Organizational Unit:
Humanoid Robotics Laboratory
Humanoid Robotics Laboratory
Permanent Link
Research Organization Registry ID
Description
Previous Names
Parent Organization
Parent Organization
Organizational Unit
Includes Organization(s)
ArchiveSpace Name Record
Publication Search Results
Now showing
1 - 5 of 5
-
ItemKrang Kinematics: A Denavit-Hartenberg Parameterization(Georgia Institute of Technology, 2014) Erdogan, Can ; Zafar, Munzir ; Stilman, Mike
-
ItemKrang: Center of Mass Estimation(Georgia Institute of Technology, 2014) Zafar, Munzir ; Erdogan, Can ; Volle, Kyle ; Stilman, Mike
-
ItemTowards Stable Balancing(Georgia Institute of Technology, 2014) Zafar, Munzir ; Erdogan, Can ; Stilman, Mike
-
ItemGravity and Drift in Force/Torque Measurements( 2014) Erdogan, Can ; Zafar, Munzir ; Stilman, Mike
-
ItemPlanning in Constraint Space: Automated Design of Functional Structures(Georgia Institute of Technology, 2013-05) Erdogan, Can ; Stilman, MikeOn the path to full autonomy, robotic agents have to learn how to manipulate their environments for their benefit. In particular, the ability to design structures that are functional in overcoming challenges is imperative. The problem of automated design of functional structures (ADFS) addresses the question of whether the objects in the environment can be placed in a useful configuration. In this work, we first make the observation that the ADFS problem represents a class of problems in high dimensional, continuous spaces that can be broken down into simpler subproblems with semantically meaningful actions. Next, we propose a framework where discrete actions that induce constraints can partition the solution space effectively. Subsequently, we solve the original class of problems by searching over the available actions, where the evaluation criteria for the search is the feasibility test of the accumulated constraints. We prove that with a sound feasibility test, our algorithm is complete. Additionally, we argue that a convexity requirement on the constraints leads to significant efficiency gains. Finally, we present successful results to the ADFS problem.