Enrico Saccon

Enrico Saccon

PhD Student in Computer Science and Robotics.

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Automated Generation of MDPs Using Logic Programming and LLMs for Robotic Applications

Published in IEEE Robotics and Automation Letters, 11(2): 1770-1777, 2025

Presents a novel framework that leverages logic programming and large language models (LLMs) to automatically generate Markov Decision Processes (MDPs) for complex robotic tasks.

Recommended citation: E. Saccon, D. De Martini, M. Saveriano, E. Lamon, L. Palopoli and M. Roveri, "Automated Generation of MDPs Using Logic Programming and LLMs for Robotic Applications," in IEEE Robotics and Automation Letters, vol. 11, no. 2, pp. 1770-1777, Feb. 2026
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Fast Shortest Path Polyline Smoothing with G¹ Continuity and Bounded Curvature

Published in IEEE Robotics and Automation Letters, 10(4): 3182–3189, 2025

Introduces an efficient algorithm for smoothing shortest paths while ensuring G¹ continuity and bounded curvature—achieving real-time feasibility for robotic navigation.

Recommended citation: P. Pastorelli, S. Dagnino, E. Saccon, M. Frego, and L. Palopoli. (2025). "Fast Shortest Path Polyline Smoothing with G¹ Continuity and Bounded Curvature." IEEE Robotics and Automation Letters, 10(4): 3182–3189.
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A Temporal Planning Framework for Multi-Agent Systems via LLM-Aided Knowledge Base Management [Preprint]

Published in arXiv preprint, arXiv:2502.19135 [cs.AI], 2025

Proposes a logic-driven temporal planning architecture for multi-agent systems, leveraging large language models for dynamic knowledge base construction and adaptation.

Recommended citation: E. Saccon, A. Tikna, D. D. Martini, E. Lamon, L. Palopoli, and M. Roveri. (2025). "A Temporal Planning Framework for Multi-Agent Systems via LLM-Aided Knowledge Base Management." arXiv:2502.19135 [cs.AI].
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An Iterative Dynamic Programming Approach to the Multipoint Markov-Dubins Problem

Published in IEEE Robotics and Automation Letters, 2020

Introduces the Iterative Dynamic Programming (IDP) algorithm for efficiently computing curvature-bounded shortest paths between multiple waypoints.

Recommended citation: M. Frego, P. Bevilacqua, E. Saccon, L. Palopoli, and D. Fontanelli. (2020). "An Iterative Dynamic Programming Approach to the Multipoint Markov-Dubins Problem." IEEE Robotics and Automation Letters, 5(2). doi:10.1109/LRA.2020.2972787.
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Conference Papers

When Prolog Meets Generative Models: A New Approach for Managing Knowledge and Planning in Robotic Applications

Published in IEEE International Conference on Robotics and Automation (ICRA 2024), 2024

Combines logic programming and generative models to automate the creation of robotic knowledge bases, enabling flexible, interpretable, and scalable task planning.

Recommended citation: E. Saccon, A. Tikna, D. De Martini, E. Lamon, L. Palopoli, and M. Roveri. (2024). "When Prolog Meets Generative Models: A New Approach for Managing Knowledge and Planning in Robotic Applications." IEEE International Conference on Robotics and Automation (ICRA).
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Multi-Agent Open Framework: Developing a Holistic System to Solve MAPF (Student Abstract)

Published in Proceedings of the International Symposium on Combinatorial Search (SoCS 2023), 2023

Describes an open-source platform integrating multiple MAPF algorithms and heuristics for benchmarking and research in cooperative multi-robot systems.

Recommended citation: E. Saccon. (2023). "Multi-Agent Open Framework: Developing a Holistic System to Solve MAPF (Student Abstract)." Proceedings of the International Symposium on Combinatorial Search (SoCS 2023), 16: 198–199.
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Comparing Multi-Agent Path Finding Algorithms in a Real Industrial Scenario

Published in AIxIA 2022 – Advances in Artificial Intelligence, 2022

Benchmarks leading MAPF algorithms in a real industrial logistics environment, bridging theoretical approaches with practical robotic deployment.

Recommended citation: E. Saccon, L. Palopoli, and M. Roveri. (2023). "Comparing Multi-Agent Path Finding Algorithms in a Real Industrial Scenario." AIxIA 2022 – Advances in Artificial Intelligence, pp. 184–197. doi:10.1007/978-3-031-27181-6_13.
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Robot Motion Planning: Can GPUs Be a Game Changer?

Published in IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC 2021), 2021

Presents a GPU-parallel implementation of Iterative Dynamic Programming (IDP) for the multi-point Markov–Dubins problem, achieving substantial performance gains over CPU methods.

Recommended citation: E. Saccon, P. Bevilacqua, D. Fontanelli, M. Frego, L. Palopoli, and R. Passerone. (2021). "Robot Motion Planning: Can GPUs Be a Game Changer?" IEEE COMPSAC 2021, pp. 21–30. doi:10.1109/COMPSAC51774.2021.00015.
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