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Posts
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portfolio
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publications
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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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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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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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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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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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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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 Machine Learning Approach for the Three-Point Dubins Problem (3PDP)
Published in Symmetry, 17(12): 2133, 2025
Presents a novel machine learning approach to solve the Three-Point Dubins Problem (3PDP), enhancing path planning for autonomous vehicles.
Recommended citation: Saccon, E.; Frego, M. A Machine Learning Approach for the Three-Point Dubins Problem (3PDP). Symmetry 2025, 17, 2133.
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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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talks
Conference Proceeding Talk at IEEE COMPSAC 2021
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In this work, we introduce a GPU-parallel implementation of the Iterative Dynamic Programming (IDP) algorithm for solving the multi-point Markov–Dubins problem, which seeks the shortest bounded-curvature path through several waypoints. Unlike traditional optimization methods (NLP/MINLP), this approach is inherently parallelizable and significantly improves accuracy, speed, and energy efficiency, making it well suited for embedded and real-time applications. Follow the title link for more info.
Conference Talk at AIxIA 2022
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Presented the paper Comparing Multi-Agent Path Finding Algorithms in a Real Industrial Scenario, benchmarking several MAPF algorithms on a factory logistics environment, bridging academic MAPF solutions with real-world robotic systems.
Doctoral Consortium Talk at SoCS 2023
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Presented the extended abstract Multi-Agent Open Framework: Developing a Holistic System to Solve MAPF, describing an open-source framework that integrates Multi-Agent Path Finding (MAPF) algorithms and task allocation mechanisms for scalable robotic coordination.
Conference Talk at IEEE ICRA 2024
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Presented the paper [When Prolog Meets Generative Models: A New Approach for Managing Knowledge and Planning in Robotic Applications], showing how Large Language Models (LLMs) can be paired with Prolog-based symbolic reasoning to automatically generate and refine robotic knowledge bases for planning and control.
Conference Talks at ICAPS 2024
Published:
Presented two contributions at ICAPS 2024:
- Previously Published Paper Track: [When Prolog Meets Generative Models: A New Approach for Managing Knowledge and Planning in Robotic Applications] — a framework combining probabilistic logic programming and generative models to create flexible, scalable robot planning systems.
- Doctoral Consortium: Adaptive and Scalable Knowledge Management for Robotic Applications via Probabilistic Logic Languages — highlighting ongoing PhD work on logic-based adaptive task planning.
teaching
Real Time Operating Systems and Middlewares Teaching Assistant
Graduate course, University of Trento, Department of Information Engineering and Computer Science, 2022
Helped the students of the “Real Time Operating Systems and Middlewares” course to understand the concepts and practical applications. Assisted with lab sessions, answered questions, and provided guidance on assignments and projects.
Programming 101 Tutor
Undergraduate course, University of Trento, Department of Information Engineering and Computer Science, 2023
Helped the students of the “Programming 101” course to learn the basics of programming in C. Assisted with lab sessions, answered questions, and provided guidance on assignments.
Robot Planning and its Applications Teaching Assistant
Graduate course, University of Trento, Department of Information Engineering and Computer Science, 2023
Assisted the professor in conducting the “Robot Planning and its Applications” course. Responsibilities included helping students during lab sessions, clarifying concepts related to robot planning algorithms, and supporting them with assignments and projects focused on autonomous robotics applications. Also, took care of developing the basis for the project, and grading and providing feedback on the students’ work.
Robot Planning and its Applications Teaching Assistant
Graduate course, University of Trento, Department of Information Engineering and Computer Science, 2024
Assisted the professor in conducting the “Robot Planning and its Applications” course. Responsibilities included helping students during lab sessions, clarifying concepts related to robot planning algorithms, and supporting them with assignments and projects focused on autonomous robotics applications. Also, took care of developing the basis for the project, and grading and providing feedback on the students’ work.
Robot Planning and its Applications Tutor
Graduate course, University of Trento, Department of Information Engineering and Computer Science, 2025
Tutored students in the “Robot Planning and its Applications” course. Responsibilities included assisting during lab sessions, clarifying concepts related to robot planning algorithms, and providing support with assignments and projects focused on autonomous robotics applications.