Advanced Sensor Systems for Robotics and Autonomous Vehicles
Studies in Computational Intelligence
In robotic and autonomous vehicle applications, sensor systems play a critical role. Machine learning (ML), data science, artificial intelligence (AI), and the internet of things (IoT) are all advancing, which opens up new possibilities for autonomous vehicles. For vehicle control, traffic monitoring, and traffic management applications, the integration of robotics, IoT, and AI is a very powerful combination. For effective robotic and vehicle control, robot sensor devices require an advanced sensor system. As a result, the AI-based system seeks the attention of the researcher to make the best use of sensor data for various robotic applications while conserving energy. The efficient collection of the data from sensors is a significant difficulty that AI technologies can effectively address. The data consistency method can also be used for time-constraint data collection applications. The present chapter discusses three important methods to improve the quality of service (QoS) and quality of experience (QoE) parameters of the robotic and autonomous vehicle applications. The first one is consistency-guaranteed and collision-resistant approach that can be used by the advanced sensor devices for the data aggregation and the removal of the redundant data. The second one is aggregation aware AI-based methods to improve the lifetime of the robotic devices and the last one is dividing the sensors devices based on continuous and event-monitoring robotic application and usage of the application-specific protocol to deal with the corresponding data. In addition the present chapter also discusses the role of sensor systems for various applications.
Open Access Status
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