This paper presents an aerial-ground field robotic team, designed to collect and transport soil and biota samples in estuarine mudflats. The robotic system has been devised so that its sampling and storage capabilities are suited for radionuclides and heavy metals environmental monitoring. Automating these time-consuming and physically demanding tasks is expected to positively impact both their scope and frequency. The success of an environmental monitoring study heavily depends on the statistical significance and accuracy of the sampling procedures, which most often require frequent human intervention. The bird's-eye view provided by the aerial vehicle aims at supporting remote mission specification and execution monitoring. This paper also proposes a preliminary experimental protocol tailored to exploit the capabilities o↵ered by the robotic system. Preliminary field trials in real estuarine mudflats show the ability of the robotic system to successfully extract and transport soil samples for o✏ine analysis.

This paper presents a robotic team suited for bottom sediment sampling and retrieval in mudflats, targeting environmental monitoring tasks. The robotic team encompasses a four-wheel-steering ground vehicle, equipped with a drilling tool designed to be able to retain wet soil, and a multi-rotor aerial vehicle for dynamic aerial imagery acquisition. On-demand aerial imagery, properly fused on an aerial mosaic, is used by remote human operators for specifying the robotic mission and supervising its execution. This is crucial for the success of an environmental monitoring study, as often it depends on human expertise to ensure the statistical significance and accuracy of the sampling procedures. Although the literature is rich on environmental monitoring sampling procedures, in mudflats, there is a gap as regards including robotic elements. This paper closes this gap by also proposing a preliminary experimental protocol tailored to exploit the capabilities offered by the robotic system. Field trials in the south bank of the river Tagus’ estuary show the ability of the robotic system to successfully extract and transport bottom sediment samples for offline analysis. The results also show the efficiency of the extraction and the benefits when compared to (conventional) human-based sampling.

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This paper presents a multi-core processing solution for ROS-based service robots. The power management together with the control and availability of the processing resources are supervised by a custom-made Power Management Board (PMB) based on a Digital Signal Processor (DSP) micro controller, implementing a Health and Usage Monitoring System (HUMS). The proposed architecture also allows for the PMB to control the most critical robot functions in case of low battery conditions or impossibility of performing energy harvesting, thus extending the lifespan of the robot. All PMB data is recorded on a SD card so as to allow offline analyses of the robotic mission and, thus, support subsequent maintenance activities. Two different implementations of the proposed system have been fielded in two Multi-Robot Systems (MRS) for environmental monitoring, covering aerial, water surface, and wheeled ground vehicles. An additional implementation of the architecture is currently being deployed on an industrial autonomous logistics robot. These three implementations are presented and discussed.

Markets globalization and boosted customer demands in terms of product quality and customization are changing the way manufacturing need to be faced. Future manufacturing is obliged to effortlessly ensure agility, robustness and performance during (re)engineering processes. This article presents the application of a Coalition Agent on a Multi-agent shop floor environment with improved and user-friendly mechanism to model and execute complex skills promoting reconfiguration over reprogramming. The Coalition Agent embeds a rule and workflow engine to support the discovery of equipment skills and definition of the required process to achieve its composition into complex skills. This approach was deployed and tested on a experimental manufacturing environment where each equipment module is "agentified" and its skills are available to be discovered and executed.