapping, and efficient path planning for indoor and outdoor environments. Experimental results show that hybrid sensor systems improve navigation accuracy, reduce localization errors, and enhance obstacle detection efficiency. The study concludes that hybrid sensor networks are essential for future autonomous robotic systems, with future research focusing on deep reinforcement learning, cloud robotics, edge computing, cognitive navigation, and IoRT-based architectures.
Hiroshi Tanaka, Yuki Nakamura· International Journal of Int...· 0 citations
Intelligent robot navigation in dynamic environments remains one of the most challenging problems in autonomous robotics because navigation systems must continuously perceive environmental changes, predict moving obstacles, and generate safe trajectories while maintaining operational efficiency. Traditional navigation approaches, including graph-based path planning, rule-based obstacle avoidance, and probabilistic localization, often exhibit limited adaptability when environmental conditions change rapidly. Recent advances in artificial intelligence, particularly Deep Reinforcement Learning (DRL), have enabled autonomous robots to learn navigation policies directly from environmental interactions without relying exclusively on handcrafted rules. DRL integrates perception, decision-making, and continuous learning into a unified framework, making it particularly suitable for complex and uncertain environments such as warehouses, hospitals, manufacturing plants, urban streets, and disaster-response scenarios.
This research-review paper presents a comprehensive analysis of Deep Reinforcement Learning-based intelligent robot navigation with emphasis on dynamic obstacle avoidance, adaptive path planning, perception integration, reward optimization, and policy learning. The paper synthesizes contemporary studies related to artificial intelligence, semantic decision intelligence, cyber-physical systems, cloud intelligence, autonomous optimization, and intelligent infrastructure to establish a multidisciplinary understanding of modern robotic navigation. Particular attention is devoted to semantic AI-enabled decision intelligence, which enhances contextual understanding during navigation and improves policy robustness in continuously evolving environments (Goyal, 2025).
A conceptual DRL navigation framework is proposed comprising environmental perception, state representation, policy optimization, experience replay, reward engineering, semantic reasoning, and adaptive trajectory generation. The framework demonstrates how semantic knowledge, sensor fusion, and reinforcement learning cooperate to produce robust navigation strategies under uncertainty. Furthermore, the paper evaluates challenges involving sparse rewards, safety constraints, computational complexity, sim-to-real transfer, multi-agent coordination, and real-time deployment.
Hiroshi Tanaka· European International Journ...· 0 citations
This paper analyzes key technological developments, system design approaches, and operational frameworks in areas such as disaster response, autonomous surveillance, firefighting, and emergency medical support and proposes a staged autonomy framework incorporating perception, cognition, control, and coordination modules.
Hiroshi Tanaka· International Journal of Int...· 0 citations
The paper is a systematic and multifaceted review of the new trends in cyber-physical security of smart factories, and looks at the threat landscapes, attack vectors, architecture weaknesses, and intersection of the information technology (IT) and operational technology (OT) worlds.
Hiroshi Tanaka, Yuki Nakamura· International Journal of Mod...· 0 citations
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