A soft pressure sensor skin for hand and wrist orthoses

Side effects caused by excessive contact pressure such as discomfort and pressure sores are commonly complained by patients wearing orthoses. These problems leading to low patient compliance decrease the effectiveness of the device. To mitigate side effects, this study describes the design and fabrication of a soft sensor skin with strategically placed 12 sensor units for static contact pressure measurement beneath a hand and wrist orthosis. A Finite Element Model was built to simulate the pressure on the hand of a subject and sensor specifications were obtained from the result to guide the design. By testing the fabricated soft sensor skin on the subject, contact pressure between 0.012 MPa and 0.046 MPa was detected, revealing the maximum pressure at the thumb metacarpophalangeal joint which was the same location of the highest pressure of simulation. In this letter, a new fabrication method combining etching and multi-material additive manufacture was introduced to produce multiple sensor units as a whole. Furthermore, a novel fish-scale structure as the connection among sensors was introduced to stabilize sensor units and reinforce the soft skin. Experimental analysis reported that the sensor signal is repeatable, and the fish-scale structure facilitates baseline resuming of sensor signal during relaxation

Soft fingertips with tactile sensing and active deformation for robust grasping of delicate objects

Soft fingertips have shown significant adaptability for grasping a wide range of object shapes, thanks to elasticity. This ability can be enhanced to grasp soft, delicate objects by adding touch sensing. However, in these cases, the complete restraint and robustness of the grasps have proved to be challenging, as the exertion of additional forces on the fragile object can result in damage. This letter presents a novel soft fingertip design for delicate objects based on the concept of embedded air cavities, which allow the dual ability of tactile sensing and active shape-changing. The pressurized air cavities act as soft tactile sensors to control gripper position from internal pressure variation; and active fingertip deformation is achieved by applying positive pressure to these cavities, which then enable a delicate object to be kept securely in position, despite externally applied forces, by form closure. We demonstrate this improved grasping capability by comparing the displacement of grasped delicate objects exposed to high-speed motions. Results show that passive soft fingertips fail to restrain fragile objects at accelerations as low as 0.1 m/s 2 , in contrast, with the proposed fingertips delicate objects are completely secure even at accelerations of more than 5 m/s 2

Precise in-hand manipulation of soft objects using soft fingertips with tactile sensing and active deformation

While soft fingertips have shown significant development for grasping tasks, its ability to facilitate the manipulation of objects within the hand is still limited. Thanks to elasticity, soft fingertips enhance the ability to grasp soft objects. However, the in-hand manipulation of these objects has proved to be challenging, with both soft fingertips and traditional designs, as the control of coordinated fine fingertip motions and uncertainties for soft materials are intricate. This paper presents a novel technique for in-hand manipulating soft objects with precision. The approach is based on enhancing the dexterity of robot hands via soft fingertips with tactile sensing and active shape changing; such that pressurized air cavities act as soft tactile sensors to provide closed loop control of fingertip position and avoid object’s damage, and pneumatic-tuned positive-pressure deformations act as a localized soft gripper to perform additional translations and rotations. We model the deformation of the soft fingertips to predict the in-hand manipulation of soft objects and experimentally demonstrate the resulting in-hand manipulation capabilities of a gripper of limited dexterity with an algorithm based on the proposed dual abilities. Results show that the introduced approach can ease and enhance the prehensile in-hand translation and rotation of soft objects for precision tasks across the hand workspace, without damage

Predicting the Mean First Passage Time (MFPT) to reach any state for a passive dynamic walker with steady state variability

Idealized passive dynamic walkers (PDW) exhibit limit cycle stability at steady state. Yet in reality, uncertainty in ground interaction forces result in variability in limit cycles even for a simple walker known as the Rimless Wheel (RW) on seemingly even slopes. This class of walkers is called metastable walkers in that they usually walk in a stable limit cycle, though guaranteed to eventually fail. Thus, control action is only needed if a failure state (i.e. RW stopping down the ramp) is imminent. Therefore, efficiency of estimating the time to reach a failure state is key to develop a minimal intervention controller to inject just enough energy to overcome a failure state when required. Current methods use what is known as a Mean First Passage Time (MFPT) from current state (rotary speed of RW at the most recent leg collision) to an arbitrary state deemed to be a failure in the future. The frequently used Markov chain based MFPT prediction requires an absorbing state, which in this case is a collision where the RW comes to a stop without an escape. Here, we propose a novel method to estimate an MFPT from current state to an arbitrary state which is not necessarily an absorbing state. This provides freedom to a controller to adaptively take action when deemed necessary. We demonstrate the proposed MFPT predictions in a minimal intervention controller for a RW. Our results show that the proposed method is useful in controllers for walkers showing up to 44.1% increase of time-to-fail compared to a PID based closed-loop controller

Significance of the Compliance of the Joints on the Dynamic Slip Resistance of a Bioinspired Hoof

Robust mechanisms for slip resistance are an open challenge in legged locomotion. Animals such as goats show impressive ability to resist slippage on cliffs. It is not fully known what attributes in their body determine this ability. Studying the slip resistance dynamics of the goat may offer insight towards the biologically-inspired design of robotic hooves. This paper tests how the embodiment of the hoof contributes to solving the problem of slip resistance. We ran numerical simulations and experiments using a passive robotic goat hoof for different compliance levels of its 3 joints. We established that compliant yaw and pitch and stiff roll can increase the energy required to slide the hoof by ≈ 20% compared to the baseline (stiff hoof). Compliant roll and pitch allow the robotic hoof to adapt to the irregularities of the terrain. This produces an Anti-Lock Braking System-like behavior of the robotic hoof for slip resistance. Therefore, the pastern and coffin joints have a substantial effect on the slip resistance of the robotic hoof while the fetlock joint has the lowest contribution. These shed insights into how robotic hooves can be used to autonomously improve slip resistance