arXiv subjects

Marcello Calisti

Publications and source records attributed to Marcello Calisti.

2 recordsLinked to original sources

arXiv2025

An Open Toolkit for Underwater Field Robotics

Underwater robotics is becoming increasingly important for marine science, environmental monitoring, and subsea industrial operations, yet the development of underwater manipulation and actuation systems remains restricted by high costs, proprietary designs, and limited access to modular, research-oriented hardware. While open-source initiatives have democratized vehicle construction and control software, a substantial gap persists for joint-actuated systems-particularly those requiring waterproof, feedback-enabled actuation suitable for manipulators, grippers, and bioinspired devices. As a result, many research groups face lengthy development cycles, limited reproducibility, and difficulty transitioning laboratory prototypes to field-ready platforms. To address this gap, we introduce an open, cost-effective hardware and software toolkit for underwater manipulation research. The toolkit includes a depth-rated Underwater Robotic Joint (URJ) with early leakage detection, compact control and power management electronics, and a ROS2-based software stack for sensing and multi-mode actuation. All CAD models, fabrication files, PCB sources, firmware, and ROS2 packages are openly released, enabling local manufacturing, modification, and community-driven improvement. The toolkit has undergone extensive laboratory testing and multiple field deployments, demonstrating reliable operation up to 40 m depth across diverse applications, including a 3-DoF underwater manipulator, a tendon-driven soft gripper, and an underactuated sediment sampler. These results validate the robustness, versatility, and reusability of the toolkit for real marine environments. By providing a fully open, field-tested platform, this work aims to lower the barrier to entry for underwater manipulation research, improve reproducibility, and accelerate innovation in underwater field robotics.

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arXiv2025

Data-Driven Greenhouse Climate Regulation in Lettuce Cultivation Using BiLSTM and GRU Predictive Control

Efficient greenhouse management is essential for sustainable food production, but the high energy demand for climate regulation poses significant economic and environmental challenges. While traditional process-based greenhouse models exist, they are often too complex or imprecise for reliable control. To address this, our study introduces a novel data-driven predictive control framework using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks within a Model Predictive Control (MPC) architecture. Training data were generated from a validated dynamic model simulating lettuce cultivation under various environmental conditions. The LSTM and GRU networks were trained to predict future greenhouse states -- including temperature, humidity, CO\textsubscript{2} concentration, and crop dry matter -- with robustness confirmed via $10$-fold cross-validation. These networks were embedded into an online MPC controller to optimize heating, ventilation, and CO\textsubscript{2} injection, aiming to minimize energy consumption and maximize crop yield while respecting biological constraints. Results showed that both the LSTM- and GRU-based controllers significantly outperformed a conventional MPC baseline. For example, humidity violations dropped from 54.77\% (MPC) to 15.45\% (GRU) and 17.71\% (LSTM), while day-night temperature deviations were kept below $2^\circ\text{C}$. The GRU controller further achieved up to 40\% lower computation time than its LSTM counterpart, confirming its real-time feasibility. Overall, the proposed GRU-driven predictive control approach offers a robust and computationally efficient solution for intelligent greenhouse climate automation under practical operational constraints.

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