Marco Frego

About

Marco Frego’s research sits at the intersection of assistive robotics, autonomous motion planning, and optimal control, with a focus on creating safe, comfortable, and efficient paths for robots that interact closely with humans. His most influential work tackles the challenge of reactive planning for assistive robots—such as robotic walking assistants—that must navigate crowded environments while respecting user comfort and safety. His 2018 paper on reactive planning for assistive robots (43 citations) and his 2016 work on path planning for human comfort (39 citations) are foundational, demonstrating how to balance obstacle avoidance with the user’s perceived ease of motion. Frego has also made significant contributions to theoretical path planning, notably through his iterative dynamic programming approach to the multipoint Markov-Dubins problem (15 citations), which solves shortest-path problems with curvature constraints. His work extends to practical applications like minimum-time and minimum-jerk traffic management for automated guided vehicles (14 citations) and efficient re-planning for robotic cars (14 citations). Beyond these, Frego has explored novel robotic platforms, including the CLIO rope-aided climbing robot for mountain rescue missions (5 citations), and has investigated the use of GPUs to accelerate motion planning. His research is widely cited for its blend of rigorous optimization theory and real-world robotic deployment.

Research Focus

Key Achievements

Top Papers

1. 1
   Reactive Planning for Assistive Robots

   43 citations · 2018
2. 2
   Path planning maximising human comfort for assistive robots

   39 citations · 2016
3. 3
   An Iterative Dynamic Programming Approach to the Multipoint Markov-Dubins Problem

   15 citations · 2020
4. 4
   Minimum Time—Minimum Jerk Optimal Traffic Management for AGVs

   14 citations · 2020
5. 5
   Efficient Re-planning for Robotic Cars

   14 citations · 2018
6. 6
   Efficient intersection between splines of clothoids

   11 citations · 2019
7. 7
   Activity Planning for Assistive Robots Using Chance-Constrained Stochastic Programming

   8 citations · 2020
8. 8
   A new Markov–Dubins hybrid solver with learned decision trees

   7 citations · 2023
9. 9
   CLIO: a Novel Robotic Solution for Exploration and Rescue Missions in Hostile Mountain Environments

   5 citations · 2023
10. 10
    Robot Motion Planning: can GPUs be a Game Changer?

    4 citations · 2021

Key Collaborators

Contact & Links