Marco Locatelli
Papers
9
Total Citations
57
H-Index
5
About
Marco Locatelli is a researcher whose work sits at the intersection of combinatorial optimization, robotics, and industrial automation. His research focuses primarily on two interconnected domains: pallet building problems for robotic palletizing systems and motion planning for robotic manipulators. Within the pallet building domain, Locatelli has made significant contributions by developing mathematical models, heuristic algorithms, and dynamic programming approaches that address real-world industrial constraints. His introduction of visibility and contiguity constraints — motivated by actual robotized pallet-building applications — represents a meaningful advance in bridging theoretical optimization and industrial practice. Papers exploring these problems have collectively accumulated over 40 citations, with his GRASP-based algorithm (2020) and mathematical modeling work (2021) being the most recognized. In robot motion planning, Locatelli has tackled challenges including minimum-jerk online trajectory generation, time-optimal speed planning for manipulators, and real-time management of kinematic singularities. His 2013 work on minimum-jerk planning through mathematical programming, with 10 citations, reflects early influence in computationally efficient online planning methods. Overall, Locatelli's research is characterized by its strong applied orientation, consistently translating rigorous mathematical optimization techniques into practical solutions for industrial robotics and automation systems.
Research Focus
Key Achievements
Top Papers
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- 3Minimum-jerk online planning by a mathematical programming approach10 citations · 2013
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- 6Optimal Time-Complexity Speed Planning for Robot Manipulators4 citations · 2019
- 7Optimizing Cooperative Pallet Loading Robots: A Mixed Integer Approach4 citations · 2021
- 8A Mixed Approach for Pallet Building Problem with Practical Constraints3 citations · 2021
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