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In the last 20 years there have been major advances in autonomous robotics. In IoT (Industry 4.0), mobile robots require more intuitive interaction possibilities with humans in order to expand its field of applications. This paper describes a user-friendly setup, which enables a person to lead the robot in an unknown environment. The environment has to be perceived by means of sensory input. For realizing a cost and resource efficient Follow Me application we use a single monocular camera as low-cost sensor. For efficient scaling of our Simultaneous Localization and Mapping (SLAM) algorithm, we integrate an inertial measurement unit (IMU) sensor. With the camera input we detect and track a person. We propose combining state of the art deep learning with Convolutional Neural Network (CNN) and SLAM algorithms functionality on the same input camera image. Based on the output robot navigation is possible. This work presents the specification, workflow for an efficient development of the Follow Me application. Our application’s delivered point clouds are also used for surface construction. For demonstration, we use our platform SCITOS G5 equipped with the afore mentioned sensors. Preliminary tests show the system works robustly in the wild.
The aim of this work is the development of artificial intelligence (AI) application to support the recruiting process that elevates the domain of human resource management by advancing its capabilities and effectiveness. This affects recruiting processes and includes solutions for active sourcing, i.e. active recruitment, pre-sorting, evaluating structured video interviews and discovering internal training potential. This work highlights four novel approaches to ethical machine learning. The first is precise machine learning for ethically relevant properties in image recognition, which focuses on accurately detecting and analysing these properties. The second is the detection of bias in training data, allowing for the identification and removal of distortions that could skew results. The third is minimising bias, which involves actively working to reduce bias in machine learning models. Finally, an unsupervised architecture is introduced that can learn fair results even without ground truth data. Together, these approaches represent important steps forward in creating ethical and unbiased machine learning systems.