The evolution of cloud computing has finally started to affect robotics. Indeed, there have been several real-time cloud applications making their way into robotics as of late. Inherent benefits of cloud robotics include providing virtually infinite computational power and enabling collaboration of a multitude of connected devices. However, its drawbacks include higher latency and overall higher energy consumption.
Moreover, local devices in proximity incur higher latency when communicating among themselves via the cloud. At the same time, the cloud is a single point of failure in the network. Fog Computing is an extension of the cloud computing paradigm providing data, compute, storage and application services to end-users on a so-called edge layer. Distinguishing characteristics are its support for mobility and dense geographical distribution. We propose to study the implications of applying fog computing concepts in robotics by developing a middle-ware solution for Robotic Fog Computing Cluster solution for enabling adaptive distributed computation in heterogeneous multi-robot systems interacting with the Internet of Things (IoT). The developed middle-ware has a modular plug-in architecture based on micro-services and facilitates communication of IOT devices with the multi-robot systems.
In addition, the developed middle-ware solutions support different load balancing or task allocation algorithms. In particular, we establish that we can enhance the performance of distributed system by decreasing overall system latency by using already established multi-criteria decision-making algorithms like TOPSIS and TODIM with naive Q-learning and with Neural Network based Q-learning. / Master of Science / Technologies like robotics are advancing at a rapid pace and have started affecting various aspects of human lives. A lot more focus is now on collaborative robotics which focuses on robots designed to work with each other. A swarm/fleet of robots has unique use cases like disaster rescue missions.
In this thesis, we explore various ways to enable efficient and effective communication between robots in a multi-robot environment. We also compare different methods a robot can communicate and share its workload with other robots in a collaborative environment. Finally, we propose a new approach to reducing robots communication cost and optimizing process through which it shares its workload with other robots in real time using machine learning techniques.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/78723 |
| Date | 21 August 2017 |
| Creators | Bhal, Siddharth |
| Contributors | Electrical and Computer Engineering, Williams, Ryan K., Tokekar, Pratap, Hsiao, Michael S. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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