In this paper, a task space decomposition (TSD) method for redundancy resolution of functionally redundant robots is introduced. It stems from the previously proposed orthogonal decomposition approach aiming to exploi...
In this paper, a task space decomposition (TSD) method for redundancy resolution of functionally redundant robots is introduced. It stems from the previously proposed orthogonal decomposition approach aiming to exploit the redundant DOFs in task space in order to perform secondary tasks. The proposed TSD method assures proper self-motion according to the gradient of the objective function and thus convergence to the correct configuration, which is not achieved by the TWA. The reason for the latter is discussed and explained mathematically. Experimental results for the proposed TSD are presented for a planar test example and a 7 DOF Franka Emika Panda robot.
There are tasks that do not require a controlled motion in all spatial directions. These unused degrees-of-freedom (DOF) make the robot functionally redundant. Traditional methods for redundancy resolution developed f...
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The paper presents a novel control method for the arm exoskeletons that takes into account the muscular force manipulability of the human arm. In contrast to classical controllers that provide assistance without consi...
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Robots operating in contact with the environment should typically take into account the knowledge of both position/orientation trajectories as well as the accompanying force/torque profiles for successful execution. P...
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ISBN:
(纸本)9781509047192
Robots operating in contact with the environment should typically take into account the knowledge of both position/orientation trajectories as well as the accompanying force/torque profiles for successful execution. Pure position control is not appropriate because even small errors in the desired trajectory can cause significant forces at the contact points. In this paper we present a method that computes an appropriate control policy for a given condition of a contact task, with the peg-in-hole (PiH) assembly tasks as example use-cases. Our method is based on statistical generalization of successfully recorded executions at different values of the external condition. The major novelty of the method is that it provides not only generalized position and orientation trajectories, but a complete skill, consisting of desired position/orientation trajectories and the accompanying force/torque profiles. To improve the execution of the skill after generalization, we combine the proposed approach with an adaptation method to refine the newly generated movement. The versatility of the proposed approach was shown by applying it to firstly, two different types of robot arms: a humanoid 7-axis Kuka LWR-4 arm and a 6-axis industrial Universal robot UR5 arm and secondly, two different peg-in-hole problems: insertion of a square peg and insertion of a round peg.
We propose a novel method that arbitrates the control between the human and the robot actors in a teaching-by-demonstration setting to form synergy between the two and facilitate effective skill synthesis on the robot...
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ISBN:
(纸本)9781509037636
We propose a novel method that arbitrates the control between the human and the robot actors in a teaching-by-demonstration setting to form synergy between the two and facilitate effective skill synthesis on the robot. We employed the human-in-the-loop teaching paradigm to teleoperate and demonstrate a complex task execution to the robot in real-time. As the human guides the robot to perform the task, the robot obtains the skill online during the demonstration. To encode the robotic skill we employed Locally Weighted Regression that fits local models to specific state region of the task based on the human demonstration. If the robot is in the state region where no local models exist, the control over the robotic mechanism is given to the human to perform the teaching. When local models are gradually obtained in that region, the control is given to the robot so that the human can examine its performance already during the demonstration stage, and take actions accordingly. This enables a co-adaptation between the agents and contributes to a faster and more efficient teaching. As a proof-of-concept, we realised the proposed robot teaching system on a haptic robot with the task of generation of a desired vertical force on a horizontal plane with unknown stiffness properties.
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