DEVELOPING NOVEL CONTROL ALGORITHMS FOR ROBOTIC SYSTEMS THAT CAN ADAPT TO CHANGING ENVIRONMENTS

Abstract

With advances in robotics, enabling robots to autonomously adapt to dynamic and uncertain environments has become a fundamental challenge. Conventional control strategies, while effective in predictable settings, often struggle in unstructured or rapidly changing conditions. Addressing this limitation requires the design of adaptive control algorithms that integrate learning-based methods, robust sensor fusion, and real-time decision-making. The integration of Robotics with Wireless Sensor Networks (WSN), especially through Robotic Wireless Sensor Networks (RWSN), offers significant potential for enhancing environmental awareness and adaptability. At the same time, the rise of human–robot collaboration emphasizes the need for reliability, safety, and transparency in robotic systems. By combining adaptive control with strict safety frameworks, robots can achieve higher levels of trust and efficiency in collaborative environments. This study examines the challenges, opportunities, and future directions in developing novel control algorithms capable of equipping robotic systems with the resilience needed to thrive in diverse and dynamic contexts.