Shared control systems aim to combine human and robot abilities to improve task performance. However, achieving optimal performance requires that the robot's level of assistance adjusts the operator's cognitive workload in response to the task difficulty. Understanding and dynamically adjusting this balance is crucial to maximizing efficiency and user satisfaction. In this paper, we propose a novel benchmarking method for shared control systems based on Fitts' Law to formally parameterize the difficulty level of a target-reaching task. With this we systematically quantify and model the effect of task difficulty (i.e. size and distance of target) and robot autonomy on task performance and operators' cognitive load and trust levels. Our empirical results (N=24) not only show that both task difficulty and robot autonomy influence task performance, but also that the performance can be modelled using these parameters, which may allow for the generalization of this relationship across more diverse setups. We also found that the users' perceived cognitive load and trust were influenced by these factors. Given the challenges in directly measuring cognitive load in real-time, our adapted Fitts' model presents a potential alternative approach to estimate cognitive load through determining the difficulty level of the task, with the assumption that greater task difficulty results in higher cognitive load levels. We hope that these insights and our proposed framework inspire future works to further investigate the generalizability of the method, ultimately enabling the benchmarking and systematic assessment of shared control quality and user impact, which will aid in the development of more effective and adaptable systems.

Physically connected humans have been shown to exploit the exchange of haptic forces and tactile information to improve their performance in joint action tasks. As human interactions are increasingly mediated through robots and networks it is important to understand the impact that network features such as lag and noise may have on human behaviour. In this paper, we investigated interaction with a human-like robot controller that provides similar haptic communication behaviour as human-human interaction and examined the influence and compensation mechanisms for delay and noise on haptic communication. The results of our experiments show that participants can perceive a difference between noise and delay, and make use of compensation mechanisms to preserve performance in both cases. However, while noise is compensated for by increasing co-contraction, delay compensation could not be explained by this strategy. Instead, computational modelling suggested that a distinct mechanism is used to compensate for the delay and yield an efficient haptic communication.

Extending the human field of view could enhance our ability to perceive our surroundings thereby improving user safety and enabling us to perform complex manipulation tasks in industrial assembly processes. We investigated the augmentation of environment perception through a study that systematically measured the performance and perception arising from the use of a third eye placed on the back of the user’s head in virtual reality. 28 participants were asked to conduct two goal-oriented tasks: One requiring the identification of targets that discretely appeared in a set of predefined locations; the other the catching of a continuously moving target. The results show that participants were able to incorporate the additional visual information in real time resulting in changes of their motion behaviour. These changes led to localised improvements in task performance for the discrete target task and more efficient motion for both tasks. Participants also showed a strong preference for performing the tasks with the third eye and perceived no accompanying increase in cognitive load.

Supernumerary robotic limbs can act as intelligent prostheses or augment the motion of healthy people to achieve actions which are not possible with only two natural hands. However, as trimanual control is not typical in everyday activities, it is still unknown how different training could influence its acquisition. We conducted an experimental study to evaluate the impact of different forms of trimanual action on training. Two groups of twelve subjects were each trained in virtual reality for five weeks using either a three independent goals task or one dependent goal task. The success of their training was then evaluated by comparing their task performance and motion characteristics between sessions. The results show that subjects dramatically improved their trimanual task performance as a result of training. However, while they showed improved motion efficiency and reduced workload for tasks with multiple independent goals with practice, no such improvement was observed when they trained with the one coordinated goal task.

Many teleoperation tasks require three or more tools working together, which need the cooperation of multiple operators. The effectiveness of such schemes may be limited by communication issues between individuals. Trimanipulation by a single operator using an artificial third arm controlled together with their natural arms may address this issue. Foot-controlled interfaces have previously shown the capability to be used for the continuous control of robot arms. However, the use of such interfaces for controlling a supernumerary robotic limb in coordination with the natural limbs is not well understood. In this paper, a teleoperation task imitating physically-coupled hands in a virtual reality scene was conducted with 14 subjects to evaluate human performance during trimanipulation. The participants were required to move three limbs together in a coordinated way mimicking three arms holding a shared physical object. It was found that after a short practice session, three-hand trimanipulation with a single subject’s hands and foot was still slower than dyad operation. However, they displayed similar performance in their success rate and higher motion efficiency than two people cooperating.

This paper presents a new generalized ray-based approach to the generation and representation of the wrench-closure workspace (WCW) for cable-driven robots (CDRs). Existing WCW studies have yet to address two significant problems, first is the lack of a generalized approach to generate the WCW with continuity information for all degrees-of-freedom (DoFs) and arbitrary CDRs, and second is a workspace representation for higher DoF robots. The proposed work addresses these issues using a generalized ray-based lattice WCW generation approach that can be applied in all DoFs. Furthermore, a new graph workspace representation is introduced with a range of advantages in the way CDR workspace can be visualized and studied. Such a representation is powerful since it: can be used for any other type of workspace beyond the WCW; can be visualized in two-dimensions regardless of the number of DoFs of the system; allows metric information to be included; and opens up the use of well-established graph theory techniques to study the workspace. Through three different CDR examples, a 3-DoF planar cable-driven parallel robot (CDPR), a 4-DoF multilink robot, and a 6-DoF CDPR, the characteristics and advantages of the proposed method are highlighted.

The study of cable-driven parallel robots (CDPRs) has attracted much attention in recent years. However, to the best of the authors' knowledge, no single software platform exists for researchers to perform different types of analyses for CDPRs of arbitrary structure. In this paper, the Cable-robot Analysis and Simulation Platform for Research (CASPR) of CDPRs is introduced. Using this platform, arbitrary types and structures of CDPRs, such as single and multi-link CDPRs, can be studied for a wide range of analyses, including kinematics, dynamics, control and workspace analysis. CASPR achieves this using a general CDPR model representation and an abstracted software architecture. Moveover, CDPRs can be defined using Extensible Markup Language (XML) with out-of-the-box availability of an extensive range of robots and analysis tools. The open-source platform aims to provide both a communal environment for the researchers to use and add models and algorithms to. The example case studies demonstrate the potential to perform analysis on CDPRs, directly compare algorithms and conveniently add new models and analyses.

Fluid motion planners are a type of artificial potential field (APF) motion planners that use the differential equations of fluid flow to determine the desired trajectory. The fluid flow approach in motion planning can efficiently produce natural-looking trajectories. However, the differential equations used in previous studies are restricted to motion planning in 2-D environments. In this paper, the fluid flow approach is extended to a motion planning framework for 3-D mobile robots that avoids spheroidal obstacles. Compared with existing APF approaches, kinematic constraints in both speed and curvature are also considered. Possessing the efficiency of 2-D fluid motion planners, the proposed approach is able to plan natural-looking reference trajectories for nonholonomic 3-D mobile robots. The approach is demonstrated through various 3-D example scenarios. The work can be considered as a fundamental framework for 3-D fluid motion planning, where additional kinematic constraints and more complex scenarios can be incorporated.