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  1. Fuzzy logic controllers can handle complex systems by incorporating expert’s knowledge in the absence of formal mathematical models. Further, fuzzy logic controllers can effectively capture and accommodate uncertainties that are inherent in real-world controlled systems. On the other hand, Robot Operating System (ROS) has been widely used for many robotic applications due to its modular structure and efficient message-passing mechanisms for the integration of system’s components. For this reason, Robot Operating System is an ideal tool for developing software stacks for robotic applications. This paper develops a generic and configurable Robot Operating System package for the implementation of fuzzy logic controllers, particularly type-1 and interval type-2, which are based on either Mamdani or Takagi-Sugeno-Kang fuzzy inference mechanisms. This is achieved by employing a systematic object-oriented approach using the Unified Model Language (UML) to implement the fuzzy inference system as a single class that is composed of fuzzifier, inference, and defuzzifier classes. The deployment of the developed Robot Operating System package is demonstrated by implementing an interval type-2 fuzzy logic control of an Unmanned Aerial Vehicle (UAV). 
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  2. In this paper, an adjustable autonomy framework is proposed for Human-Robot Collaboration (HRC) in which a robot uses a reinforcement learning mechanism guided by a human operator's rewards in an initially unknown workspace. Within the proposed framework, the robot can adjust its autonomy level in an HRC setting that is represented by a Markov Decision Process. A novel Q-learning mechanism with an integrated greedy approach is implemented for robot learning to capture the correct actions and the robot's mistakes for adjusting its autonomy level. The proposed HRC framework can adapt to changes in the workspace, and can adjust the autonomy level, provided consistent human operator's reward. The developed algorithm is applied to a realistic HRC setting, involving a Baxter humanoid robot. The experimental results confirm the capability of the developed framework to successfully adjust the robot's autonomy level in response to changes in the human operator's commands or the workspace. 
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