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.

Hand closure model for planning top grasps with soft robotic hands

Automating the act of grasping is one of the most compelling challenges in robotics. In recent times, a major trend has gained the attention of the robotic grasping community: soft manipulation. Along with the design of intrinsically soft robotic hands, it is important to devise grasp planning strategies that can take into account the hand characteristics, but are general enough to be applied to different robotic systems. In this article, we investigate how to perform top grasps with soft hands according to a model-based approach, using both power and precision grasps. The so-called closure signature (CS) is used to model closure motions of soft hands by associating to them a preferred grasping direction. This direction can be aligned to a suitable direction over the object to achieve successful top grasps. The CS-alignment is here combined with a recently developed AI-driven grasp planner for rigid grippers that is adjusted and used to retrieve an estimate of the optimal grasp to be performed on the object. The resulting grasp planner is tested with multiple experimental trials with two different robotic hands. A wide set of objects with different shapes was grasped successfully.

Quasi-static analysis of planar sliding using friction patches

Flat objects lying on a surface are hard to grasp, but could be manipulated by sliding along the surface in a non-prehensile manner. This strategy is commonly employed by humans as pre-manipulation, for example to bring a cell phone to the edge of a table to pick it up. To endow robots with a similar capability, we introduce a mathematical model of planar sliding by means of a soft finger. The model reveals various aspects of interaction through frictional contacts, which can be used for planning and control. Specifically, using a quasi-static analysis we are able to derive a hybrid dynamical system to predict the motion of the object and the interaction forces. The conditions for which the object sticks to the friction patch, pivots, or completely slides against it are obtained. It is possible to find fixed points of the system and the path taken by the object to reach such configurations. Theoretical as well as comprehensive experimental results are presented.

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.

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.

Endoscopic add-on stiffness probe for real-time soft surface characterisation in MIS

This paper explores a novel stiffness sensor which is mounted on the tip of a laparoscopic camera. The proposed device is able to compute stiffness when interacting with soft surfaces. The sensor can be used in Minimally Invasive Surgery, for instance, to localise tumor tissue which commonly has a higher stiffness when compared to healthy tissue. The purely mechanical sensor structure utilizes the functionality of an endoscopic camera to the maximum by visually analyzing the behavior of trackers within the field of view. Two pairs of spheres (used as easily identifiable features in the camera images) are connected to two springs with known but different spring constants. Four individual indenters attached to the spheres are used to palpate the surface. During palpation, the spheres move linearly towards the objective lens (i.e. the distance between lens and spheres is changing) resulting in variations of their diameters in the camera images. Relating the measured diameters to the different spring constants, a developed mathematical model is able to determine the surface stiffness in real-time. Tests were performed using a surgical endoscope to palpate silicon phantoms presenting different stiffness. Results show that the accuracy of the sensing system developed increases with the softness of the examined tissue.