Enabling Global Robot Navigation Based on a Cloud Robotics Approach

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Implications of the Google’s US 8,996,429 B1 Patent in Cloud Robotics-Based Therapeutic Researches

Intended for being informative to both legal and engineer communities, this chapter raises awareness on the implications of recent patents in the field of human-robot interaction (HRI) studies. Google patented the use of cloud robotics to create robot personality(-ies). The broad claims of the patent could hamper many HRI research projects in the field. One of the possible frustrated research lines is related to robotic therapies because the personalization of the robot accelerates the process of engagement, which is extremely beneficial for robotic cognitive therapies. This chapter presents, therefore, the scientific examination, description, and comparison of the Tufts University CEEO project “Data Analysis and Collection through Robotic Companions and LEGO® Engineering with Children on the Autism Spectrum project” and the US 8,996,429 B1 Patent on the Methods and Systems for Robot Personality Development of Google. Some remarks on ethical implications of the patent will close the chapter and open the discussion to both communities

Design of a cloud robotic system to support senior citizens: the KuBo experience

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Cloud Robotics and Autonomous Vehicles

Recently, a good amount of research has been focused on the development of the autonomous vehicles. Autonomous vehicles possess great potential in numerous challenging applications, for example, autonomous armoured fighting vehicles, automated highway systems, etc. To enable the usage of autonomous vehicles in such challenging applications, it is important to ensure the safety, efficiency, reliability and robustness of the system. Most of the existing implementations of the autonomous vehicles operate as standalone systems limited to onboard capabilities (computations, memory, data, etc.), which limit their potential and performance in real-world applications. The advent of the Internet and emerging advances in the cloud infrastructure suggests new methodologies where vehicles are not limited to onboard capabilities. Processing is also performed remotely on cloud to support different operations and to increase the proficiency of decision-making. This chapter surveys the research to date in the evolution of autonomous vehicles, cloud and cloud-enabled autonomous vehicles, with the limitations of existing systems, research challenges and possible future directions. The chapter can help new researchers in the field to understand and evaluate different approaches for the design of the autonomous vehicular systems

Intelligent Transportation System for Smart-Cities using Fuzzy Logic

According to United Nations population statistics 2017, the world population is 7.6 billion and is growing rapidily alomost 11 billion by end of 21 century with a 70% chance of continued growth, this rapid increasing population have created low standards of living in cities. Smart Cities are facing pressures associated with due innovations and globalization to improve their citizens life. Computational intelligence is the study of adaptive mechanism to facilitate intelligent behavior in changing and complex environments. Traffic congestion and monitoring has become one of the critical issues in big cities. The adaptive mechanism of computational intelligence in changing the behavior of complex environments of smart city is very effective. The developing framework and services for smart-city requires sound infrastructure, latest current technology adoption. A framework model with the integration of cloud-data, social network (SN) services that is collecting stream data with smart sensors in the context of smart cities is proposed. The adaptive mechanism of computational intelligence in changing thebehavior of complex environments of smart city is very effective. A radical framework that enables the analysis of big-data sets stemming from Social Networking (SN) sites. Smart cities understanding is a broad concept only city transportation sector is focused in this article. Fuzzy logic modeling techniques are used in many fields i.e. medical, engineering. business and computing related problems. To solve various traffic management issues in cities a detailed analysis of fuzzy logic system is proposed. This paper presents an analysis of the results achieved using Fuzzy Logic System (FLS) for smart cities. The results are verified using MATLAB Simulation