TUBITAK 1001 – Project Number: 122E249
A data-driven approach for developing and repairing robot controllers
Summary: This project focuses on a novel data‑driven method for designing and repairing controllers for unmanned ground and surface vehicles using formal behavior patterns. It begins by simulating trajectories with a diverse set‑valued controller—ranging from exploratory approaches to those trained via reinforcement or deep learning—and labeling outcomes as successes or failures. Next, logical formulas are automatically synthesized to capture the patterns behind positive and negative behaviors. Finally, the controller is iteratively repaired so that future simulations adhere to these specifications, refining performance over successive loops. This enables clear, human‑readable insights into otherwise opaque learned controllers, enhances robustness against real‑world uncertainty, and operates without needing explicit system models. The method has been validated both in simulation and on physical UGV and USV platforms.
TUBITAK 1001 Programme Project Number: 118E195
Feedback Motion Planning of an Unmanned Surface Vehicle via Random Sequential Composition
Project Summary : In this project proposal, our goal is to develop an original unmanned surface vehicle (USV), and a novel methodology that solves the path planning and navigation problems for this system (and other USV systems) in a robust and computationally feasible way. Our approach in solving the motion planning problem is based on synthesizing two different existing methodologies. One of these methods is sampling based path planning, and the other one is the sequential composition of dynamic behaviors. Our aim for adopting such a synthesis route is to create a hybrid framework that exploits the strengths of two different approaches and removes the possible weak points of each method. In this project proposal, first we plan to develop the algorithm and test the results in a simulation environment. In parallel to these simulation studies, we aim to develop a new USV that can be used effectively in robust motion planning and navigation studies. After the simulation studies and full development of USV, we are planning to implement and test the methods on the developed USV system using field experiments.