In this letter, we address the challenge of sensor fusion in Soft Robotics for estimating forces and deformations. In the context of intrinsic sensing, we propose the use of soft capacitive sensing to find a contact's location, and the use of pneumatic sensing to estimate the force intensity and the deformation. Using a FEM-based numerical approach, we integrate both sensing streams and model two Soft Robotics devices we have conceived. These devices are a Soft Pad and a Soft Finger. We show in an evaluation that external forces on the Soft Pad can be estimated and that the shape of the Soft Finger can be reconstructed.

With respect to automotive safety, the driver plays a crucial role. Stress level, tiredness, and distraction of the driver are therefore of high interest. In this paper, a driver state detection system based on cellular neural networks (CNNs) to monitor the driver's stress level is presented. We propose to include a capacitive-based wireless hand detection (position and touch) sensor for a steering wheel utilizing ink-jet printed sensor mats as an input sensor in order to improve the performance. A driving simulator platform providing a realistic virtual traffic environment is utilized to conduct a study with 22 participants for the evaluation of the proposed system. Each participant is driving in two different scenarios, each representing one of the two no-stress/stress driver states. A “threefold” cross validation is applied to evaluate our concept. The subject dependence is considered carefully by separating the training and testing data. Furthermore, the CNN approach is benchmarked against other state-of-the-art machine learning techniques. The results show a significant improvement combining sensor inputs from different driver inherent domains, giving a total related detection accuracy of 92%. Besides that, this paper shows that in case of including the capacitive hand detection sensor, the accuracy increases by 10%. These findings indicate that adding a subject-independent sensor, such as the proposed capacitive hand detection sensor, can significantly improve the detection performance.

Soft robotics are considered one of the most promising approaches toward fully collaborative robotic devices. Soft robotic systems are intrinsically safe due to their compliant nature. Using additive fabrication for the development of soft capacitive sensors enables high flexibility and individualization capabilities in terms of design and material properties. In this work, we outline the sensorization of stretchable capacitive sensors for proximity and tactile detection. This sensor will represent an essential component of a soft robotic finger in future work. While the functional principle has already been shown previously, here we explore the design space further and perform more in depth test of the influences of the strain on the tactile measurements. The capacitive pad’s sensitivity is evaluated by applying normal force on each sensor, where the applied force is ranged between 1 and 16 N, with resultant capacitive change of the four tactile sensors between 1 and 60 pF. The capacitive pad’s proximity sensor is tested using five different materials, with proximity range of 20 mm, with change in capacitance between 0.1 and 2.4 pF. An essential aspect of soft robotics is that the sensor may also be subject to substantial deformations. We demonstrate that our proposed sensor design can withstand large strains of more than 35%, while the sensitivity toward sensing normal forces does not significantly change. This demonstrates that the proposed sensor design will be suitable for various soft robotics applications, where large deformations may occur.

Mobile robotics is already well established in today’s production lines. Navigation, control and perception for mobile robots are vivid fields of research fostering advances in Industry 4.0. In order to increase the flexibility of such mobile platforms, it is also common practice to add serial manipulator arms to their yielding systems with nine degrees of freedom and more. These platforms are not limited to industry but are supportive in various field such as service, assistance, teleoperation and also rehabilitation. Due to the operation of such increasingly complex systems in less structured and dynamic environments - often in close contact with humans - more demanding challenges evolve in terms of systems design, control and sensors. These challenges are also reflected in the various RoboCup leagues. In this paper, we discuss state-of-the-art developments in mobile manipulation using developments and work done in the context of the RoboCup competition as design examples. Additionally, we elaborate on the recent challenges of the RoboCup Rescue League as well as on the RoboCup@Work League.

We address multi-walled carbon nanotubes (MWCNTs) for structural health monitoring in adhesive bonds, such as in building structures. MWCNT-loaded composites are employed to sense strain changes under tension load using an AC impedance measurement setup. Different weight percentages of 1, 1.5, 2 and 3 wt % MWCNTs are added to the base epoxy resin using different dispersion times, i.e., 5, 10, and 15 min. The equivalent parallel resistance of the specimens is first measured by applying an alternating voltage at different frequencies. To determine the mechanical as well as sensory properties, the specimens are then subjected to a tensile test with concurrent impedance measurement at a fixed pre-chosen frequency. Using alternating voltage, a higher sensitivity of the impedance reading can be achieved. Employing these sensors in buildings and combining the readings of a network of such devices can significantly improve the buildings’ safety. Additionally, networks of such sensors can be used to identify necessary maintenance actions and locations.

This work reports on inkjet- and 3D-printed force/pressure sensing devices. The employed printing processes can enable cost- and resource-efficient, fast and flexible designs compared to solid-state technology which is used as a reference design. The pressure sensing devices are realized as either 3D-printed steel or 3D-printed ceramic diaphragms. Both designs thus withstand harsh environmental conditions and elevated temperatures. Additionally, the increased size guarantees less sensitivity to dirt or moisture due to the larger diameter of the diaphragms. The hardware used for the capacitive sensor read-out is based on a shunt measurement system and subsequent demodulation. This architecture enables long cabling, allowing for large parasitic capacitances, without significant loss of transferred signal power, due to the employed power matching to the transmission line. Physical analyses such as contact angle measurements, profilometer measurements, and focused ion beam analysis are done to characterize the resulting printed surfaces. Then, the response of both designs is evaluated by applying force on the diaphragms surface using a moveable load cell. The gathered results are also compared to finite element method simulations.