Dexterous magnetic manipulation of conductive non-magnetic objects.

Dexterous magnetic manipulation of ferromagnetic objects is well established, with three to six degrees of freedom possible depending on object geometry1. There are objects for which non-contact dexterous manipulation is desirable that do not contain an appreciable amount of ferromagnetic material but do contain electrically conductive material. Time-varying magnetic fields generate eddy currents in conductive materials2-4, with resulting forces and torques due to the interaction of the eddy currents with the magnetic field. This phenomenon has previously been used to induce drag to reduce the motion of objects as they pass through a static field5-8, or to apply force on an object in a single direction using a dynamic field9-11, but has not been used to perform the type of dexterous manipulation of conductive objects that has been demonstrated with ferromagnetic objects. Here we show that manipulation, with six degrees of freedom, of conductive objects is possible by using multiple rotating magnetic dipole fields. Using dimensional analysis12, combined with multiphysics numerical simulations and experimental verification, we characterize the forces and torques generated on a conductive sphere in a rotating magnetic dipole field. With the resulting model, we perform dexterous manipulation in simulations and physical experiments.

In Vitro Simulation of Shoulder Motion Driven by Three-Dimensional Scapular and Humeral Kinematics.

In vitro simulation of three-dimensional (3D) shoulder motion using in vivo kinematics obtained from human subjects allows investigation of clinical conditions in the context of physiologically relevant biomechanics. Herein, we present a framework for laboratory simulation of subject-specific kinematics that combines individual 3D scapular and humeral control in cadavers. The objectives were to: (1) robotically simulate seven healthy subject-specific 3D scapulothoracic and glenohumeral kinematic trajectories in six cadavers, (2) characterize system performance using kinematic orientation accuracy and repeatability, and muscle force repeatability metrics, and (3) analyze effects of input kinematics and cadaver specimen variability. Using an industrial robot to orient the scapula range of motion (ROM), errors with repeatability of ±0.1 mm and <0.5 deg were achieved. Using a custom robot and a trajectory prediction algorithm to orient the humerus relative to the scapula, orientation accuracy for glenohumeral elevation, plane of elevation, and axial rotation of <3 deg mean absolute error (MAE) was achieved. Kinematic accuracy was not affected by varying input kinematics or cadaver specimens. Muscle forces over five repeated setups showed variability typically <33% relative to the overall simulations. Varying cadaver specimens and subject-specific human motions showed effects on muscle forces, illustrating that the system was capable of differentiating changes in forces due to input conditions. The anterior and middle deltoid, specifically, showed notable variations in patterns across the ROM that were affected by subject-specific motion. This machine provides a platform for future laboratory studies to investigate shoulder biomechanics and consider the impacts of variable input kinematics from populations of interest, as they can significantly impact study outputs and resultant conclusions.

Development of a Novel Computational Model for Evaluating Fall Risk in Patient Room Design.

OBJECTIVES: This study proposes a computational model to evaluate patient room design layout and features that contribute to patient stability and mitigate the risk of fall. BACKGROUND: While common fall risk assessment tools in nursing have an acceptable level of sensitivity and specificity, they focus on intrinsic factors and medications, making risk assessment limited in terms of how the physical environment contributes to fall risk. METHODS: We use literature to inform a computational model (algorithm) to define the relationship between these factors and the risk of fall. We use a trajectory optimization approach for patient motion prediction. RESULTS: Based on available data, the algorithm includes static factors of lighting, flooring, supportive objects, and bathroom doors and dynamic factors of patient movement. This preliminary model was tested using four room designs as examples of typical room configurations. Results show the capabilities of the proposed model to identify the risk associated with different room layouts and features. CONCLUSIONS: This innovative approach to room design evaluation and resulting estimation of patient fall risk show promise as a proactive evidence-based tool to evaluate the relationship of potential fall risk and room design. The development of the model highlights the challenge of heterogeneity in factors and reporting found in the studies of patient falls, which hinder our understanding of the role of the built environment in mitigating risk. A more comprehensive investigation comparing the model with actual patient falls data is needed to further refine model development.

Replicating dynamic humerus motion using an industrial robot.

Transhumeral percutaneous osseointegrated prostheses provide upper-extremity amputees with increased range of motion, more natural movement patterns, and enhanced proprioception. However, direct skeletal attachment of the endoprosthesis elevates the risk of bone fracture, which could necessitate revision surgery or result in loss of the residual limb. Bone fracture loads are direction dependent, strain rate dependent, and load rate dependent. Furthermore, in vivo, bone experiences multiaxial loading. Yet, mechanical characterization of the bone-implant interface is still performed with simple uni- or bi-axial loading scenarios that do not replicate the dynamic multiaxial loading environment inherent in human motion. The objective of this investigation was to reproduce the dynamic multiaxial loading conditions that the humerus experiences in vivo by robotically replicating humeral kinematics of advanced activities of daily living typical of an active amputee population. Specifically, 115 jumping jack, 105 jogging, 15 jug lift, and 15 internal rotation trials-previously recorded via skin-marker motion capture-were replicated on an industrial robot and the resulting humeral trajectories were verified using an optical tracking system. To achieve this goal, a computational pipeline that accepts a motion capture trajectory as input and outputs a motion program for an industrial robot was implemented, validated, and made accessible via public code repositories. The industrial manipulator utilized in this study was able to robotically replicate over 95% of the aforementioned trials to within the characteristic error present in skin-marker derived motion capture datasets. This investigation demonstrates the ability to robotically replicate human motion that recapitulates the inertial forces and moments of high-speed, multiaxial activities for biomechanical and orthopaedic investigations. It also establishes a library of robotically replicated motions that can be utilized in future studies to characterize the interaction of prosthetic devices with the skeletal system, and introduces a computational pipeline for expanding this motion library.

3D-Printing and Machine Learning Control of Soft Ionic Polymer-Metal Composite Actuators.

This paper presents a new manufacturing and control paradigm for developing soft ionic polymer-metal composite (IPMC) actuators for soft robotics applications. First, an additive manufacturing method that exploits the fused-filament (3D printing) process is described to overcome challenges with existing methods of creating custom-shaped IPMC actuators. By working with ionomeric precursor material, the 3D-printing process enables the creation of 3D monolithic IPMC devices where ultimately integrated sensors and actuators can be achieved. Second, Bayesian optimization is used as a learning-based control approach to help mitigate complex time-varying dynamic effects in 3D-printed actuators. This approach overcomes the challenges with existing methods where complex models or continuous sensor feedback are needed. The manufacturing and control paradigm is applied to create and control the behavior of example actuators, and subsequently the actuator components are combined to create an example modular reconfigurable IPMC soft crawling robot to demonstrate feasibility. Two hypotheses related to the effectiveness of the machine-learning process are tested. Results show enhancement of actuator performance through machine learning, and the proof-of-concepts can be leveraged for continued advancement of more complex IPMC devices. Emerging challenges are also highlighted.

Grip Stabilization of Novel Objects Using Slip Prediction.

Controlling contact with arbitrary, unknown objects defines a fundamental problem for robotic grasping and in-hand manipulation. In real-world scenarios, where robots interact with a variety of objects, the sheer number of possible contact interactions prohibits acquisition of the necessary models for all objects of interest. As an alternative to traditional control approaches that require accurate models, predicting the onset of slip can enable controlling contact interactions without explicit model knowledge. In this article, we propose a grip stabilization approach for novel objects based on slip prediction. Using tactile information, such as applied pressure and fingertip deformation, our approach predicts the emergence of slip and modulates the contact forces accordingly. We formulate a supervised-learning problem to predict the future occurrence of slip from high-dimensional tactile information provided by a BioTac sensor. This slip mapping generalizes across objects, including objects absent during training. We evaluate how different input features, slip prediction time horizons, and available tactile information channels, impact prediction accuracy. By mounting the sensor on a PA-10 robotic arm, we show that employing prediction in a controller's feedback loop yields an object grip stabilization controller that can successfully stabilize multiple, previously unknown objects by counteracting slip events.