Automation in a domestic environment is not flawless and human interference will be necessary, for implementing robots in this environment.
When remotely controlling semi-autonomous robots, proposed concepts can be divided into two main concepts: To trade control back and forth between the human and the operator and to share control continuously. However, a deep analysis lacks about when either of these methods is useful.
This study focuses on comparing task completion time and task behavior Trading Control (TC) and Haptic Shared Control (HSC) using a mix of accurate models of the tasks and models with a small translational offset. These are examined in the current mix as well as separated into models that were accurate or did contain an offset.
In remote execution of a constraint motion task, we hypothesize Haptic Shared control to have a lower task completion time compared to Trading Control when inaccuracies are present. When the model is fully accurate, on the other hand, we hypothesize Trading Control to have a lower task completion time compared to haptic shared control.
Participants used a 6DOF haptic manipulator to control a virtual robot arm, in order to open a simulated drawer.
An autonomous controller was developed based on a model that was perfectly accurate (50% of the time), or that had an effective endpoint error (50% of the time). Control over the automation was either traded by pressing a space bar or continuously shared through haptic shared control.
In trials with a translational offset, Haptic shared control had a lower task completion time compared to trading control. In the trials with a perfectly accurate model of the task, Trading Control had the benefit of lowering peak collision force and increasing smoothness of the master input. Therefore more research is needed to better understand when trading control is beneficial compared to haptic shared control.

In our aging society, the demand for care is increasing. Therefore, it is foreseen that robots will assist elderly. However, human assistance will often be required by the robot, which thus should be at a position advantageous for telemanipulation. For autonomous manipulation, methods have been developed that position a robot. But for telemanipulation, human capabilities and master limitations change the positions suitability. In this research, a positioning model -- Inverse Telemanipulation Capability Map (ITCM) -- is designed which take these into account. The goal of this research is to find if the ITCM can be used to replace manual robot placement and if telemanipulation is influenced by the base pose. Six participants have done a pick-and-place task with the robot positioned in the lowest (ITCM-low) and the highest scoring base pose (ITCM-high) and a base pose selected by an expert (Expert). The results show no difference between the ITCM-high and Expert conditions. Participants also reported that the task was less or equally difficult in the ITCM-high condition. From the base pose of the ITCM-high to the ITCM-low condition, the task execution time increases with 71 % and effort metrics are around one and a half times as high. Moreover, participants reported that task was more difficult from the ITCM-low base pose. It is concluded that the base pose influences performance and effort and that the base pose can be succesfully selected with the presented model.

In telemanipulation commonly master and slave have dissimilar workspaces. Workspace extension methods can overcome this mismatch between master and slave. Literature proposes several workspace extension methods for translations such as scaling and indexing. However, for rotations it is unclear how workspace extension methods should be designed. The present study proposes a methodology to design rotational workspace extension methods with a variable gain. Which is designed based on the speed-accuracy trade-off and several task characteristics, like the distribution of rotational amplitudes during telemanipulation tasks. The effectiveness of the variable gain method is evaluated in terms of task performance and control effort in a within-subject-haptic-telemanipulation-single-degree-rotational-pointing-experiment, based on Fitts’ tapping task. The parameters are chosen in accordance with a care robot case study where the rotational workspace of the master device is 45°, but where most tasks require 90° slave rotation, and some even 180°. It is hypothesized that variable gain workspace extension allows improved performance in regions it is customized for (up to 90% of the slave rotations) with respect to a conventional constant scaling, while the operator is able to perform in all regions with similar order of magnitude metrics. To test this hypothesis a variable scaling method, a constant scaling method, and a baseline method (without scaling) are designed. The experimental results show improved performance on fine positioning time and reversal rate for the variable scaling method at the focus region. Furthermore, human operators accept variable scaling and are able to manipulate linear changes of the gain equally smooth as constant gains, while high nonlinear changes of the gain are more difficult to manipulate smoothly. To conclude, this study demonstrates a methodology for designing variable gain workspace extension methods for specific task characteristics which allows improved execution performance compared to the conventional constant scaling method. For applying this methodology in real-life applications, the results need to be scaled for more realistic situations, such as higher degrees of freedom and in-contact tasks.