Cloud robotics

The work carried out in this WP includes the creation of a generic cloudification model for robotic algorithms, that integrates with the Robot Operating System (ROS), allowing the cooperation and information sharing between different robots to take place, but giving guaranties of privacy and security at the same time. This information sharing is the key to accelerating and making robust the learning of problems such as voice or object recognition.

Team & Collaborators

• Luís Alexandre | WP leader
• Chiranjeevi Karri | Grantee, C4-UBI (06/2019-08/2020)
• Saeid Alirezazadeh | Grantee, C4-UBI
• André Correia | Grantee, C4-UBI

Publications

• Alirezazadeh, S. & Alexandre, L. A. (2020). Improving Makespan in Dynamic Task Allocation for Cloud Robotic Systems with Time Window Constraints. ArXiv e-prints 2012.03555. (submitted for publication).
• Alirezazadeh, S. & Alexandre, L. A. (2020). Optimal Algorithm Allocation for Single Robot Cloud Systems. ArXiv e-prints 2003.08683 (submitted for publication).
• Karri, C. (2021). Secure robot face recognition in cloud environments. In Multimedia Tools and Applications, Springer.
• Karri, C. (2020). Deep Learning Algorithms for Secure Robot Face Recognition in Cloud Environments. In The 19th International Conference on Ubiquitous Computing and Communication (IUCC-2020) IEEE, to appear.
• Alirezazadeh, S. & Alexandre, L. A. (2020). Dynamic task allocation for robotic network cloud systems. In The 19th International Conference on Ubiquitous Computing and Communication (IUCC-2020) IEEE, to appear (preprint).

Related publications

• Pereira, C., Falcao, G., & Alexandre, L. A. (2019). Pragma-oriented parallelization of the direct sparse odometry SLAM algorithm. In 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (pp. 252-259). IEEE.
• Lopes, V., & Alexandre, L. A. (2019). An Overview of Blockchain Integration with Robotics and Artificial Intelligence. In Symposium on Blockchain for Robotic Systems, MIT Media Lab.
• Marques, J., Falcao, G., & Alexandre, L. A. (2018). Distributed learning of CNNs on heterogeneous CPU/GPU architectures. In Applied Artificial Intelligence, 32(9-10), 822-844.
• Falcao, G., Alexandre, L. A., Marques, J., Frazão, X., & Maria, J. (2017). On the evaluation of energy-efficient deep learning using stacked autoencoders on mobile gpus. In 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (pp. 270-273). IEEE.
• Maria, J., Amaro, J., Falcao, G., & Alexandre, L. A. (2016). Stacked autoencoders using low-power accelerated architectures for object recognition in autonomous systems. In Neural Processing Letters, 43(2), 445-458.