Teaching robotics to engineering students can be a challenging endeavor. In order to provide hands-on experiences, physical robot platforms are required. Previously, obtaining these platforms could be expensive, and required a lot of technical expertise from teaching staff. However, more recent models address these issues, therefore providing more opportunities for hands-on sessions. In this paper, we describe how we used the Turtlebot 3 mobile robot in master courses at KU Leuven. We provide an overview of the main functionalities, and suggest a number of improvements to further lower the learning curve for students. Additionally, we elaborate on the curriculum and learning outcomes of two courses that utilized Turtlebots in practically oriented sessions..

Localization based on visible light is a novel technique for indoor positioning that provides a number of advantages over traditional radio frequency based approaches. An important step in the deployment of visible light positioning systems is the calibration procedure, during which environmental parameters such as the positions of light sources are determined. This work presents a proof-of-concept approach to obtain these parameters in an efficient manner by using a mobile robot. This robot builds a map of the environment, and adds the location and identifier of optical transmitters to this map. With this approach, light source modulation frequencies can be estimated with sufficient accuracy to uniquely identify each source. Additionally, the inter-LED distance has an average accuracy of less than 10 cm compared to the real distance.

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Most indoor positioning systems require calibration before use. Fingerprinting requires the construction of a signal strength map, while ranging systems need the coordinates of the beacons. Calibration approaches exist for positioning systems that use Wi-Fi, radio frequency identification or ultrawideband. However, few examples are available for the calibration of visible light positioning systems. Most works focused on obtaining the channel model parameters or performed a calibration based on known receiver locations. In this paper, we describe an improved procedure that uses a mobile robot for data collection and is able to obtain a map of the environment with the beacon locations and their identities. Compared to previous work, the error is almost halved. Additionally, this approach does not require prior knowledge of the number of light sources or the receiver location. We demonstrate that the system performs well under a wide range of lighting conditions and investigate the influence of parameters such as the robot trajectory, camera resolution and field of view. Finally, we also close the loop between calibration and positioning and show that our approach has similar or better accuracy than manual calibration.