Organizational Unit:
Humanoid Robotics Laboratory

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Publication Search Results

Now showing 1 - 7 of 7
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    Manipulation Planning with Soft Task Constraints
    (Georgia Institute of Technology, 2012-10) Kunz, Tobias ; Stilman, Mike
    We present a randomized configuration space planner that enforces soft workspace task constraints. A soft task constraint allows an interval of feasible values while favoring a given exact value. Previous work only allows for enforcing an exact value or an interval without a specific preference. Soft task constraints are a useful concept in everyday life. For example when carrying a container of liquid we want to keep it as close to the upright position as possible but want to be able to tilt it slightly in order to avoid obstacles. This paper introduces the necessary algorithms for handling such constraints, including projection methods and useful representations of everyday constraints. Our algorithms are evaluated on a series of simulated benchmark problems and shown to yield significant improvement in constraint satisfaction.
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    Time-Optimal Trajectory Generation for Path Following with Bounded Acceleration and Velocity
    (Georgia Institute of Technology, 2012-07) Kunz, Tobias ; Stilman, Mike
    This paper presents a novel method to generate the time-optimal trajectory that exactly follows a given differentiable joint-space path within given bounds on joint accelerations and velocities. We also present a path preprocessing method to make nondifferentiable paths differentiable by adding circular blends. We introduce improvements to existing work that make the algorithm more robust in the presence of numerical inaccuracies. Furthermore we validate our methods on hundreds of randomly generated test cases on simulated and real 7-DOF robot arms. Finally, we provide open source software that implements our algorithms.
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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.
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    Time-Optimal Path Following with Bounded Joint Accelerations and Velocities
    (Georgia Institute of Technology, 2011) Kunz, Tobias ; Stilman, Mike
    This paper presents a method to generate the time-optimal trajectroy that exactly follows a given differentiable joint-space path within given bounds on joint accelerations and velocities. We also present a path preprocessing method to make nondifferentiable paths differentiable by adding circular blends.
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    Turning Paths Into Trajectories Using Parabolic Blends
    (Georgia Institute of Technology, 2011) Kunz, Tobias ; Stilman, Mike
    We present an approach for converting a path of multiple continuous linear segments into a trajectory that satisfies velocity and acceleration constraints and closely follows the given path without coming to a complete stop at every waypoint. Our method applies parabolic blends around waypoints to improve speed. In contrast to established methods that smooth trajectories with parabolic blends, our method does not require the timing of waypoints or durations of blend phases. This makes our approach particularly useful for robots that must follow kinematic paths that are not explicitly parametrized by time. Our method chooses timing automatically to achieve high performance while satisfying the velocity and acceleration constraints of a given robot.
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    Real-Time Path Planning for a Robot Arm in Changing Environments
    (Georgia Institute of Technology, 2010-10) Kunz, Tobias ; Reiser, Ulrich ; Stilman, Mike ; Verl, Alexander
    We present a practical strategy for real-time path planning for articulated robot arms in changing environments by integrating PRM for Changing Environments with 3D sensor data. Our implementation on Care-O-Bot 3 identifies bottlenecks in the algorithm and introduces new methods that solve the overall task of detecting obstacles and planning a path around them in under 100 ms. A fast planner is necessary to enable the robot to react to quickly changing human environments. We have tested our implementation in real-world experiments where a human subject enters the manipulation area, is detected and safely avoided by the robot. This capability is critical for future applications in automation and service robotics where humans will work closely with robots to jointly perform tasks.
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    Robot Limbo: Optimized Planning and Control for Dynamically Stable Robots Under Vertical Obstacles
    (Georgia Institute of Technology, 2010-05) Teeyapan, Kasemsit ; Wang, Jiuguang ; Kunz, Tobias ; Stilman, Mike
    We present successful control strategies for dynamically stable robots that avoid low ceilings and other vertical obstacles in a manner similar to limbo dances. Given the parameters of the mission, including the goal and obstacle dimensions, our method uses a sequential composition of IO-linearized controllers and applies stochastic optimization to automatically compute the best controller gains and references, as well as the times for switching between the different controllers. We demonstrate this system through numerical simulations, validation in a physics-based simulation environment, as well as on a novel two-wheeled platform. The results show that the generated control strategies are successful in mission planning for this challenging problem domain and offer significant advantages over hand-tuned alternatives.