2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR), 2020
Service delivery application involving robots relies on the success of the navigation process. To... more Service delivery application involving robots relies on the success of the navigation process. To ensure maximum performance, the navigation process should generate optimal path avoiding collisions with dynamic obstacles. Selecting such a path by analyzing the dynamic environment condition while putting minimal overhead on robots is a challenging problem in service robot navigation. In this work, we develop a Fog assisted service robot navigation system that puts most of the computing tasks to a central Fog server. The server employs a vision-based monitoring system to locate the robots and obstacles. The optimal path is generated by analyzing the available information and the robots are instructed accordingly. We implement a test-bed system to evaluate the performance of the proposed system. The results depict that the proposed fog-based system can achieve as low as 23.81% of average distance covered, 22.05% of average service delivery time, and 22.72% of average energy consumption compared to the without fog systems. Contribution-A fog-based system for service robot navigation is proposed that uses computer vision to generate optimal obstacle free path.
Uploads
Papers by Lafifa Jamal
The paper delves into the financial pressures faced by universities, prompting them to seek innovative solutions for survival and growth. By forming networks and ecosystems, universities can leverage collective expertise and resources, enhancing their ability to provide high-quality education and research. The role of these collaborations in fostering innovation, improving operational efficiency, and addressing societal challenges is critically examined.
Furthermore, the paper discusses the implications of these developments for the future of higher education. It highlights the potential for increased interdisciplinary research, enhanced student mobility, and the creation of more flexible and adaptive educational models. The integration of digital technologies and artificial intelligence in these networks is also explored, emphasizing their role in facilitating communication, collaboration, and data-driven decision-making.
In conclusion, the paper argues that the future of universities lies in their ability to adapt to changing environments through strategic collaborations and the adoption of innovative practices. By embracing these changes, universities can continue to play a pivotal role in shaping the knowledge economy and addressing global challenges.