The Experts below are selected from a list of 60 Experts worldwide ranked by ideXlab platform
Andy H. Park - One of the best experts on this subject based on the ideXlab platform.
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Closed-form inverse Kinematic Joint solution for humanoid robots
2010 IEEE RSJ International Conference on Intelligent Robots and Systems, 2010Co-Authors: Andy H. ParkAbstract:This paper focuses on developing a consistent methodology for deriving a closed-form inverse Kinematic Joint solution of a general humanoid robot. Most humanoid-robot researchers resort to iterative methods for inverse Kinematics using the Jacobian matrix to avoid the difficulty of finding a closed-form Joint solution. Since a closed-form Joint solution, if available, has many advantages over iterative methods, we have developed a novel reverse decoupling mechanism method by viewing the Kinematic chain of a limb of a humanoid robot in reverse order and then decoupling it into the positioning and orientation mechanisms, and finally utilizing the inverse transform technique in deriving a consistent Joint solution for the humanoid robot. The proposed method presents a simple and efficient procedure for finding the Joint solution for most of the existing humanoid robots. Extensive computer simulations of the proposed approach on a Hubo KHR-4 humanoid robot show that it can be applied easily to most humanoid robots with slight modifications.
Kröger Torsten - One of the best experts on this subject based on the ideXlab platform.
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Learning Robot Trajectories subject to Kinematic Joint Constraints
2021Co-Authors: Kiemel, Jonas C., Kröger TorstenAbstract:We present an approach to learn fast and dynamic robot motions without exceeding limits on the position $\theta$, velocity $\dot{\theta}$, acceleration $\ddot{\theta}$ and jerk $\dddot{\theta}$ of each robot Joint. Movements are generated by mapping the predictions of a neural network to safely executable Joint accelerations. The neural network is invoked periodically and trained via reinforcement learning. Our main contribution is an analytical procedure for calculating safe Joint accelerations, which considers the prediction frequency $f_N$ of the neural network. As a result, the frequency $f_N$ can be freely chosen and treated as a hyperparameter. We show that our approach is preferable to penalizing constraint violations as it provides explicit guarantees and does not distort the desired optimization target. In addition, the influence of the selected prediction frequency on the learning performance and on the computing effort is highlighted by various experiments.Comment: IEEE International Conference on Robotics and Automation (ICRA 2021); 7 pages, 7 figure
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Learning Collision-free and Torque-limited Robot Trajectories based on Alternative Safe Behaviors
2021Co-Authors: Kiemel, Jonas C., Kröger TorstenAbstract:This paper presents an approach to learn online generation of collision-free and torque-limited trajectories for industrial robots. A neural network, which is trained via reinforcement learning, is periodically invoked to predict future motions. For each robot Joint, the network outputs the Kinematic state that is desired at the end of the current time interval. Compliance with Kinematic Joint limits is ensured by the design of the action space. Given the current Kinematic state and the network prediction, a trajectory for the current time interval can be computed. The main idea of our paper is to execute the predicted motion only if a collision-free and torque-limited way to continue the trajectory is known. In practice, the predicted motion is expanded by a braking trajectory and simulated using a physics engine. If the simulated trajectory complies with all safety constraints, the predicted motion is carried out. Otherwise, the braking trajectory calculated in the previous decision step serves as an alternative safe behavior. For evaluation, up to three simulated robots are trained to reach as many randomly placed target points as possible. We show that our method reliably prevents collisions with static obstacles and collisions between the robots, while generating motions that respect both torque limits and Kinematic Joint limits. Experiments with a real robot demonstrate that safe trajectories can be generated in real-time.Comment: 8 pages; 7 figure
Ming Cong - One of the best experts on this subject based on the ideXlab platform.
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Kinematic model and analysis of an actuation redundant parallel robot with higher Kinematic pairs for jaw movement
IEEE Transactions on Industrial Electronics, 2015Co-Authors: Weiliang Xu, Ming CongAbstract:A jaw movement robot that can simulate jaw movement and reaction forces in temporomandibular Joints (TMJs) of a man will find many applications in dentistry, food science, and biomechanics. The TMJ is the most sophisticated Joint in the human body, and its compound movements are not given sufficient consideration when a jaw robot is designed. Based on the biological finding about the mastication system and its motion characteristics, this paper proposes an actuation redundant parallel mechanism for the jaw movement robot and designs the actuation systems and models the TMJ in a higher pair Kinematic Joint. The prototype of the proposed jaw movement robot is presented, consisting of six prismatic–universal–spherical linkages for muscle groups of mastication and two point contacts for left and right TMJs. This robot has four degrees of freedom but is driven by six actuators. Each prismatic–universal–spherical linkage is made up of a rotary motor, a prismatic Joint, a universal Joint, and a spherical Joint. The closed-form solution to the Kinematics is found. This novel robot is evaluated by simulations of Kinematics, workspace, and a chewing movement experiment.
Kiemel, Jonas C. - One of the best experts on this subject based on the ideXlab platform.
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Learning Robot Trajectories subject to Kinematic Joint Constraints
2021Co-Authors: Kiemel, Jonas C., Kröger TorstenAbstract:We present an approach to learn fast and dynamic robot motions without exceeding limits on the position $\theta$, velocity $\dot{\theta}$, acceleration $\ddot{\theta}$ and jerk $\dddot{\theta}$ of each robot Joint. Movements are generated by mapping the predictions of a neural network to safely executable Joint accelerations. The neural network is invoked periodically and trained via reinforcement learning. Our main contribution is an analytical procedure for calculating safe Joint accelerations, which considers the prediction frequency $f_N$ of the neural network. As a result, the frequency $f_N$ can be freely chosen and treated as a hyperparameter. We show that our approach is preferable to penalizing constraint violations as it provides explicit guarantees and does not distort the desired optimization target. In addition, the influence of the selected prediction frequency on the learning performance and on the computing effort is highlighted by various experiments.Comment: IEEE International Conference on Robotics and Automation (ICRA 2021); 7 pages, 7 figure
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Learning Collision-free and Torque-limited Robot Trajectories based on Alternative Safe Behaviors
2021Co-Authors: Kiemel, Jonas C., Kröger TorstenAbstract:This paper presents an approach to learn online generation of collision-free and torque-limited trajectories for industrial robots. A neural network, which is trained via reinforcement learning, is periodically invoked to predict future motions. For each robot Joint, the network outputs the Kinematic state that is desired at the end of the current time interval. Compliance with Kinematic Joint limits is ensured by the design of the action space. Given the current Kinematic state and the network prediction, a trajectory for the current time interval can be computed. The main idea of our paper is to execute the predicted motion only if a collision-free and torque-limited way to continue the trajectory is known. In practice, the predicted motion is expanded by a braking trajectory and simulated using a physics engine. If the simulated trajectory complies with all safety constraints, the predicted motion is carried out. Otherwise, the braking trajectory calculated in the previous decision step serves as an alternative safe behavior. For evaluation, up to three simulated robots are trained to reach as many randomly placed target points as possible. We show that our method reliably prevents collisions with static obstacles and collisions between the robots, while generating motions that respect both torque limits and Kinematic Joint limits. Experiments with a real robot demonstrate that safe trajectories can be generated in real-time.Comment: 8 pages; 7 figure
Hyungju Andy Park - One of the best experts on this subject based on the ideXlab platform.
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CLOSED-FORM INVERSE Kinematic POSITION SOLUTION FOR HUMANOID ROBOTS
International Journal of Humanoid Robotics, 2012Co-Authors: Hyungju Andy ParkAbstract:This paper focuses on developing a consistent methodology for deriving a closed-form inverse Kinematic Joint solution of a humanoid robot with decision equations to select a proper solution from multiple solutions. Most researchers resort to iterative methods for inverse Kinematics using the Jacobian matrix to avoid the difficulty of finding a closed-form Joint solution. Since a closed-form Joint solution, if available, has many advantages over iterative methods, we have developed a novel reverse-decoupling method by viewing the Kinematic chain of a limb of a humanoid robot in reverse order and then decoupling it into the positioning and orientation mechanisms, and finally utilizing the inverse-transform technique to derive a consistent Joint solution for the humanoid robot. The proposed method presents a simple and efficient procedure for finding the Joint solution for most of the existing humanoid robots. Extensive computer simulations of the proposed approach on a Hubo KHR-4 humanoid robot show that it can be applied easily to most humanoid robots such as HOAP-2, HRP-2 and ASIMO humanoid robots with slight modifications.
