Abstract
Efficient area coverage is paramount across various domains such as painting, mining, agriculture, cleaning, and inspection. However, conventional fixed-shape robots encounter limitations in adapting to diverse environments, leading to inefficiencies and constraints in coverage. While small fixed-shape robots excel in maneuvering through confined spaces, they struggle to efficiently cover large open areas, while their larger counterparts face challenges in navigating tight spaces. This thesis proposes a novel solution leveraging self-reconfigurable robots capable of dynamically adjusting their robotic footprints based on environmental conditions. By seamlessly altering their size and shape, these robots address the shortcomings of fixed-shape counterparts, offering enhanced adaptability and efficiency in area coverage tasks. The proposed method integrates advanced path planning strategies, considering both static and dynamic obstacles, to optimize coverage in varied environments.
Through comprehensive analysis and experimentation, this research showcases the effectiveness of the proposed approach in significantly reducing coverage time while maintaining high levels of area coverage. By combining global and local path planning algorithms tailored to reconfigurable robots, this thesis establishes a new benchmark in complete coverage path planning, surpassing existing state-of-the-art methodologies. Ultimately, this research contributes to advancing the field of reconfigurable robotics by offering a robust framework for efficient area coverage, enhancing productivity across diverse applications.