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:

    Which sensor methodologies are relevant for autonomous mobile robots?

    How can a systematic selection of signal processing hardware be performed and what are the relevant criteria?

    How can a development plan for future products be sketched out despite the remaining uncertainties?

    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.


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    Title :

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


    Contributors:

    Published in:

    Publication date :

    2024-10-25


    Size :

    18 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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