Marco Lecce

Led and supervised R&D activities for an advanced long-range drone detection, tracking, and wireless power transfer system using high-power IR laser beams. Oversaw key phases from concept development to system integration, contributing to hardware selection, control architecture design, optical-mechanical integration, and real-world validation. Developed and coordinated detection, tracking, precision-pointing, and laser power-delivery solutions, including RTK-aided pre-positioning, multi-sensor data fusion, photodiode-based laser spot-centering, photovoltaic feedback, and vision-based PnP pose estimation integrated with a Fast Steering Mirror. Supported TRL advancement, industrialization pathways, IP preparation, and scientific publications. Contributed to the development of an Engineering Model of an orbital recharging payload, planned for a 2027 launch as a prototype of the final system. Provided mechatronics expertise across both terrestrial and space-oriented projects.

Led and supervised multidisciplinary R&D and consulting projects across automotive, agritech, industrial automation, and advanced engineering domains, acting as a technical reference for both project definition and execution. Combined strong hands-on engineering expertise with technical management responsibilities, contributing to the ideation, structuring, and supervision of publicly and privately co-funded innovation projects, from early concept and proposal preparation to technical delivery and validation. Coordinated teams of mechatronic engineers, data scientists, simulation specialists, and thesis students, guiding the development of Proofs of Concept, advanced simulation workflows, Model-Based Design solutions, control architectures, data-driven models, and scientific Machine Learning methodologies. Key activities included the technical supervision of plant modelling and automated scheduling solutions for train maintenance operations, reduced-order thermal modelling and virtual sensing for electric motors, ML-based diagnostics, robotics control, driver-behavior estimation, and scalable methodologies for CAE-enhanced scientific Machine Learning. Supported technical documentation, stakeholder alignment, business development activities, and research roadmap definition, bridging engineering depth, project governance, and industrial innovation objectives.

• CAD modeling, lumped and FEA simulation of an innovative system for the passive pitch-control of the blades in small three-blade wind turbines and Design for Additive Manufacturing. • Drafting and technical participation in PNRR EcoSister calls for the modeling of innovative components in AM (additive manufacturing) for small wind turbines. • Consultancy in competitive tenders for agriculture 4.0.

Provided independent R&D consulting for a robotics company, supporting the design and development of innovative mechanical, automated, and robotic systems. Contributed expertise in robotic systems, additive manufacturing, mechanical design, prototyping, and FEM-based structural assessment, with a focus on transforming early-stage technical concepts into manufacturable and testable prototypes. Worked autonomously within the company’s R&D team, supporting feasibility analysis, design refinement, prototype development, and engineering validation activities.

E-axle thermal model design for Predictive Maintenance. E-motor cooling control. Automotive modelling and simulation in Simulink environment. Vehicle model implementation in a driving simulator Development of a PoC for the estimation of wheels wear

I wrote a scientific paper to apply for the 2022 edition of the International Council of Aeronautical Sciences (ICAS), concerning Adaptive path planning for spraying UAS in wind environment, in association with the Department of Mechanical and Aerospace department of Politecnico di Torino. The paper was accepted by the ICAS commission and pubblically presented in Stocolm, in September 2022. (URL paper: https://iris.polito.it/handle/11583/2972004)

Design and control of Unmanned Aerial System for precision agriculture, grapevine diseases recognition by means of Convolutional Neural Networks (CNN) and precision spraying control of Plant Protection Products (PPP) in variable enviromental conditions.