Organizational Unit:
Humanoid Robotics Laboratory

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Now showing 1 - 5 of 5
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    Multi-Process Control Software for HUBO2 Plus Robot
    (Georgia Institute of Technology, 2013-04) Grey, M .X. ; Dantam, Neil ; Lofaro, Daniel M. ; Bobick, Aaron F. ; Egerstedt, Magnus B. ; Oh, Paul ; Stilman, Mike
    Humanoid robots require greater software reliability than traditional mechantronic systems if they are to perform useful tasks in typical human-oriented environments. This paper covers a software architecture which distributes the load of computation and control tasks over multiple processes, enabling fail-safes within the software. These fail-safes ensure that unexpected crashes or latency do not produce damaging behavior in the robot. The distribution also offers benefits for future software development by making the architecture modular and extensible. Utilizing a low-latency inter-process communication protocol (Ach), processes are able to communicate with high control frequencies. The key motivation of this software architecture is to provide a practical framework for safe and reliable humanoid robot software development. The authors test and verify this framework on a HUBO2 Plus humanoid robot.
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    Humanoid Robot Teleoperation for Tasks with Power Tools
    (Georgia Institute of Technology, 2013-04) O’Flaherty, Rowland ; Vieira, Peter ; Grey, M. X. ; Oh, Paul ; Bobick, Aaron F. ; Egerstedt, Magnus B. ; Stilman, Mike
    This paper presents the implementation of inverse kinematics to achieve teleoperation of a physical humanoid robot platform. The humanoid platform will be used to compete in the DARPA Robot Challenge, which requires autonomous execution of various search and rescue tasks, such as cutting through walls, which is a very practical application to robotics. Using a closed-form kinematic solution and a basic feedback controller, our objective of executing simple tasks is realized via teleoperation. Joint limits and singularities are accounted for using the different cases in the kinematic solution; and a decision method is implemented to determine how to position the end-effector when the goal is outside the feasible workspace.
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    Kinematics and Inverse Kinematics for the Humanoid Robot HUBO2+
    (Georgia Institute of Technology, 2013) O’Flaherty, Rowland ; Vieira, Peter ; Grey, Michael ; Oh, Paul ; Bobick, Aaron F. ; Egerstedt, Magnus B. ; Stilman, Mike
    This paper derives the forward and inverse kinematics of a humanoid robot. The specific humanoid that the derivation is for is a robot with 27 degrees of freedom but the procedure can be easily applied to other similar humanoid platforms. First, the forward and inverse kinematics are derived for the arms and legs. Then, the kinematics for the torso and the head are solved. Finally, the forward and inverse kinematic solutions for the whole body are derived using the kinematics of arms, legs, torso, and head.
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    Make Your Robot Talk Correctly: Deriving Models of Hybrid System
    (Georgia Institute of Technology, 2011-07) Dantam, Neil ; Stilman, Mike ; Egerstedt, Magnus B.
    Using both formal language and differential equations to model a robotic system, we introduce a calculus of transformation rules for the symbolic derivation of hybrid controllers. With a Context-Free Motion Grammar, we show how to test reachability between different regions of state-space and give several symbolic transformations to modify the set of event strings the system may generate. This approach lets one modify the language of the hybrid system, providing a way to change system behavior so that it satisfies linguistic constraints on correct operation.
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    Dynamic Chess: Strategic Planning for Robot Motion
    (Georgia Institute of Technology, 2011-05) Kunz, Tobias ; Kingston, Peter ; Stilman, Mike ; Egerstedt, Magnus B.
    We introduce and experimentally validate a novel algorithmic model for physical human-robot interaction with hybrid dynamics. Our computational solutions are complementary to passive and compliant hardware. We focus on the case where human motion can be predicted. In these cases, the robot can select optimal motions in response to human actions and maximize safety. By representing the domain as a Markov Game, we enable the robot to not only react to the human but also to construct an infinite horizon optimal policy of actions and responses. Experimentally, we apply our model to simulated robot sword defense. Our approach enables a simulated 7-DOF robot arm to block known attacks in any sequence. We generate optimized blocks and apply game theoretic tools to choose the best action for the defender in the presence of an intelligent adversary.