This research explores how reinforcement learning combined with deep neural networks can enhance autonomous robotic navigation. The study focuses on adapting robots to dynamic environments by improving decision-making and optimizing pathfinding algorithms for real-time applications in robotics and AI systems.

Alexander James Carter, William Benjamin Wright, Charlotte Olivia Smith, John Daniel Green, Rachel Amy Thomas

This paper examines the integration of AI and deep learning in robotic systems designed for search and rescue operations in disaster zones. It discusses the challenges and innovative solutions for autonomous robots to navigate, assess, and assist in real-time in hazardous environments.

Sophia Maria Evans, Emma Charlotte Johnson, George Timothy White, Olivia Sarah Wilson, Nathan Matthew Adams

This research explores collaborative robotic systems that autonomously explore hazardous environments and handle dangerous materials. The paper discusses advancements in robotic teamwork, AI algorithms, and communication strategies that allow robots to work in unison for complex tasks.

Michael Christopher Taylor, Lauren Isabel Brown, Jonathan Ryan Lee, Elizabeth Megan Harris, Jacob Benjamin Carter

This paper presents autonomous multi-robot systems for environmental monitoring in large-scale and hazardous areas. The study focuses on the challenges of coordinating multiple robots, real-time data processing, and the integration of AI for environmental sensing and hazard detection in critical areas.

Olivia Lily Foster, Clara Maria Jackson, Max Daniel Cooper, Isabella Sophie Turner, Henry Thomas Mitchell

This study presents an overview of robotics for industrial automation. It covers key advancements in robotic systems for manufacturing, highlighting the integration of AI, machine learning, and robotics for real-time production processes and system optimization in complex manufacturing environments.

John Alexander Robinson, Lucas Matthew Gray, Sarah Victoria Clark, Laura Isabelle Thompson, Oliver Jack Lewis