the proceedings contain 266 papers. the topics discussed include: multi-fidelity modeling for analysis of serial production lines;on the separation of a polyhedron from its single-part mold;a GPU-based parallel slicer...
ISBN:
(纸本)9781509067800
the proceedings contain 266 papers. the topics discussed include: multi-fidelity modeling for analysis of serial production lines;on the separation of a polyhedron from its single-part mold;a GPU-based parallel slicer for 3D printing;using dVRK teleoperation to facilitate deep learning of automation tasks for an industrial robot;anomaly detection and productivity analysis for cyber-physical systems in manufacturing;adaptive slicing for the FDM process revisited;Robolink feeder: reconfigurable bin-picking and feeding with a lightweight cable-driven manipulator;a circuit-breaker use-case operated by a humanoid in aircraft manufacturing;a framework of cyber-physical system for smart cotton production;automated vision based detection of blistering on metal surface: for robot;regulation by competing: a hidden layer of gene regulatory networks;big data analytic based personalized air quality health advisory model;data-based predictive optimization for byproduct gas system in steel industry;PPIM: a protein-protein interaction database for maize;conflict between energy, stability and robustness in production schedules;position-loop based cross-coupled and synchronization control of a parallel kinematics machine;polynomial trajectory approximation along specified paths for robot manipulators;on model predictive path following and trajectory tracking for industrial robots;and nonlinear programming for multi-vehicle motion planning with homotopy initialization strategies.
Underactuated balance robots represent a broad class of mechanical systems, ranging from Furuta pendulum, autonomous motorcycles, and robotic bipedal walkers, etc. the control tasks of these systems include trajectory...
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Underactuated balance robots represent a broad class of mechanical systems, ranging from Furuta pendulum, autonomous motorcycles, and robotic bipedal walkers, etc. the control tasks of these systems include trajectory tracking and balancing requirements. We present a data-driven modeling and control framework of the underactuated balance robots. A machine-learning method is used to capture the dynamics and the balance equilibrium manifold that represents balancing task target. We combine the learning-based models withthe structural properties of the external/internal convertible form of these underactuated systems. Applications of the proposed learning-based models and control design are applied to the Furuta pendulum by simulation and experiments.
Motor Primitives (MPs) are a promising approach for the data-driven acquisition as well as for the modular and re-usable generation of movements. However, a modular control architecture with MPs is only effective if t...
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ISBN:
(纸本)9781479926190
Motor Primitives (MPs) are a promising approach for the data-driven acquisition as well as for the modular and re-usable generation of movements. However, a modular control architecture with MPs is only effective if the MPs support co-activation as well as continuously blending the activation from one MP to the next. In addition, we need efficient mechanisms to adapt a MP to the current situation. Common approaches to movement primitives lack such capabilities or their implementation is based on heuristics. We present a probabilistic movement primitive approach that overcomes the limitations of existing approaches. We encode a primitive as a probability distribution over trajectories. the representation as distribution has several beneficial properties. It allows encoding a time-varying variance profile. Most importantly, it allows performing new operations - a product of distributions for the co-activation of MPs conditioning for generalizing the MP to different desired targets. We derive a feedback controller that reproduces a given trajectory distribution in closed form. We compare our approach to the existing state-of-the art and present real robot results for learning from demonstration.
We utilize here regression tools to plan dynamic locomotion in the Phase Space of the robot's center of mass behavior and state feedback controllers to accomplish the desired plans. In real robotic systems, simpli...
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ISBN:
(纸本)9781479926190
We utilize here regression tools to plan dynamic locomotion in the Phase Space of the robot's center of mass behavior and state feedback controllers to accomplish the desired plans. In real robotic systems, simplified locomotion models and disturbances in the control processes result in deviations from the actual closed loop dynamics with respect to the desired locomotion trajectories. To tackle these challenges, we propose here the use of two control strategies: (1) support vector regression to approximate complex nonlinear center of mass dynamics and plan the feet contact transitions, and (2) sliding mode control to track feet trajectories given the contact timing and location plans. First, support vector regression is utilized to learn a data set obtained through numerical simulation, providing an analytical approximation of the center of mass behavior. To approximate Phase Plane curves, which are characterized by vertical tangents and loop or cyclic behaviors, we use implicit functions for regression as opposed to explicit methods. Based on the proposed regression approximations of the dynamics, we develop contact transition plans and apply robust controllers to converge to the desired feet trajectories. In particular, state feedback controllers might be more convenient than time based controllers in terms of robustness to disturbances. Overall, our methods are capable of learning complex center of mass trajectories and might benefit from the use of robust control techniques. Various case studies are analyzed to validate the effectiveness of the methods including single and multi step planning in a numerical simulation, and swing leg trajectory control on our Hume bipedal robot.
Motor Primitives (MPs) are a promising approach for the data-driven acquisition as well as for the modular and re-usable generation of movements. However, a modular control architecture with MPs is only effective if t...
详细信息
Motor Primitives (MPs) are a promising approach for the data-driven acquisition as well as for the modular and re-usable generation of movements. However, a modular control architecture with MPs is only effective if the MPs support co-activation as well as continuously blending the activation from one MP to the next. In addition, we need efficient mechanisms to adapt a MP to the current situation. Common approaches to movement primitives lack such capabilities or their implementation is based on heuristics. We present a probabilistic movement primitive approach that overcomes the limitations of existing approaches. We encode a primitive as a probability distribution over trajectories. the representation as distribution has several beneficial properties. It allows encoding a time-varying variance profile. Most importantly, it allows performing new operations - a product of distributions for the co-activation of MPs conditioning for generalizing the MP to different desired targets. We derive a feedback controller that reproduces a given trajectory distribution in closed form. We compare our approach to the existing state-of-the art and present real robot results for learning from demonstration.
We utilize here regression tools to plan dynamic locomotion in the Phase Space of the robot's center of mass behavior and state feedback controllers to accomplish the desired plans. In real robotic systems, simpli...
详细信息
We utilize here regression tools to plan dynamic locomotion in the Phase Space of the robot's center of mass behavior and state feedback controllers to accomplish the desired plans. In real robotic systems, simplified locomotion models and disturbances in the control processes result in deviations from the actual closed loop dynamics with respect to the desired locomotion trajectories. To tackle these challenges, we propose here the use of two control strategies: (1) support vector regression to approximate complex nonlinear center of mass dynamics and plan the feet contact transitions, and (2) sliding mode control to track feet trajectories given the contact timing and location plans. First, support vector regression is utilized to learn a data set obtained through numerical simulation, providing an analytical approximation of the center of mass behavior. To approximate Phase Plane curves, which are characterized by vertical tangents and loop or cyclic behaviors, we use implicit functions for regression as opposed to explicit methods. Based on the proposed regression approximations of the dynamics, we develop contact transition plans and apply robust controllers to converge to the desired feet trajectories. In particular, state feedback controllers might be more convenient than time based controllers in terms of robustness to disturbances. Overall, our methods are capable of learning complex center of mass trajectories and might benefit from the use of robust control techniques. Various case studies are analyzed to validate the effectiveness of the methods including single and multi step planning in a numerical simulation, and swing leg trajectory control on our Hume bipedal robot.
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