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
The application of autonomous robotics in public safety is transforming how societies prepare for, respond to, and recover from emergencies. Traditional human-centered operations in disaster response, firefighting, search and rescue, law enforcement, and infrastructure protection often expose personnel to high-risk environments. Autonomous robots offer a safer alternative by operating in hazardous and uncertain conditions with minimal human intervention. Advancements in Artificial Intelligence, sensor fusion, machine perception, embedded systems, and communication technologies have enabled modern robots to make complex decisions, collaborate in teams, and adapt to dynamic environments. This paper reviews the evolution of public safety robotics, from early teleoperated systems to advanced multi-agent autonomous robots. It analyzes key technological developments, system design approaches, and operational frameworks in areas such as disaster response, autonomous surveillance, firefighting, and emergency medical support. The study proposes a staged autonomy framework incorporating perception, cognition, control, and coordination modules. Results indicate that autonomous robotics can significantly improve situational awareness, reduce response time, and enhance responder safety, while highlighting challenges related to reliability, trust, legal responsibility, and interoperability.
Hiroshi Tanaka· International Journal of Int...· 0 citations
By Industry 4.0 has resulted in the prevalence of smart factories, in which there is a close relationship between computation, communication, and physical processes through cyber-physical systems (CPS). Even though productivity, flexibility, and efficiency are improved under this integration, new security challenges that have never been experienced previously and go beyond the traditional cybersecurity boundaries are created. Cyber-physical security (CPSec) is the provision of defensive of the cyber components, as well as physical processes against malicious attacks, failures, and system anomalies. The paper is a systematic and multifaceted review of the new trends in cyber-physical security of smart factories. It looks at the threat landscapes, attack vectors, architecture weaknesses, and intersection of the information technology (IT) and operational technology (OT) worlds. In addition, the article evaluates the innovative defense systems such as artificial intelligence-based intrusion detection systems, blockchain-based trust management systems, zero-trust-based architectures, and security validation using digital twins. An adaptable, resilient, and real-time threat response-focused layered security approach is suggested to smart manufacturing settings. Recent experimental data on the role of advanced CPSec strategies is presented based on the considerations of industrial case studies and simulations. Lastly, the paper identifies open research problems and future directions requirements in order to establish robust, scale up and smart cyber-physical security systems in next generation smart factories.
Hiroshi Tanaka, Yuki Nakamura· International Journal of Mod...· 0 citations