Autonomous mobile robots

This PhD thesis explores the challenges and solutions at the intersection of autonomous mobile robots (AMRs) and real-time video streaming, aiming to improve both robot control and communication systems. It begins by digging into the control strategies that drive AMRs, particularly focusing on Model Predictive Control (MPC) and Dynamic Programming (DP) to optimize decision-making and navigation. The research then shifts to industrial vehicles, proposing a fault-tolerant system designed to detect encoder sensor failures during motor speed control, which improves safety and reliability in real-world applications. The thesis also investigates the use of drones for tasks like surveillance and rescue, addressing the challenge of maintaining high-quality video streams despite fluctuating network conditions. A novel approach using Nonlinear MPC is introduced to optimize drone altitudes, ensuring clear video transmission and efficient altitude control. Finally, the thesis presents an adaptive bitrate algorithm, RT-MPC, to enhance video quality in real time by adjusting to network fluctuations. This work bridges the gap between autonomous robotics and video streaming, providing practical solutions for both fields.