The application of autonomous robotics to close-contact healthcare tasks, such as the nasopharyngeal (NP) swab test, has clear potential for reducing infection risks to staff and improving efficiency. We propose a control system that performs the NP swab test with a collaborative manipulator arm, guided by an instrumented end-effector that measures force and visual information. We assume a scenario where the patient is unrestrained, with hardware general enough for other types of close contact tasks. The system employs visual servo control to align the swab with the nostrils. A compliant joint velocity controller inserts the swab into the nasal cavity, following a planned trajectory adjusted with force-feedback. Fuzzy logic systems are designed to detect when the swab reaches the nasopharynx and enforce safety criteria. We validate the system using a second robotic arm that holds a nasal cavity phantom and simulates natural head motions. Extensive experiments identify controller configurations capable of effectively performing the NP swab test even with significant head motion.

The implementation of stable locomotion on humanoid robots is a difficult task. This is complicated by the fact that there is no uniform method for analyzing a robot and its control architecture and for calculating indicators to quantify performance of flat ground walking. Moreover, there is no widely accepted indicator do distinct between a stable and unstable state of the robot. We propose the Increased Step Size Stability Assessment (I3SA) as a testing protocol and standardized procedure for data collection and evaluation of stability for locomotion on flat terrain. We apply this test to the humanoid robot REEM-C. The biped must cover a set distance of four meters with predefined step sizes. The initial step size is defined as 20% of the total leg length of the robot. After three successful trials, the step size is continuously increased until REEM-C’s last successful trial at 40% of its total leg length, leading to REEM-C’s I3SA rating of 40. The recorded data are evaluated using metrics known from the literature, such as the capture point, foot placement estimator, and the angular momentum acting at the center of mass. We illustrate the experimental setup, data collection and processing, the calculation of performance indicators for several step sizes and the trial which resulted in a fall of the robot. The trend towards decreasing stability with increased step size and the available key assumptions are reported.

Compliance is an important feature of human locomotion, where many studies have focused on the identification of stiffness parameters in order to build new machines, and stiffness is often assumed to be constant. However, it has been shown that during human walking, stiffness profiles are not constant in time and have high modulations in particular when changing walking phases. In order to understand how the constant stiffness assumption influences the walking gait and how much modulation is needed in walking mechanisms to reproduce natural gaits, in this work we focus on the analysis of joint and bi-articular stiffness profiles modulation and the influence of these modulations on the walking motions in different walking environments, i.e. level ground, slope and stairs. The analysis is carried out on periodic steps from motion capture data. mapped on a 14 segments human model with spring damper systems.

One of the biggest challenges of humanoid robots is to keep the balance at any moment, as they can be subject to different types of external perturbations. Therefore, push recovery is a relevant issue in humanoid robotics, which still represents an open challenge. In literature, the most used method is based on the capture point, where a reduced model of the robot is used to compute recovery motions. In this paper we want to explore the problem with a different approach, by using whole-body models combined with optimal control. The robot is subject to an external perturbation, which is taken into account in the system dynamics as continuous external force. Optimal control is used to generate whole-body recovery motions by using the whole-body dynamic model of the robot. Instead of the capture point criterion, we optimize for a stable motion that allows to perform recovery within one step, at the end of which the robot is in a state of zero velocity. We apply the method to the model of the humanoid robot HeiCub, where serial springs can be mounted in the knee and ankle pitch actuators to change them into Series Elastic Actuators (SEA). The recovery motion is computed for both the model with and without SEA.

In this paper we present a method that uses optimal control for offline human gait synthesis that does not depend on motion capture data or task-specific controllers. Our method is based on efficient simulation of rigid multibody systems and a direct multiple-shooting method to solve the underlying space-time optimization problem. We formulated different optimization criteria and synthesized gaits for a fullbody 3-D human model with 34 degrees of freedom and compared the resulting movements with human data. By combining different criteria we are able to improve the similarity of the synthesized motions with respect to recorded human motioncapture motions.

The thirteen papers in this special section focus on the topic of movement science for humans and humanoids. The papers include the collection and organization of human movement data for enabling robotics research; the use of human movement as inspiration for humanoid planning, control, and motion generation; the development of algorithms for improved estimation of human and humanoid system parameters; and the use of human movement understanding in robotics applications including human-robot interaction and rehabilitation.

The anthropomorphic body form is a complex articulated system of links/limbs and joints, simultaneously redundant and underactuated, and capable of a wide range of sophisticated movement. The human body and its movement have long been a topic of study in physiology, anatomy, biomechanics, and neuroscience and have served as inspiration for humanoid robot design and control. This survey paper reviews the literature on robotics research using anthropomorphic design principles as an inspiration, at both the design and control levels. Next, anthropomorphic body modeling, motion analysis, and synthesis techniques are overviewed. Finally, key applications arising at the intersection of robotics and human movement science are introduced. The survey ends with a discussion of open research questions and directions for future work.

The MOBOT* project aims to develop assistive devices that support elderly patients with mobility disabilities during gait and sit-to-stand (STS) transfer. The device is supposed to help such patients to maintain an independent life through mobility as well as to regain basic life skills since self-effort is encouraged. Situation-adapted optimal assistance will be provided by the device both proactively and adaptively through behavior-based and context-aware robot control based on multimodal action recognition. The design of such a device, including the choice of actuators, which can provide the desired level of support without losing balance, is a challenging task. For these design choices, in particular STS motions have to be considered since they are more demanding for both the patient and the device. In this paper, we present a model-based optimization approach to this design task, using direct multiple shooting optimal control methods. They are applied to simultaneously determine optimal states and controls as well as the optimal mechanical design parameters of the assistive device that provide optimal STS assistance to subjects from a range of body heights and weights that represents the MOBOT target group.

In this paper we investigate the use of optimal control techniques to improve Functional Electrical Stimulation (FES) for drop foot correction on hemiplegic patients. A model of the foot and the tibialis anterior muscle, the contraction of which is controlled by electrical stimulation has been established and is used in the optimal control problem. The novelty in this work is the use of the ankle accelerations and shank orientations (so-called external states) in the model, which have been measured on hemiplegic patients in a previous experiment using Inertial Measurement Units (IMUs). The optimal control problem minimizes the square of muscle excitations which serves the overall goal of reducing energy consumption in the muscle. In a first step, an offline optimal control problem is solved for test purposes and shows the efficiency of the FES optimal control for drop foot correction. In a second step, a Nonlinear Model Predictive Control (NMPC) problem - or online optimal control problem, is solved in a simulated environment. While the ulitmate goal is to use NMPC on the real system, i.e. directly on the patient, this test in simulation was meant to show the feasibility of NMPC for online drop foot correction. In the optimization problem, a set of fixed constraints of foot orientation was applied. Then, an original adaptive constraint taking into account the current ankle height, was introduced and tested. Comparisons between results under fixed and adaptive constraints highlight the advantage of the adaptive constraints in terms of energy consumption, where its quadratic sum of controls, obtained by NMPC, was three times lower than with the fixed constraint. This feasibility study was a first step in application of NMPC on real hemiplegic patients for online FES-based drop foot correction. The adaptive constraints method presents a new and efficient approach in terms of muscular energy consumption minimization.

Yoyo playing may seem easy for a human, but it is a challenging problem for a humanoid robot. This paper presents an approach to generate yoyo motions for the humanoid robot, HRP-2, based on motion recorded from human yoyo playing, dynamical models and numerical optimal control techniques. We recorded vertical yoyo playing of 4 subjects measuring yoyo height and rotation angle as well as the corresponding hand motions. A detailed multi-phase yoyo model with impact collisions and control patterns of human yoyo playing were identified from these measurements. Based on this knowledge, reliable yoyo motions within the feasibility ranges of HRP-2 were generated using optimal control. The resulting motions have been implemented on the robot using open-loop and event-based quasi open-loop control strategies.

Building on previous propositions to generate walking gaits online through the use of linear model predictive control, the goal of this paper is to show that it is possible to allow on top of that a continuous adaptation of the positions of the foot steps, allowing the generation of stable walking gaits even in the presence of strong perturbations, and that this additional adaptation requires only a minimal modification of the previous schemes, especially maintaining the same linear model predictive form. Simulation results are proposed then on the HRP-2 humanoid robot, showing a significant improvement over the previous schemes.

We investigate the open-loop stability of a planar biped robot performing a periodic motion of forward somersaults with alternating single-leg contacts. The robot has a trunk and two actuated telescopic legs with point feet which are coupled to the trunk by actuated hinges. There is compliance and damping in the hip and in the legs. The concept of open-loop control implies that all actuators of the system receive predetermined inputs that are never altered by any feedback interference. Only with the right choice of model parameters and actuator inputs is it possible to create such self-stabilizing motions exploiting the natural stability properties of the system. These unknowns have been determined using special-purpose stability-optimization methods. The resulting motion is not only stable, but also a more efficient form of forward motion than running for the investigated robot.

We present two-dimensional monopods that are capable of stable periodic hopping motions without any feedback. It is shown that a large foot radius is not necessary for self-stabilization and that even one-legged robots with a point foot can perform open-loop stable motions although they have no statically stable standing position. The concept of open-loop stability implies that the actuators receive purely periodic torque or force inputs that are never altered with any feedback to prevent the robot from falling. The robots consist of a toroidal body and a leg that are connected by a hinge powered by a torque. The leg bounces on an adjustable spring. The design of these robots and the choice of model parameter values leading to stable motions is a difficult task that has been accomplished using newly developed stability optimization methods.

Studies a two-legged kneed walking robot with point feet. At any given moment, only one foot is in contact with the ground, and the switching is instantaneous. The robot can be considered as a simplified model of human walking. The periodic torque histories applied to each link are a priori prescribed for a motion and are not changed by any feedback interference. Nevertheless the robot is capable of naturally recovering from perturbations, returning to standard gait-a property that we call open-loop stable. We formulate the problem of open-loop stabilization as an optimal control problem. Design parameters and periodic torque inputs that lead to a stable configuration are computed using a two-level optimization procedure. We believe that this is the first demonstration of the ability to create stable actuated open-loop gaits of bipedal walking robots.