Welcome! I am an Associate Professor of Robotics Engineering in the College of Engineering at Northeastern University. I am the Director of the Robotics and Intelligent Vehicles Research (RIVeR) Laboratory and a co-founder of the Robotics Collaborative at Northeastern.
I received my PhD and MS degrees in electrical and computer engineering from Purdue University. I hold a BS in electrical and electronics engineering from the Middle East Technical University in Turkey. Our research interests, in my group, include supervised autonomy for humanoid robots, shared autonomy for intelligent
vehicles, and human-in-the-loop control systems. Our projects have been sponsored by NSF, NASA, DOE-EM, DARPA, and numerous industry partners. I led project teams for the NASA Sample Return Robot Centennial Challenge, SmartAmerica Challenge and the DARPA Robotics Challenge. I currently lead one of two research groups selected by NASA to develop autonomy for the humanoid robot Valkyrie, designed by the NASA Johnson Space Center. Please visit the lab pages for more information on our projects, robot.neu.edu. I teach Engineering Algorithms at the undergraduate level, and Mobile Robotics, Principles of Assistive Robotics, and Humanoids Robotics at the graduate level on a rotating basis.
Please click on the title of the papers to access them. A complete list is provided here.
This paper introduces an anytime synthesized motion planning algorithm for humanoid robots unifying locomotion and manipulation planning. It generates an entire set of motions to finish specific tasks in an environment containing obstacles by exploiting a powerful inverse kinematics (IK) engine. The IK engine can compute solutions allowing the robot to reposition its feet for meeting the task requirements. The presented planning algorithm has two primary beneficial capabilities. First, it is capable of generating a motion plan to complete a task handling multiple ordered or unordered actions. Second, it produces an initial solution very quickly, and then searches for the opportunity to improve the the solution during execution. The performance of the proposed algorithm is evaluated on the NASA-JSC Valkyrie humanoid robot by demonstrating an object pick up task in simulation and a box pick-and-place task in the real world.
Path planning on a 2D-grid is a well-studied problem in robotics. It usually involves searching for a shortest path between two vertices on a grid. Single-source path planning is a modified problem which asks to find distances from a given point to all other points on the map. A high-performance algorithm for single-source any-angle path planning on a grid that we named CWave is proposed in this work. “Any-angle” attribute of a path planning algorithm implies that such algorithm can find paths which may include any angle segments, as opposed to standard A* on an 8-connected graph, the path can turn with 45°-increments only. The key idea of the presented algorithm is that it does not represent the grid as a graph and uses discrete geometric primitives to define the wave front. In its purest form, CWave requires for computation only integer arithmetics and multiplication by two, but can accumulate the distance error at turning points. A modified version of CWave with minimal usage of floating-point calculations is also developed. It allows to eliminate any accumulative errors which is proven mathematically and experimentally on several maps. The performance of the algorithm on three maps is demonstrated to be significantly faster than that of Theta*, Lazy Theta* and Field A* adapted for single-source planning. The limitations of the current implementations of the algorithm as well as potential improvements are discussed.
In the DARPA Robotics Challenge (DRC), participating human-robot teams were required to integrate mobility, manipulation, perception, and operator interfaces to complete a simulated disaster mission. We describe our approach using the humanoid robot Atlas Unplugged developed by Boston Dynamics. We focus on our approach, results, and lessons learned from the DRC Finals to demonstrate our strategy, including extensive operator practice, explicit monitoring for robot errors, adding additional sensing, and enabling the operator to control and monitor the robot at varying degrees of abstraction. Our safety-first strategy worked: we avoided falling, and remote operators could safely recover from difficult situations. We were the only team in the DRC Finals that attempted all tasks, scored points (14/16), did not require physical human intervention (a reset), and did not fall in the two missions during the two days of tests. We also had the most consistent pair of runs.
Motions of a robot interacting with its environment can be described by a set of constraints. This paper introduces an approach, called motion template, which can quickly program and compose the constraints for the motion planner to generate the trajectory. Two types of motion templates, grasp and turn, are specifically described to explain the details of the technique. The reusability and shareability properties of the motion template are demonstrated using a variety of the motion planning applications across different robot platforms. A motion template framework is used to implement the motion template with the trajectory optimization.
Courses & more...
This course covers the foundational concepts on the design, implementation, analysis, and experimental evaluation of engineering algorithms. Visit the course page for more information and latest news.
This course covers mathematical models, algorithms and engineering of autonomous mobile robots. Use-inspired projects will bridge the gap between the theory and practice. Visit the course page for more information and latest news.