⏩ Volume 21, Issue No.4, 2023 (ISR)
Robotic Systems for Autonomous Navigation in Urban Environments Using AI and Deep Reinforcement Learning for Safe Traffic Management

This paper investigates the use of deep reinforcement learning and AI in robotic systems for autonomous navigation in urban environments. The focus is on ensuring safe traffic management by using advanced algorithms to dynamically adapt to traffic conditions, obstacles, and unforeseen events.

John Michael Anderson, Emily Sophia Clark, William Robert Harris, Olivia Claire Miller, Jacob Thomas Robinson

Paper ID: 52321401
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Optimizing Multi-Robot Systems for Complex Manufacturing Processes Using Machine Learning and Distributed Control for Enhanced Productivity

This paper presents a framework for optimizing multi-robot systems in manufacturing processes. The integration of machine learning and distributed control systems enhances productivity by allowing robots to work collaboratively in dynamic environments, learning from each task to improve their efficiency over time.

Lucas Benjamin Davis, Nathan Samuel Roberts, Olivia Maria Johnson, Clara Elizabeth Williams, Sarah Victoria Moore

Paper ID: 52321402
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AI-Powered Robotic Systems for Industrial Automation and Fault Detection in High-Risk Environments Using Real-Time Data Processing

This paper investigates AI-powered robotic systems for industrial automation, specifically focusing on fault detection in high-risk environments. Through real-time data processing and predictive analytics, the system detects potential issues early, reducing downtime and improving safety in complex industrial operations.

David William Thompson, Alexander Daniel Harris, Jessica Marie Clark, Elizabeth Anna King, Robert Samuel Anderson

Paper ID: 52321403
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Designing Autonomous Robots for Search-and-Rescue Missions in Disaster Zones Using Computer Vision and Machine Learning for Enhanced Decision-Making

This research explores autonomous robots used in search-and-rescue missions in disaster zones. The robots leverage computer vision and machine learning algorithms to analyze the environment, identify survivors, and make real-time decisions to navigate complex, hazardous situations for improved mission outcomes.

Michael James Walker, Hannah Elizabeth Robinson, Andrew Jonathan Scott, Claire Olivia Adams, Thomas Daniel Lee

Paper ID: 52321404
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Integrating Machine Learning Algorithms in Robotic Systems for Smart Agriculture and Precision Farming in Rural Areas

This study discusses the integration of machine learning algorithms in robotic systems for smart agriculture. The robots, used for precision farming, can assess crop health, predict yields, and optimize resource allocation, making them an invaluable tool for sustainable farming practices in rural regions.

James Alexander Brown, Olivia Claire Scott, Ethan Michael Robinson, Grace Isabella Harris, Nathan Thomas Johnson

Paper ID: 52321405
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