scholarly journals Statistical Learning by Imitation of Competing Constraints in Joint Space and Task Space

2009 ◽  
Vol 23 (15) ◽  
pp. 2059-2076 ◽  
Author(s):  
Sylvain Calinon ◽  
Aude Billard
Author(s):  
Sina Baghi ◽  
Fariborz Razban ◽  
Kambiz G. Osgouie

Gimbal transmissions are non-linear direct transmissions and can be used in robotic arms replacing the traditional revolute joints. They offer potential advantages for critical cases such as joint space and task space singularities or where a different mechanical advantage is needed compared to what traditional revolute joints provide. This can be obtained by properly adjusting the different parameters of Gimbal joints used in different joints of the manipulator (such as their offset angle and/or chamfer angle). In this paper the concept of Gimbal mechanism as a joint is investigated. Then, as an example, Gimbal joints are used to replace the basic revolute joints of a 3-UPU parallel manipulator and actuator velocities are obtained for a task space trajectory. The outcomes for a manipulator with traditional revolute joints and with Gimbal equipped joints are compared. Then the workspace and dexterity analyses are done on both manipulators.


2021 ◽  
Vol 54 (1-2) ◽  
pp. 102-115
Author(s):  
Wenhui Si ◽  
Lingyan Zhao ◽  
Jianping Wei ◽  
Zhiguang Guan

Extensive research efforts have been made to address the motion control of rigid-link electrically-driven (RLED) robots in literature. However, most existing results were designed in joint space and need to be converted to task space as more and more control tasks are defined in their operational space. In this work, the direct task-space regulation of RLED robots with uncertain kinematics is studied by using neural networks (NN) technique. Radial basis function (RBF) neural networks are used to estimate complicated and calibration heavy robot kinematics and dynamics. The NN weights are updated on-line through two adaptation laws without the necessity of off-line training. Compared with most existing NN-based robot control results, the novelty of the proposed method lies in that asymptotic stability of the overall system can be achieved instead of just uniformly ultimately bounded (UUB) stability. Moreover, the proposed control method can tolerate not only the actuator dynamics uncertainty but also the uncertainty in robot kinematics by adopting an adaptive Jacobian matrix. The asymptotic stability of the overall system is proven rigorously through Lyapunov analysis. Numerical studies have been carried out to verify efficiency of the proposed method.


Author(s):  
Juliane Scheil ◽  
Thomas Kleinsorge

AbstractA common marker for inhibition processes in task switching are n − 2 repetition costs. The present study aimed at elucidating effects of no-go trials on n − 2 repetition costs. In contrast to the previous studies, no-go trials were associated with only one of the three tasks in the present two experiments. High n − 2 repetition costs occurred if the no-go task had to be executed in trial n − 2, irrespective of whether a response had to be withheld or not. In contrast, no n − 2 repetition costs were visible if the other two tasks were relevant in n − 2. Whereas this n − 2 effect was unaffected by whether participants could reliably exclude a no-go trial or not, effects of no-gos in trial n were determined by this knowledge. The results differ from effects of no-go trials that are not bound to a specific task. It is assumed that the present no-go variation exerted its effect not on the response level, but on the level of task sets, resulting in enhanced salience of the no-go task that leads to higher activation and, as a consequence, to stronger inhibition. The dissociation of the effects on no-gos in trials n − 2 and n as a function of foreknowledge suggests that the balance between activation and inhibition is shifted not only for single trials and tasks, but for the whole task space.


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