The interaction and collaboration between humans and multiple robots represent a novel field of research known as human multirobot systems. Adequately designed systems within this field allow teams composed of both humans and robots to work together effectively on tasks, such as monitoring, exploration, and search and rescue operations. This article presents a deep reinforcement learning-based affective workload allocation controller specifically for multihuman multirobot teams. The proposed controller can dynamically reallocate workloads based on the performance of the operators during collaborative missions with multirobot systems. The operators' performances are evaluated through the scores of a self-reported questionnaire (i.e., subjective measurement) and the results of a deep learning-based cognitive workload prediction algorithm that uses physiological and behavioral data (i.e., objective measurement). To evaluate the effectiveness of the proposed controller, we conduct an exploratory user experiment with various allocation strategies. The user experiment uses a multihuman multirobot CCTV monitoring task as an example and carry out comprehensive real-world experiments with 32 human subjects for both quantitative measurement and qualitative analysis. Our results demonstrate the performance and effectiveness of the proposed controller and highlight the importance of incorporating both subjective and objective measurements of the operators' cognitive workload as well as seeking consent for workload transitions, to enhance the performance of multihuman multirobot teams.

The beneficial aspects of fully-actuated multirotor UAVs, provided by nonparallel rotor configuration, are increasingly being recognized and utilized to great benefit in high-precision applications. Full six-degree-of-freedom force control, higher control bandwidth and improved disturbance rejection prove valuable. However, the cant angle will cause great multirotor dihedral effect and significantly affects blade flapping, which decreases the flight performance of nonparallel actuated UAVs. Therefore, this paper presents a novel aerodynamic model for fully-actuated hexrotor UAVs while considering the aerodynamic effects caused due to tilt angled propeller configurations. In the proposed aerodynamic model, the significance of multirotor dihedral effect, defined as an aerodynamic coefficient proportional to the relative linear velocity of the UAV, is modeled for nonparallel actuators. Additionally, the modeling for blade flapping effect for cant angled propellers is provided to accurately model the aerodynamics. Wind tunnel experiments were conducted to characterize the aerodynamic constants for multirotor dihedral effect, blade flapping effect and air friction. Experimental results are presented to validate the proposed aerodynamic model on a fully-actuated hexrotor UAV (Purdue’s Dexterous Hexrotor). Lastly, the multirotor dihedral effect and blade flapping effect at different cant angles and at different wind speeds are analyzed.

Inspection for structural properties (surface stiffness and coefficient of restitution) is crucial for understanding and performing aerial manipulations in unknown environments, with little to no prior knowledge on their state. Inspection-on-the-fly is the uncanny ability of humans to infer states during manipulation, reducing the necessity to perform inspection and manipulation separately. This paper presents an infrastructure for inspection-on-the-fly method for aerial manipulators using hybrid physical interaction control. With the proposed method, structural properties (surface stiffness and coefficient of restitution) can be estimated during physical interactions. A three-stage hybrid physical interaction control paradigm is presented to robustly approach, acquire and impart a desired force signature onto a surface. This is achieved by combining a hybrid force/motion controller with a model-based feed-forward impact control as intermediate phase. The proposed controller ensures a steady transition from unconstrained motion control to constrained force control, while reducing the lag associated with the force control phase. And an underlying Operational Space dynamic configuration manager permits complex, redundant vehicle/arm combinations. Experiments were carried out in a mock-up of a Dept. of Energy exhaust shaft, to show the effectiveness of the inspection-on-the-fly method to determine the structural properties of the target surface and the performance of the hybrid physical interaction controller in reducing the lag associated with force control phase.

In this paper we combine Gaussian process regression and impedance control, to illicit robust, anthropomorphic, adaptive control of a powered ankle prosthesis. We learn the non-linear manifolds which guide how locomotion variables temporally evolve, and regress that surface over a velocity range to create a manifold. The joint set of manifolds, as well as the temporal evolution of the gait-cycle duration is what we term a locomotion envelope. Current powered prostheses have problems adapting across speeds. It is likely that humans rely upon a control strategy which is adaptable, can become more robust and accurate with more data and provides a nonparametric approach which allows the strategy to grow with the number of observations. We demonstrate such a strategy in this study and successfully simulate locomotion well beyond our training data. The method we propose is based on common physical features observed in numerous human subjects walking at different speeds. Based on the derived locomotion envelopes we show that ankle power increases monotonically with speed among all subjects. We demonstrate our methods in simulation and human experiments, on a powered ankle foot prosthesis to demonstrate the effectiveness of the method.

The use of human-interactive robots in industry and daily life has become more prevalent throughout society as more people are using collaborative, and assistive robots to accomplish a task. To demonstrate the utility and importance of assistive robots to middle school students, a unique educational platform called Neu-pulator (neurally-controlled manipulator) was designed and fabricated to introduce their application in improving the quality of life. This robotic manipulator consists of low-cost components, which reflect the characteristics of a human arm, and is actuated by signals from the students neuromuscular system. During summer 2016, a 5-day program introduced students to the engineering design process as they designed, programmed, manufactured, and tested the Neu-pulator robot. A series of surveys and group interviews were performed to understand how each students attitude and opinion towards different STEM-related topics evolved throughout the course, both quantitatively and qualitatively. We observed that the students confidence, attitude, and excitement towards STEM improved over the course of the week, especially when they could see the robot they developed in action. With the use of this unique educational platform, a bridge can be made from learning fundamental STEM concepts to real-world application of human interactive and assistive robots.

This paper describes the details of a theme-based and hands-on STEM learning program utilizing an underwater robot called GUPPIE. Glider for Underwater Problem-solving and Promotion of Interest in Engineering (GUPPIE) is an example of a robot with oceanographic and environmental monitoring application. GUPPIE helps students to learn about fundamentals of physics (buoyancy, gravity, drag and lift force), electronics (circuitry and power distribution), programming, building (using tools and assembly), and testing in a systematic way. In this work we analyse the effects of the hands-on activities with meaningful context on students' 1) confidence level; 2) attitude towards robotics; 3) and level of interest towards STEM careers. The survey results suggest that using robots with sensible real world applications improves young students' attitudes and interests towards robotics. Based on these results, introducing young students to topics that are not part of official school curriculum, such as programming, increases the excitement and confidence in early ages, especially for girls, and can result in pursuing STEM related subjects in higher education. Results also revealed tha.t girls are more interested in building while boys are more attracted to programming.

This paper, for the first time, describes the estimation of the time-varying impedance of the human ankle in the sagittal (SP) and frontal (FP) planes during the stance phase of walking. The result of this work is aimed to provide design parameters for the development of 2-DOF powered ankle-foot prostheses capable of mimicking the time-varying impedance of the human ankle. Sixteen axes of rotations combining different amounts of SP and FP rotations were studied. For each axis, positive and negative rotations were considered separately. Four unimpaired male subjects walked on an instrumented vibrating platform that applied combined torque perturbations in the SP and FP simultaneously, while the ankle angles and torques were recorded. Based on the recorded data, the ankle impedance was estimated with a time resolution of 20 ms from 7% to 93% of the stance length (SL). The ankle stiffness and damping showed great variability through the SL and across axes of rotation. The maximum stiffness was 4.7±0.5 Nm/rad/kg at 0.21 s of the SL when the ankle rotated at an axis 22.5° from the SP combining Dorsiflexion (D) and Inversion (I). The minimum stiffness was 1.4±0.6 Nm/rad/kg at 0.05 s of the SL at an axis 45° from the SP combining D and Eversion (E). The maximum damping was 0.09±0.02 Nms/rad/kg at 0.21 s of the SL combining D and I at an axis 25° from the SP. The minimum was 0.02±0.01 Nms/rad/kg at 0.05 s of the SL combining P and I at an axis 45° from the SP.

This paper describes a finite state machine to control an ankle-foot prosthesis with two degrees of freedom (DOF) in the sagittal and frontal planes. Strain gauges were installed in the foot to provide ground reaction torques feedback for impedance and admittance controllers to be used at heel-strike and push-off of the gait, respectively. The quasi-static stiffness of the ankle with the active control was measured showing a near linear relationship between the torque feedback gain and the stiffness of the ankle. The performance of the finite state machine and controllers were also evaluated using a custom-made circular treadmill and the results were compared to the results of the prosthesis using position controller and inactive controllers. The results showed that the impedance/admittance controller was capable of tracking the desired input trajectory while decreasing the required torque at the ankle joint.