Education:

2024 - Ing.  in Cybernetics and Robotics, FEE, CTU in Prague, thesis Model Predictive Path Integral Control of a Drone Using a Database of Motion Primitives supervised by V. Vonásek (Dean's Award for Outstanding Thesis)

2021 - Bc.  in Cybernetics and Robotics, FEE, CTU in Prague, thesis Improving Sampling-Based Motion Planning Using Library of Trajectories supervised by V. Vonásek (Dean's Award for Outstanding Thesis)

1. Michal Minařík, Vojtěch Vonásek and Robert Pěnička. Enhancing sampling-based planning with a library of paths. Robotics and Autonomous Systems 198:105334, April 2026. URL video code, DOI BibTeX

   @article{MINARIK2026105334,
   	title = "Enhancing sampling-based planning with a library of paths",
   	journal = "Robotics and Autonomous Systems",
   	volume = 198,
   	pages = 105334,
   	year = 2026,
   	month = "April",
   	issn = "0921-8890",
   	doi = "https://doi.org/10.1016/j.robot.2026.105334",
   	url = "https://www.sciencedirect.com/science/article/pii/S0921889026000072",
   	author = "Michal Minařík and Vojtěch Vonásek and Robert Pěnička",
   	keywords = "Path planning, Sampling-based planners, Guided planning, 3D object similarity, Library",
   	abstract = "Path planning for 3D solid objects is a challenging problem, requiring a search in a six-dimensional configuration space, which is, nevertheless, essential in many robotic applications such as bin-picking and assembly. The commonly used sampling-based planners, such as Rapidly-exploring Random Trees, struggle with narrow passages where the sampling probability is low, increasing the time needed to find a solution. In scenarios like robotic bin-picking, various objects must be transported through the same environment. However, traditional planners start from scratch each time, losing valuable information gained during the planning process. We address this by using a library of past solutions, allowing the reuse of previous experiences even when planning for a new, previously unseen object. Paths for a set of objects are stored, and when planning for a new object, we find the most similar one in the library and use its paths as approximate solutions, adjusting for possible mutual transformations. The configuration space is then sampled along the approximate paths. Our method is tested in various narrow passage scenarios and compared with state-of-the-art methods from the OMPL library. Results show significant speed improvements (up to 85% decrease in the required time) of our method, often finding a solution in cases where the other planners fail. Our implementation of the proposed method is released as an open-source package.",
   	code = "https://github.com/m-minarik/rrtlib",
   	video = "https://youtu.be/1BTlWC742Aw"
   }
   

2. Michal Minařík, Pěnička Robert, Vojtěch Vonásek and Martin Saska. Model Predictive Path Integral Control for Agile Unmanned Aerial Vehicles. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2024, 13144-13151. PDF, DOI BibTeX

   @inproceedings{MinarikAgileMPPI20242,
   	title = "Model Predictive Path Integral Control for Agile Unmanned Aerial Vehicles",
   	author = "Mina\v{r}\'{i}k, Michal and P\v{e}ni\v{c}ka, Robert, and Von\'{a}sek, Vojt\v{e}ch and Saska, Martin",
   	year = 2024,
   	pages = "13144-13151",
   	booktitle = "2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)",
   	pdf = "data/papers/iros2024minarik.pdf",
   	doi = "10.1109/IROS58592.2024.10802756"
   }
   

3. Petr Ježek, Michal Minařík, Vojtěch Vonásek and Robert Pěnička. KRRF: Kinodynamic Rapidly-Exploring Random Forest Algorithm for Multi-Goal Motion Planning. IEEE Robotics and Automation Letters ():1-8, 2024. URL video code, DOI BibTeX

   @article{jezek2024krrf,
   	author = "Je\v{z}ek, Petr and Mina\v{r}\'{i}k, Michal and Von\'{a}sek, Vojt\v{e}ch and P\v{e}ni\v{c}ka, Robert",
   	journal = "IEEE Robotics and Automation Letters",
   	title = "KRRF: Kinodynamic Rapidly-Exploring Random Forest Algorithm for Multi-Goal Motion Planning",
   	year = 2024,
   	volume = "",
   	number = "",
   	pages = "1-8",
   	keywords = "Trajectory;Planning;Costs;Random forests;Robots;Computational modeling;Space exploration;Collision avoidance;Traveling salesman problems;Robot kinematics;Motion and Path Planning;Planning, Scheduling and Coordination",
   	doi = "10.1109/LRA.2024.3478570",
   	url = "https://ieeexplore.ieee.org/document/10714001",
   	code = "https://github.com/ctu-mrs/krrf",
   	video = "https://www.youtube.com/watch?v=KLneA8Mkep4"
   }