Reducing Computation Requirements for Autonomous Mobile Robots on Low Powered Embedded Systems

Fully autonomous mobile robots have the potential to revolutionize various industries, from warehouse management to hospital logistics and last-mile deliveries. However, a significant obstacle to achieving reliable autonomy lies in the high computational and energy requirements. In response to this challenge, our paper introduces two innovative algorithms: the Pure Image Segmentation Approach (PISA) and the UNet Based Approach to Semantic Segmentation (UBASS). PISA leverages classical computer vision techniques, offering a fresh perspective on solving crucial tasks such as object detection, object avoidance, and lane detection. In contrast, UBASS harnesses the power of deep learning algorithms for semantic segmentation, unlocking new capabilities in robot perception. Our experiments showcase the effectiveness of these algorithms, demonstrating their accuracy and computational efficiency. Notably, PISA and UBASS outperform or match traditional techniques, including End-to-End Deep Learning and Canny Edge Detection, in terms of both task performance and resource utilization. This research contributes to the advancement of autonomous mobile robotics by offering practical and efficient solutions for navigation and perception challenges. By combining classic and contemporary approaches, we aim to inspire further research in the field, ultimately paving the way for more accessible and dependable autonomous mobile robots.