Assessment of Fitts' Law for Quantifying Combined Rotational and Translational Movements

Objective: To develop a model for human performance in combined translational and rotational movements based on Fitts' law. Background: Fitts' law has been successfully applied to translational movements in the past, providing generalization beyond a specific task as well as performance predictions. For movements involving both translations and rotations, no equivalent theory exists, making comparisons of input devices for these movements more ambiguous. Method: The study consisted of three experiments. In the first two, participants performed either pure translational or pure rotational movements of 1 degree of freedom. The third experiment involved the same movements combined. Results: On average, the performance times for combined movements were equal to the sum of the times for equivalent separate rotational and translational movements. A simple Fitts' law equivalent for combined movements with a similar slope as the separate components was proposed. In addition, a significant degree of coordination of the combined movements was found. This had a strong bias toward a parallel execution in 12 out of 13 participants. Conclusion: Combined movements with rotations and translations of 1 degree of freedom can be approximated using a simple Fitts' law equivalent. The rotational and translational components appear to be coordinated by the central nervous system to generate a parallel execution. Application: The results may help drive human interface designs and provide insights into the coordination of combined movements. Future extensions may be possible for the movements of higher degrees of freedom used in robot teleoperation and virtual reality applications.This work was supported by the Institute for Dexterous Space Robotics (Grant No. NNX06AD23G).Publicad

Agricultural Robotics: The Future of Robotic Agriculture

Agri-Food is the largest manufacturing sector in the UK. It supports a food chain that generates over £108bn p.a., with 3.9m employees in a truly international industry and exports £20bn of UK manufactured goods. However, the global food chain is under pressure from population growth, climate change, political pressures affecting migration, population drift from rural to urban regions and the demographics of an aging global population. These challenges are recognised in the UK Industrial Strategy white paper and backed by significant investment via a Wave 2 Industrial Challenge Fund Investment ("Transforming Food Production: from Farm to Fork"). Robotics and Autonomous Systems (RAS) and associated digital technologies are now seen as enablers of this critical food chain transformation. To meet these challenges, this white paper reviews the state of the art in the application of RAS in Agri-Food production and explores research and innovation needs to ensure these technologies reach their full potential and deliver the necessary impacts in the Agri-Food sector

Adaptive Aid on Targeted Robot Manipulator Movements in Tele-Assistance

The teleoperation of robot manipulators over the internet suffers from variable delays in the communications. Here we address a tele-assistance scenario, where a remote operator assists a disabled or elderly user on daily life tasks. Our behavioral approach uses local environment information from robot sensing to help enable faster execution for a given movement tolerance. This is achieved through a controller that automatically slows the operator down before having collisions, using a set of distributed proximity sensors. The controller is made to gradually increase the assistance in situations similar to those where ollisions have occurred in the past, thus adapting to the given operator, robot and task-set. Two controlled virtual experiments for tele-assistance with a 5 DOF manipulator were performed, with 300 ms and 600 ms mean variable round-trip delays. The results showed significant improvements in the median times of 12.6% and 16.5%, respectively. Improvements in the subjective workload were also seen with the controller. A first implementation on a physical robot manipulator is described

Do different robot appearances change emotion recognition in children with ASD?

Socially Assistive Robotics has emerged as a potential tool for rehabilitating cognitive and developmental disorders in children with autism. Social robots found in the literature are often able to teach critical social skills, such as emotion recognition and physical interaction. Even though there are promising results in clinical studies, there is a lack of guidelines on selecting the appropriate robot and how to design and implement the child-robot interaction. This work aims to evaluate the impacts of a social robot designed with three different appearances according to the results of a participatory design (PD) process with the community. A validation study in the emotion recognition task was carried out with 21 children with autism. Spectrum disorder results showed that robot-like appearances reached a higher percentage of children's attention and that participants performed better when recognizing simple emotions, such as happiness and sadness. This study offers empirical support for continuing research on using SAR to promote social interaction with children with ASD. Further long-term research will help to identify the differences between high and low-functioning children. [Abstract copyright: Copyright © 2023 Pinto-Bernal, Sierra M., Munera, Casas, Villa-Moreno, Frizera-Neto, Stoelen, Belpaeme and Cifuentes.