Case Study: Cognitive Sensors for Autonomous Guided Vehicles (AGVs)

Cognitive sensing is regarded as a key technology for autonomous guided vehicles. However, it remains an open question on which basis and in which combination future sensor systems can be designed in order to achieve cognitive recognition and perception. This case study will explore the variety of technologies available in the field of sensing and signal processing that can be used in autonomous guided vehicles such as those used in logistics as transportation systems or mobile robots. The focus on autonomous systems provides the framework for the development of cognitive sensing, which involves recognizing the environment by preprocessing sensor data. This case study begins by reviewing and comparing relevant sensor methodologies. It then considers electrical hardware approaches to processing sensor data in order to implement algorithms for recognizing and perceiving the environment. The following questions are addressed: This case study provides the competence necessary for finding solutions amidst technological uncertainties. Firstly, it analyzes and evaluates the current state of the art and outlines different alternative solutions, and secondly, it presents the results in the form of a roadmap and a phased implementation plan.