Trajectory Control-An Effective Strategy for Controlling Multi-DOF Upper Limb Prosthetic Devices.

Despite great innovations in upper-extremity prosthetic hardware in recent decades, controlling a multiple joint upper limb prosthesis such as an elbow/wrist/hand system is still an open clinical challenge, in large part due to an insufficient number of control inputs available to users. While simultaneous control is in its early stages, the common control approach is sequential control, in which joints and grasps are driven one at a time. In this paper, we introduce and evaluate a concept we call trajectory control, that builds upon this approach, in which motions of the wrist, elbow, and shoulder DOFs (and subsets of them) are coupled into predefined sets of coordinated trajectories; to be selected by the user and driven with a single input variable. These trajectories were designed based on an earlier motion study of activities of daily life obtained from human demonstrations. We experimentally evaluate the efficacy of our approach through a human subjects study in which tasks are performed in a virtual environment. The results show that as device complexity increased (i.e. greater number of DOFs corresponding to more proximal amputations), participants were able to complete tasks faster with trajectory control while exhibiting similar levels of body compensation when compared to sequential and simultaneous control. Additionally, participants found trajectory control to be more intuitive and displayed more natural movement.

Exploiting Robot Hand Compliance and Environmental Constraints for Edge Grasps.

This paper presents a method to grasp objects that cannot be picked directly from a table, using a soft, underactuated hand. These grasps are achieved by dragging the object to the edge of a table, and grasping it from the protruding part, performing so-called slide-to-edge grasps. This type of approach, which uses the environment to facilitate the grasp, is named Environmental Constraint Exploitation (ECE), and has been shown to improve the robustness of grasps while reducing the planning effort. The paper proposes two strategies, namely Continuous Slide and Grasp and Pivot and Re-Grasp, that are designed to deal with different objects. In the first strategy, the hand is positioned over the object and assumed to stick to it during the sliding until the edge, where the fingers wrap around the object and pick it up. In the second strategy, instead, the sliding motion is performed using pivoting, and thus the object is allowed to rotate with respect to the hand that drags it toward the edge. Then, as soon as the object reaches the desired position, the hand detaches from the object and moves to grasp the object from the side. In both strategies, the hand positioning for grasping the object is implemented using a recently proposed functional model for soft hands, the closure signature, whereas the sliding motion on the table is executed by using a hybrid force-velocity controller. We conducted 320 grasping trials with 16 different objects using a soft hand attached to a collaborative robot arm. Experiments showed that the Continuous Slide and Grasp is more suitable for small objects (e.g., a credit card), whereas the Pivot and Re-Grasp performs better with larger objects (e.g., a big book). The gathered data were used to train a classifier that selects the most suitable strategy to use, according to the object size and weight. Implementing ECE strategies with soft hands is a first step toward their use in real-world scenarios, where the environment should be seen more as a help than as a hindrance.

Real-Time Vision-Based Stiffness Mapping †.

This paper presents new findings concerning a hand-held stiffness probe for the medical diagnosis of abnormalities during palpation of soft-tissue. Palpation is recognized by the medical community as an essential and low-cost method to detect and diagnose disease in soft-tissue. However, differences are often subtle and clinicians need to train for many years before they can conduct a reliable diagnosis. The probe presented here fills this gap providing a means to easily obtain stiffness values of soft tissue during a palpation procedure. Our stiffness sensor is equipped with a multi degree of freedom (DoF) Aurora magnetic tracker, allowing us to track and record the 3D position of the probe whilst examining a tissue area, and generate a 3D stiffness map in real-time. The stiffness probe was integrated in a robotic arm and tested in an artificial environment representing a good model of soft tissue organs; the results show that the sensor can accurately measure and map the stiffness of a silicon phantom embedded with areas of varying stiffness.

Multi-Axis Force/Torque Sensor Based on Simply-Supported Beam and Optoelectronics.

This paper presents a multi-axis force/torque sensor based on simply-supported beam and optoelectronic technology. The sensor's main advantages are: (1) Low power consumption; (2) low-level noise in comparison with conventional methods of force sensing (e.g., using strain gauges); (3) the ability to be embedded into different mechanical structures; (4) miniaturisation; (5) simple manufacture and customisation to fit a wide-range of robot systems; and (6) low-cost fabrication and assembly of sensor structure. For these reasons, the proposed multi-axis force/torque sensor can be used in a wide range of application areas including medical robotics, manufacturing, and areas involving human-robot interaction. This paper shows the application of our concept of a force/torque sensor to flexible continuum manipulators: A cylindrical MIS (Minimally Invasive Surgery) robot, and includes its design, fabrication, and evaluation tests.