M. Mert Ankarali — accepted AI-conference papers

2020

MPC-Graph: Feedback Motion Planning Using Sparse Sampling Based Neighborhood Graph

O. Kaan Karagoz, Simay Atasoy, M. Mert Ankarali

IROS 2020poster

Robust and safe feedback motion planning and navigation is a critical task for autonomous mobile robotic systems considering the highly dynamic and uncertain nature scenarios of modern applications. For these reasons motion planning and navigation algorithms that have deep roots in feedback control…

Cited by 7 Source ↗Scholar ↗

2018

RG-Trees: Trajectory-Free Feedback Motion Planning Using Sparse Random Reference Governor Trees

Ferhat Golbol, M. Mert Ankarali, Afsar Saranli

IROS 2018poster

Sampling based methods resulted in feasible and effective motion planning algorithms for high dimensional configuration spaces and complex environments. A vast majority of such algorithms as well as their application rely on generating a set of open-loop trajectories first, which are then tracked by…

Cited by 16 Source ↗Scholar ↗