---

Publications

2025

• Signorile, F., Mastromarino, F., Scarabaggio, P., Gialò, V., Carli, R. & Dotoli, M. (2025) Optimal Positioning of Electric Vehicle Chargers for Efficient Land Use in Smart Cities: Integration with Fuel Stations IN Volta, M. (Ed.), IFAC-PapersOnLine.Elsevier B.V., 315-320. doi:10.1016/j.ifacol.2025.08.156
  [BibTeX] [Abstract] [Download PDF]

  Expanding the charging infrastructure is essential for the widespread adoption of electric vehicles (EVs). A promising and effective solution is integrating EV charging points into existing fuel stations, thus optimizing land use while enhancing accessibility. Hence, the optimal placement of EV chargers within fuel station networks is a critical challenge. Traditional approaches rely on the so-called Maximal Covering Location Problem (MCLP), which assumes binary coverage and overlooks capacity constraints. This paper extends the MCLP framework by introducing a novel distance-based scalable coverage function and incorporating capacity limitations to prevent stations overload. The proposed model enhances the accuracy of demand distribution and offers a realistic, scalable approach to planning EV charging infrastructure. To validate its effectiveness, the proposed model is tested on a real-world case study involving the city of Bari, Italy. © 2025 Elsevier B.V., All rights reserved.

  @CONFERENCE{Signorile2025315,
  author = {Signorile, Federico and Mastromarino, Fabio and Scarabaggio, Paolo and Gialò, Valeria and Carli, Raffaele and Dotoli, Mariagrazia},
  title = {Optimal Positioning of Electric Vehicle Chargers for Efficient Land Use in Smart Cities: Integration with Fuel Stations},
  year = {2025},
  journal = {IFAC-PapersOnLine},
  volume = {59},
  number = {9},
  pages = {315 - 320},
  doi = {10.1016/j.ifacol.2025.08.156},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105016601583&doi=10.1016%2Fj.ifacol.2025.08.156&partnerID=40&md5=1355076b28a3acbd873b765c23e1594f},
  affiliations = {Politecnico di Bari, Department of Electronic and Information Engineering, Bari, Italy},
  abstract = {Expanding the charging infrastructure is essential for the widespread adoption of electric vehicles (EVs). A promising and effective solution is integrating EV charging points into existing fuel stations, thus optimizing land use while enhancing accessibility. Hence, the optimal placement of EV chargers within fuel station networks is a critical challenge. Traditional approaches rely on the so-called Maximal Covering Location Problem (MCLP), which assumes binary coverage and overlooks capacity constraints. This paper extends the MCLP framework by introducing a novel distance-based scalable coverage function and incorporating capacity limitations to prevent stations overload. The proposed model enhances the accuracy of demand distribution and offers a realistic, scalable approach to planning EV charging infrastructure. To validate its effectiveness, the proposed model is tested on a real-world case study involving the city of Bari, Italy. © 2025 Elsevier B.V., All rights reserved.},
  author_keywords = {Charging stations; Electric vehicles; Land use; Maximal covering location problem; Optimal positioning; Smart cities},
  keywords = {Charging (batteries); Charging stations; Electric vehicles; Optimization; Smart city; Sustainable development; Charging infrastructures; Charging station; Covering location problems; Effective solution; Electric vehicle charging; Electric vehicles chargers; Fuel station; Maximal covering location problem; Optimal placements; Optimal positioning; Land use},
  correspondence_address = {F. Signorile; Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, 70125, Italy; email: ti.ab1765467277ilop.1765467277dhp@21765467277eliro1765467277ngis.1765467277f1765467277; F. Mastromarino; Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, 70125, Italy; email: ti.ab1765467277ilop@1765467277onira1765467277morts1765467277am.oi1765467277baf1765467277; P. Scarabaggio; Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, 70125, Italy; email: ti.ab1765467277ilop@1765467277oigga1765467277barac1765467277s.olo1765467277ap1765467277; V. Gialò; Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, 70125, Italy; email: ti.ab1765467277ilop.1765467277itned1765467277uts@o1765467277laig.1765467277v1765467277; R. Carli; Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, 70125, Italy; email: ti.ab1765467277ilop@1765467277ilrac1765467277.elea1765467277ffar1765467277; M. Dotoli; Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, 70125, Italy; email: ti.ab1765467277ilop@1765467277iloto1765467277d.aiz1765467277argai1765467277ram1765467277},
  editor = {Volta, M.},
  publisher = {Elsevier B.V.},
  issn = {24058963; 24058971; 14746670},
  isbn = {9783902661869; 9788374810357; 8374810351; 9783902661463; 9783902661586; 9783902661906; 9783902661104; 9783902823007; 9783902823243; 9783902823106},
  language = {English},
  abbrev_source_title = {IFAC-PapersOnLine},
  type = {Conference paper},
  publication_stage = {Final},
  source = {Scopus},
  note = {Cited by: 0; All Open Access; Gold Open Access}
  }

• Mastromarino, F., Scarabaggio, P., Carli, R. & Dotoli, M. (2025) Voxel-Based Hierarchical Approximate Convex Decomposition for Efficient 3D Representation of Objects in Robotic Applications IN IEEE International Conference on Automation Science and Engineering.IEEE Computer Society, 159-164. doi:10.1109/CASE58245.2025.11164152
  [BibTeX] [Abstract] [Download PDF]

  Approximate Convex Decomposition (ACD) is essential for industrial robotics, enabling efficient collision detection, motion planning, and physics-based simulation of robotic manipulators. However, traditional ACD methods, such as Hierarchical ACD (HACD) and Volumetric HACD (V-HACD), often suffer from high computational costs and over-segmentation, making them unsuitable for real-time robotic applications. This paper presents a novel voxel-based HACD (VX-HACD) approach designed to enhance computational efficiency while preserving the geometric fidelity of robotic manipulator components. The proposed approach first converts the input mesh into a structured voxel grid, simplifying the convex decomposition process. A gap-filling algorithm ensures topological continuity, preventing segmentation artifacts caused by voxel discretization. Additionally, a hierarchical voxel aggregation strategy reduces the number of convex components while maintaining accuracy, optimizing the representation for robotic applications. The methodology is validated on high-complexity robotic manipulator components, demonstrating reduced processing times, lower volumetric error, and fewer convex components compared to state-of-the-art ACD techniques. The proposed approach, while validated in the context of industrial robotics for collision-aware motion planning, can be applied to a wide range of applications requiring efficient convex decomposition in high-performance simulation (e.g., precision or surgical robotics, video games, and physics simulation). © 2025 Elsevier B.V., All rights reserved.

  @CONFERENCE{Mastromarino2025159,
  author = {Mastromarino, Fabio and Scarabaggio, Paolo and Carli, Raffaele and Dotoli, Mariagrazia},
  title = {Voxel-Based Hierarchical Approximate Convex Decomposition for Efficient 3D Representation of Objects in Robotic Applications},
  year = {2025},
  journal = {IEEE International Conference on Automation Science and Engineering},
  pages = {159 - 164},
  doi = {10.1109/CASE58245.2025.11164152},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105018303126&doi=10.1109%2FCASE58245.2025.11164152&partnerID=40&md5=c6a7f808e2810378e2bf810f4d31b76e},
  affiliations = {Politecnico di Bari, Department of Electronic and Information Engineering, Bari, Italy},
  abstract = {Approximate Convex Decomposition (ACD) is essential for industrial robotics, enabling efficient collision detection, motion planning, and physics-based simulation of robotic manipulators. However, traditional ACD methods, such as Hierarchical ACD (HACD) and Volumetric HACD (V-HACD), often suffer from high computational costs and over-segmentation, making them unsuitable for real-time robotic applications. This paper presents a novel voxel-based HACD (VX-HACD) approach designed to enhance computational efficiency while preserving the geometric fidelity of robotic manipulator components. The proposed approach first converts the input mesh into a structured voxel grid, simplifying the convex decomposition process. A gap-filling algorithm ensures topological continuity, preventing segmentation artifacts caused by voxel discretization. Additionally, a hierarchical voxel aggregation strategy reduces the number of convex components while maintaining accuracy, optimizing the representation for robotic applications. The methodology is validated on high-complexity robotic manipulator components, demonstrating reduced processing times, lower volumetric error, and fewer convex components compared to state-of-the-art ACD techniques. The proposed approach, while validated in the context of industrial robotics for collision-aware motion planning, can be applied to a wide range of applications requiring efficient convex decomposition in high-performance simulation (e.g., precision or surgical robotics, video games, and physics simulation). © 2025 Elsevier B.V., All rights reserved.},
  keywords = {Computational efficiency; Industrial manipulators; Motion planning; Robot programming; Three dimensional computer graphics; Topology; 3d representations; Approximate convex decompositions; Collision detection; Convex decomposition; Decomposition methods; Industrial robotics; Motion-planning; Physics-based Simulation; Robotic manipulators; Robotics applications; Flexible manipulators},
  correspondence_address = {F. Mastromarino; The Department of Electrical and Information Engineering, The Polytechnic of Bari, Italy; email: ti.ab1765467277ilop@1765467277onira1765467277morts1765467277am.oi1765467277baf1765467277},
  publisher = {IEEE Computer Society},
  issn = {21618089; 21618070},
  isbn = {9781665490429; 9781728103556; 9781509067800; 9781467381833; 9798350320695; 9781509024094; 9781479952830; 9781538635933; 9781479915156; 9781457717307},
  language = {English},
  abbrev_source_title = {IEEE Int. Conf. Autom. Sci. Eng.},
  type = {Conference paper},
  publication_stage = {Final},
  source = {Scopus},
  note = {Cited by: 0}
  }