Puttichai Lertkultanon

Nanyang Technological University | ntu · School of Mechanical and Aerospace Engineering (MAE)

Planning motions for two robot arms to move an object collaboratively is a difficult problem, mainly because of the closed-chain constraint, which arises whenever two robot hands simultaneously grasp a single rigid object. In this paper, we propose a manipulation planning algorithm to bring an object from an initial stable placement (position and o...

Closed kinematic chains are created whenever multiple robot arms concurrently manipulate a single object. The closed-chain constraint, when coupled to robot joint and reachability limits, dramatically reduces the extent of the configuration space and changes its connectivity. We propose a regrasping move, termed "IK-switch", which allows efficientl...

Path-velocity decomposition is an intuitive yet powerful approach to addressing the complexity of kinodynamic motion planning. The difficult trajectory planning problem is solved in two separate, simpler steps: first, a path is found in the configuration space that satisfies the geometric constraints (path planning), and second, a time-parameteriza...

In manipulation tasks, a robot interacts with movable object(s). The configuration space in manipulation planning is thus the Cartesian product of the configuration space of the robot with those of the movable objects. It is the complex structure of such a "Composite Configuration Space" that makes manipulation planning particularly challenging. Pr...

When possible, non-prehensile transportation (i.e. transporting objects without grasping them) can be faster and more efficient than prehensile transportation. However, the need to explicitly consider reaction and friction forces yields kino-dynamic constraints that are difficult to take into account by traditional planning algorithms. Based on the...

Path-velocity decomposition is an intuitive yet powerful approach to address the complexity of kinodynamic motion planning. The difficult trajectory planning problem is solved in two separate steps: first, find a path in the configuration space that satisfies the geometric constraints, and second, find a time-parameterization of that path satisfyin...

This paper presents a convex framework for problems of fitting multivariate autoregressive (AR) models that cooperate Granger causality constraints to fMRI time series which describe the dynamics of the human brain activity level. The Granger causality characterizes a relationship structure of variables in the system and can be explained from a com...