On-chip Magnetoresistive Sensors for Detection and Localization of Paramagnetic Particles

This paper presents the work towards miniaturized magnetic biosensor array based on the detection of paramagnetic particles using the giant magnetoresistance (GMR) effect. GMR sensors have been studied for many years, but its application for on-chip integration and in complex configurations, as well as effective localization for Lab-On-Chip and Tissue Engineering applications is not yet explored. This work demonstrates the development of initial prototypes of 5 and 9 sensor GMR arrays of varying geometries and corresponding calibration and localization algorithms to detect and localize paramagnetic materials in 2D. The generation of a uniform magnetic field using a 16 magnet Halbach cylinder was also analyzed and optimized using FEA for different sensor configurations. Results show excellent localization for the fully calibrated 5 sensor arrays, with a mean (SD) error of 2.45 (1.61) mm for the ferrofluid as compared to 1.48 (1.14) mm for a strong ferromagnet for a 25×25mm2 array surface. The 9sensor array similarly showed good results for full calibration

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Enhancing Graph Representation of the Environment through Local and Cloud Computation

Enriching the robot representation of the operational environment is a challenging task that aims at bridging the gap between low-level sensor readings and high-level semantic understanding. Having a rich representation often requires computationally demanding architectures and pure point cloud based detection systems that struggle when dealing with everyday objects that have to be handled by the robot. To overcome these issues, we propose a graph-based representation that addresses this gap by providing a semantic representation of robot environments from multiple sources. In fact, to acquire information from the environment, the framework combines classical computer vision tools with modern computer vision cloud services, ensuring computational feasibility on onboard hardware. By incorporating an ontology hierarchy with over 800 object classes, the framework achieves cross-domain adaptability, eliminating the need for environment-specific tools. The proposed approach allows us to handle also small objects and integrate them into the semantic representation of the environment. The approach is implemented in the Robot Operating System (ROS) using the RViz visualizer for environment representation. This work is a first step towards the development of a general-purpose framework, to facilitate intuitive interaction and navigation across different domains.Comment: 5 pages, 4 figure