Now showing items 1-10 of 10

    • Combining Motion Planning and Optimization for Flexible Robot Manipulation 

      Scholz, Jonathan; Stilman, Mike (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2010-12)
      Robots that operate in natural human environments must be capable of handling uncertain dynamics and underspecified goals. Current solutions for robot motion planning are split between graph-search methods, such as ...
    • Dynamic Pushing Strategies for Dynamically Stable Mobile Manipulators 

      Kolhe, Pushkar; Dantam, Neil; Stilman, Mike (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2010-05)
      This paper presents three effective manipulation strategies for wheeled, dynamically balancing robots with articulated links. By comparing these strategies through analysis, simulation and robot experiments, we show ...
    • Golem Krang: Dynamically Stable Humanoid Robot for Mobile Manipulation 

      Stilman, Mike; Olson, Jon; Gloss, William (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2010-05)
      What would humans be like if nature had invented the wheel? Golem Krang is a novel humanoid torso designed at Georgia Tech. The robot dynamically transforms from a .5 m static to a 1.5 m dynamic configuration. Our robot ...
    • The Motion Grammar: Linguistic Perception, Planning, and Control 

      Dantam, Neil; Stilman, Mike (Georgia Institute of TechnologyMIT, 2011-06)
      We present and analyze the Motion Grammar: a novel unified representation for task decomposition, perception, planning, and control that provides both fast online control of robots in uncertain environments and the ...
    • Path Planning Among Movable Obstacles: A Probabilistically Complete Approach 

      van den Berg, Jur; Stilman, Mike; Kuffner, James; Lin, Ming; Manocha, Dinesh (Georgia Institute of Technology, 2008)
      In this paper we study the problem of path planning among movable obstacles, in which a robot is allowed to move the obstacles if they block the robot's way from a start to a goal position. We make the observation that ...
    • Planning Among Movable Obstacles with Artificial Constraints 

      Stilman, Mike; Kuffner, James J. (Georgia Institute of TechnologySage Publications, 2008-11)
      This paper presents artificial constraints as a method for guiding heuristic search in the computationally challenging domain of motion planning among movable obstacles. The robot is permitted to manipulate unspecified ...
    • Probabilistic Human Action Prediction and Wait-sensitive Planning for Responsive Human-robot Collaboration 

      Hawkins, Kelsey P.; Vo, Nam; Bansal, Shray; Bobic, Aaron F. (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2013-10)
      A novel representation for the human component of multi-step, human-robot collaborative activity is presented. The goal of the system is to predict in a probabilistic manner when the human will perform different subtasks ...
    • Push Planning for Object Placement on Cluttered Table Surfaces 

      Cosgun, Akansel; Hermans, Tucker; Emeli, Victor; Stilman, Mike (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2011-09)
      We present a novel planning algorithm for the problem of placing objects on a cluttered surface such as a table, counter or floor. The planner (1) selects a placement for the target object and (2) constructs a sequence ...
    • Robot Path Planning Using Field Programmable Analog Arrays 

      Koziol, Scott; Hasler, Paul; Stilman, Mike (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2012-05)
      We present the successful application of reconfigurable Analog-Very-Large-Scale-Integrated (AVLSI) circuits to motion planning for the AmigoBot robot. Previous research has shown that custom application-specific-integ ...
    • Sampling Heuristics for Optimal Motion Planning in High Dimensions 

      Akgun, Baris; Stilman, Mike (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2011-09)
      We present a sampling-based motion planner that improves the performance of the probabilistically optimal RRT* planning algorithm. Experiments demonstrate that our planner finds a fast initial path and decreases the ...