Individuals with tetraplegia have their independence and quality of life severely affected. Assistive robotic arms can enhance their autonomy, but effective control interfaces are essential for optimizing their usability and performance. This study aims to evaluate the performance and user experience of three control interfaces for an assistive robotic arm: Graphical User Interfaces (GUI), Embedded Interface, and Directional Gaze. Thirty-three able-bodied participants were recruited to control an assistive robotic arm through the three different interfaces in a between-subjects experiment. Performance was measured using the Yale-CMU-Berkeley (YCB) Block Pick and Place Protocol. Usability (SUS) and task, workload (NASATLX) were measured through subjective questionnaires. Additionally, we report saccades per minute and fixation duration. The results revealed statistically significant differences showing that Embedded and GUI interfaces, when compared to the Directional Gaze interface, can lead to lower workloads and higher performance in pick-up tasks.

Eye-gaze stands out as an intuitive interface for hands-free control of robotic devices due to its brief training time, fast calibration, low invasiveness, and reduced complexity and cost. However, current approaches are limited by available screen space, excessive wait times, frequent context switching, inconsistent gaze tracker accuracy, and the trade-off between feature-richness and usability. This article presents Diegetic Graphical User Interfaces, a novel, intuitive, and computationally inexpensive approach for gaze-controlled interfaces applied to a robotic arm for precision pick-and-place tasks. By using customizable symbols paired with fiducial markers, interactive buttons are defined and embedded into the robot, which users can trigger via gaze. Twenty-one participants completed the Yale-CMU-Berkeley (YCB) Block Pick and Place Protocol, reporting good usability and user experience, while achieving comparable workload to similar systems. The resulting system is fast to learn, does not restrain the user’s head, and mitigates context switching, while demonstrating intuitive control continuous Cartesian control of a robot arm in precision tasks.

An estimated 20% of patients admitted to hospital wards are affected by delirium. Early detection is recommended to treat underlying causes of delirium, however workforce strain in general wards often causes it to remain undetected. This work proposes a robotic implementation of the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) to aid early detection of delirium. Interactive features of the assessment are performed by Human-robot Interaction while a Transformer-based deep learning model predicts the Richmond Agitation Sedation Scale (RASS) level of the patient from image sequences; thermal imaging is used to maintain patient anonymity. A user study involving 18 participants role-playing each of alert, agitated, and sedated levels of the RASS is performed to test the HRI components and collect a dataset for deep learning. The HRI system achieved accuracies of 1.0 and 0.833 for the inattention and disorganised thinking features of the CAM-ICU, respectively, while the trained action recognition model achieved a mean accuracy of 0.852 on the classification of RASS levels during cross-validation. The three features represent a complete set of capabilities for automated delirium detection using the CAM-ICU, and the results demonstrate the feasibility of real-world deployment in hospital general wards.

The integration of variable stiffness actuators (VSA) in robotic systems endows them with intrinsic flexibility and therefore robustness to unknown disturbances. However, this characteristic presents a challenge: choosing the best intrinsic stiffness setting guaranteeing the required force ap-plication capability while keeping the system as adaptable to uncertainties as possible. This paper proposes a method to set the optimal stiffness for a multi-finger VSA hand to perform a desired manipulation task. The task is generically represented as a force (with unknown magnitude) applied along a reference direction. According to the force application's direction and the hand's kinematic state, the fingers assume a certain role to split the collective force application. We employ the endpoint stiffness ellipsoid to analyze the required finger stiffness to fulfill the task. We evaluate the optimized stiffness settings in a door opening application with an iterative adaption of the stiffness behavior to handle the unknown force requirement. The results show a successful collective behavior of the fingers, where the stiffness setting considers a task-oriented force-adaptability trade-off and effective use of independent VSA fingers.

In this article, we present an online scalable tele-impedance framework, which enables the individual and collaborative control of multiple different robotic platforms. The framework provides an intuitive low-cost interface with visual feedback and a SpaceMouse, through which the operator can define the desired task-level trajectories and impedance profiles. With a simple mouse click, the user can switch between the robots and the collaborative operation mode. The control, subsequently, manages the distribution of the required parameters into the involved robots. Thanks to the introduced virtual hand concept, where each robot is defined as a finger, new robots can be easily added or removed via their kinodynamic parameters. The proposed framework was evaluated with three different experiments: a simulated auscultation on a mock-up patient, a cooperative task where a robot drives the patient on a wheelchair and a different robot performs the auscultation, and a collaborative task where two robots relocate a container. The results demonstrate the capabilities of the framework in terms of adaptability to different robotic platforms, the number of robots involved, and the task requirements. Additionally, quantitative and subjective analysis of 12 subjects showed how the developed interface, even in the presence of inaccurate visual feedback, allowed a smooth and accurate execution of the tasks.

Full teleoperation of mobile robots during the execution of complex tasks not only demands high cognitive and physical effort but also generates less optimal trajectories compared to autonomous controllers. However, the use of the latter in cluttered and dynamically varying environments is still an open and challenging topic. This is due to several factors such as sensory measurement failures and rapid changes in task requirements. Shared-control approaches have been introduced to overcome these issues. However, these either present a strong decoupling that makes them still sensitive to unexpected events, or highly complex interfaces only accessible to expert users. In this work, we focus on the development of a novel and intuitive shared-control framework for target detection and control of mobile robots. The proposed framework merges the information coming from a teleoperation device with a stochastic evaluation of the desired goal to generate autonomous trajectories while keeping a human-in-control approach. This allows the operator to react in case of goal changes, sensor failures, or unexpected disturbances. The proposed approach is validated through several experiments both in simulation and in a real environment where the users try to reach a chosen goal in the presence of obstacles and unexpected disturbances. Operators receive both visual feedback of the environment and voice feedback of the goal estimation status while teleoperating a mobile robot through a control-pad. Results of the proposed method are compared to pure teleoperation proving a better time-efficiency and easiness-of-use of the presented approach.

Autonomous goal detection and navigation control of mobile robots in remote environments can help to unload human operators from simple, monotonous tasks allowing them to focus on more cognitively stimulating actions. This can result in better task performances, while creating user-interfaces that are understandable by non-experts. However, full autonomy in unpredictable and dynamically changing environments is still far from becoming a reality. Thus, teleoperated systems integrating the supervisory role and instantaneous decision-making capacity of humans are still required for fast and reliable robotic operations. This work presents a novel shared-autonomy framework for goal detection and navigation control of mobile manipulators. The controller exploits human-gaze information to estimate the desired goal. This is used together with control-pad data to predict user intention, and to activate the autonomous control for executing a target task. Using the control-pad device, a user can react to unexpected disturbances and halt the autonomous mode at any time. By releasing the control-pad device (e.g., after avoiding an instantaneous obstacle) the controller smoothly switches back to the autonomous mode and navigates the robot towards the target. Experiments for reaching a target goal in the presence of unknown obstacles are carried out to evaluate the performance of the proposed shared-autonomy framework over seven subjects. The results prove the accuracy, time-efficiency, and ease-of-use of the presented shared-autonomy control framework.

The protection of vulnerable road users (VRU) remains one of the most challenging problems for our society and several governmental and consumer organization has set targets to reduce the VRU fatality and injury rates. The automotive industry is, therefore, developing pedestrian and cyclist detection systems that combine pre-crash sensing, risk estimation and vehicle dynamics control (braking, steering) with the objective to meet the challenging VRU accident reduction targets set by various governments. The complexity of VRU detection makes the development process of these systems a complicated and time-consuming activity. Simulation software can help to overcome these difficulties by providing an efficient and safe environment for designing and evaluating VRU safety systems. This paper outlines the use of a software packages that provide the ability to cover all critical aspects of VRU safety design. It focuses on the sensing and control systems, as well as the evaluation of these systems for a range of different traffic scenarios. The presented system model uses fused information from radar model for collision estimation and vision sensor model for object classification, to deploy autonomous braking and thus mitigate the effect of collision with VRUs.