One of the most valuable contributions robotics can offer is support to daily human activities, yet rigid robots often fail to comply with safety regulations in the proximity of humans. Soft robotics takes inspiration from living organisms' ability to adapt to their environment using flexible structures. These systems have to generate mechanical forces and simultaneously sense their environment. We developed a soft gripper with integrated sensing microstructures by monolithically 3D printing the structure. The rubber gripper mimics the versatile sensing and actuation abilities of living organisms. This is done using stereolithographic printing technology, rubber material, and resistive, pressure sensitive EGaIn microchannels. Printed microscale pressure sensing cavities are filled with liquid metal and act as resistive pressure sensors. They imitate human haptic perception and provide a sensitivity of 0.5% kPa −1 . Simultaneously, a soft-robotic actuator design, which is derived from pneumatic networks, delivers a force of 2.5 N with 16 kPa of actuating pressure and an average efficiency of 0.56 mW kPa −1 . Monolithically 3D printed systems promise numerous advantages since the compliance matching between multi-modal capillary sensing networks and actuators enables scale production of smart soft manipulators. Potential applications include collaborative manufacturing and medical support systems such as exoskeletons.
We present and evaluate a microcontroller-optimized limitedmemory implementation of a Warping Longest Common Subsequence algorithm (WarpingLCSS). It permits to spot patterns within noisy sensor data in real-time in resource constrained sensor nodes. It allows variability in the sensed system dynamics through warping; it uses only integer operations; it can be applied to various sensor modalities; and it is suitable for embedded training to recognize new patterns. We illustrate the method on 3 applications from wearable sensing and activity recognition using 3 sensor modalities: spotting the QRS complex in ECG, recognizing gestures in everyday life, and analyzing beach volleyball. We implemented the system on a low-power 8-bit AVR wireless node and a 32-bit ARM Cortex M4 microcontroller. Up to 67 or 140 10-second gestures can be recognized simultaneously in real-time from a 10Hz motion sensor on the AVR and M4 using 8mW and 10mW respectively. A single gesture spotter uses as few as 135μW on the AVR. The method allows low data rate distributed in-network recognition and we show a 100 fold data rate reduction in a complex activity recognition scenario. The versatility and low complexity of the method makes it well suited as a generic pattern recognition method and could be implemented as part of sensor front-ends.
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An efficient computational methodology is proposed for optimal gear ratio planning in motor vehicle kinetic energy recovery systems (KERS) using a flywheel and continuously variable transmission (CVT). Initial modeling of a clutch-less KERS, comprising an input wheel, CVT, flywheel, and bearings, shows that the “least effort” or “minimum energy loss” optimal control problem can be formulated in two ways: one being a conventional two-state formulation involving input wheel angular velocity and CVT gear ratio, for which least effort control can be solved in simple cases with Pontryagin's maximum principle. The second formulation involves a single-state CVT gear ratio equation for which the input wheel angular velocity and acceleration appear as unknown time-dependent parameters. A novel multiparameter optimization methodology is proposed using the single-state formulation to find optimal CVT gear ratios by adopting two discrete time scales: one being a small time scale for numerical integration of the model, and the second involving discrete transitions, hundreds of times larger. Using Chebyshev polynomial expansions (CPEs) to initially generate sets of zero-energy-loss least effort kinematics for use as the time-dependent parameters in the CVT gear ratio equation, two solution approaches are developed. The first involves a single large discrete time transition, which only requires discretization of the input wheel angular acceleration at the start and end-of-transition. The second approach involves multiple large-scale discrete time transitions as a generalization of the first, but additionally needing discretization of the input wheel angular velocity, and the CVT gear ratio, plus dynamic programming to find the optimum. Both approaches are tested using the clutchless KERS model by assuming a “super CVT” gear ratio range (but with no restrictions for use with slipping clutches). Comparison with least effort control via Pontryagin's maximum principle shows that the single transition approach is in practice far superior. The single transition approach is then used to compare a minimum energy loss clutchless KERS gear ratio plan, with one obtained using constant input wheel angular acceleration as a benchmark. This comparison, involving power losses throughout the KERS, shows the very clear benefits of adopting an optimal gear ratio plan.
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