In our ageing society, the demand for care is increasing. Therefore, it is foreseen that robots can help elderly to keep living independently. However, due to the unstructured and dynamic environment, human assistance will often be required for manipulation tasks. While the robot can navigate autonomously, it does not know where to go for telemanipulation tasks.
Methods have been developed that position a robot for autonomous manipulation. But with telemanipulation, there are fewer acceptable base poses due to human and telemanipulation limitations. The objective of this study is to design and evaluate a positioning model, the Inverse Telemanipulation Capability Map (ITCM), which includes these constraints. A scoring metric is used based on manipulability and task visibility. Six participants did a pick-and-place task with the robot in a base pose used by an expert (Expert), in the lowest (ITCM-low) and the highest scoring base pose (ITCM-high).
The results show an increase in completion time of 71% from the ITCM-high to ITCM-low condition and the effort metrics are around one and a half times higher. Between the ITCM-high and Expert conditions no differences were found in the metrics. But participants also reported that the task was less or equally difficult in the ITCM-high condition.
It is concluded that the ITCM model is a good predictor for the telemanipulation advantageousness of a base pose and even the lowest scoring pose is acceptable. It can thus successfully be used to select navigational goals for semi-autonomous mobile manipulators.