This study examines how deep learning can enhance the perception capabilities of autonomous robots, enabling them to navigate and interact more efficiently within smart city environments. The paper focuses on object recognition, path planning, and real-time decision-making processes.

Rajesh Kumar Gupta, Yao Ming Li, Ananya Sharma, Qing Zhang, Michael Joseph Brown

This paper explores the integration of artificial intelligence in robotic surgery. Focusing on minimally invasive procedures, it reviews the developments in surgical robots, with AI facilitating enhanced precision, real-time data analysis, and better patient outcomes during complex surgeries.

David John Miller, Emma Louise Robinson, Yi Chen Wang, Johan Andersson, Isabella Margaret Lee

This study investigates the use of blockchain technology for securing robot-generated data in healthcare systems. It demonstrates how blockchain can enhance data integrity and privacy while enabling secure, decentralized sharing of health data between robots and healthcare providers.

Li Wei Chen, Maria Teresa Gonzalez, Daniel James Robinson, Qi Yu Zhang, Elena Maria Costa

This paper presents AI-driven robotic systems designed for environmental monitoring in smart cities. The study highlights the use of robotics and AI to monitor air quality, waste management, and resource usage, enhancing city planning, sustainability, and pollution control efforts.

Jun Wang, Lara Sofia Rossi, Takashi Hiroshi Yamada, Martin Thomas Green, Anastasia Felicia Morris

This research explores the application of machine learning algorithms for predictive maintenance in robotic systems. The paper discusses how AI and big data analytics can help predict potential failures, optimize maintenance schedules, and reduce downtime in robotic systems within Industry 4.0 environments.

William Richard Brown, Liu Yun Mei, Rajeev Ranjan Kumar, Pauline Helen Clark, Francisco Javier Ruiz