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We describe a challenging robotics deployment in a complex ecosystem to monitor a rich plant community. The study site is dominated by dynamic grassland vegetation and is thus visually ambiguous and liable to drastic appearance change over the course of a day and especially through the growing season. This dynamism and complexity in appearance seriously impact the stability of the robotics platform, as localisation is a foundational part of that control loop, and so routes must be carefully taught and retaught until autonomy is robust and repeatable. Our system is demonstrated over a 6-week period monitoring the response of grass species to experimental climate change manipulations. We also discuss the applicability of our pipeline to monitor biodiversity in other complex natural settings., Comment: to be presented at the Workshop on Field Robotics - ICRA 2024

This paper introduces RobotCycle, a novel ongoing project that leverages Autonomous Vehicle (AV) research to investigate how road infrastructure influences cyclist behaviour and safety during real-world journeys. The project's requirements were defined in collaboration with key stakeholders, including city planners, cyclists, and policymakers, informing the design of risk and safety metrics and the data collection criteria. We propose a data-driven approach relying on a novel, rich dataset of diverse traffic scenes and scenarios captured using a custom-designed wearable sensing unit. By analysing road-user trajectories, we identify normal path deviations indicating potential risks or hazardous interactions related to infrastructure elements in the environment. Our analysis correlates driving profiles and trajectory patterns with local road segments, driving conditions, and road-user interactions to predict traffic behaviours and identify critical scenarios. Moreover, by leveraging advancements in AV research, the project generates detailed 3D High-Definition Maps (HD Maps), traffic flow patterns, and trajectory models to provide a comprehensive assessment and analysis of the behaviour of all traffic agents. These data can then inform the design of cyclist-friendly road infrastructure, ultimately enhancing road safety and cyclability. The project provides valuable insights for enhancing cyclist protection and advancing sustainable urban mobility., Comment: IEEE Intelligent Vehicles Symposium (IV 2024)

Performance and lifetime testing of batteries requires considerable effort and expensive specialist equipment. A wide range of potentiostats and battery testers are available on the market, but there is no standardisation of data exchange and data storage between them. To address this, we present Galvanalyser, a battery test database developed to manage the growing challenges of collating, managing and accessing data produced by multiple different battery testers. Collation is managed by a client-side application, the `Harvester', which pushes new data up to a PostgreSQL database on a server. Data access is possible in two ways: firstly, a web application allows data to be searched and viewed in a browser, with the option to plot data; secondly, a Python application programming interface (API) can connect directly to the database and pull requested data sets into Python. We hope to make Galvanalyser openly available soon. If you wish to test the system, please contact us for early access.