Motion planning is one of key challenges in robotics today. When determining how a robot should perform a task, both environmental and performance factors should be considered. For example, obstacles in the environment should be avoided in order to prevent collision, while also taking into account sensor measurement uncertainty and energy consumption restrictions. Currently, there are many different methods of approaching this problem, ranging from classical optimization to deep learning.

Filip is developing motion planning algorithms with a focus on mobile manipulators, whose functionality encompasses a wide range of tasks. Currently, he is exploring the connection between state estimation and motion planning in collaboration with the LAMoR group at the University of Zagreb.

Manipulability Optimization for Manipulator Motion

CHigh and low manipulability variants of trajectories performing the same task.

Manipulability Maximization using Continuous-time Gaussian Processes
Filip Marić, Oliver Limoyo, Luka Petrović, Trevor Ablett, Ivan Petrović, Jonathan Kelly
arXiv pre-print.

Global Polynomial Optimization for Robot Kinematics

Convex relaxations for polynomial formulations of inverse kinematics.

Inverse Kinematics for Serial Kinematic Chains via Sum of Squares Optimization
Filip Marić, Matthew Giamou, Soroush Khoubyarian, Ivan Petrović, Jonathan Kelly
arXiv pre-print.