3/5/2023 0 Comments Gobot simulatorTo address this limitation this paper proposes a novel optical-based soft-tipped force sensor capable of adjusting its range and sensitivity through pneumatic modulation. However, current force sensors have limited usability in applications such as grasping and palpation, where the range of angled forces changes between tasks. The effect of the material properties of the elastomer on force ranges and sensitivity values of the proposed sensor is also discussed.įorce sensors are essential for measuring and controlling robot-object interactions. The proposed sensor shows a perfectly linear behavior and a low hysteresis error of 8.3%, making it suitable for tactile sensing and biomedical applications. The force range for the normal, shear and angular direction is obtained as 0–20 N, 0–3.5 N and 0–1.5 N, respectively. The force sensitivity of the proposed sensor in normal, shear and angular directions is 16 mV/N, 30 mV/N and 81 mV/N, respectively, with minimum mechanical crosstalk. The experimental characterization of the proposed sensor is performed for applied force in normal, shear and 45° angular direction. The initial spacing between the magnets and the Hall sensors is optimized to achieve a large displacement range using finite element method (FEM) simulations. The proposed sensor is fabricated using rapid prototyping techniques and utilizes Neodymium magnets embedded in an elastomer over Hall sensors such that their displacement produces a voltage change that can be used to calculate the applied force. The proposed sensor is designed to fully decouple the output response for normal, shear and angular forces. This paper presents a multi-axis low-cost soft magnetic tactile sensor with a high force range for force feedback in robotic surgical systems. Different bladder compression profiles are evaluated to characterize IR range finding and HE based techniques in application scenarios. Different NN configurations are examined to determine a configuration that provides accurate estimates with as few nodes as possible. Efficacy of different training data sets on NN performance is studied. The paper presents the HE sensor array, signal processing of HE voltage data, and then a Neural Network (NN) for predicting bladder compression. A Hall-Effect (HE) magnetic sensing system is also examined where HE sensors embedded in the base of the bladder sense the position of a magnet in the top of the bladder. An IR rangefinder-based solution is evaluated using regression techniques as well as a Neural Network to estimate bladder compression. Compression of these bladders determines impact dissipation hence the focus of this paper is sensing and estimation of bladder compression. This research focuses on soft robotic bladders that are used to monitor and control the interaction between a user's head and the shell of a Smart Helmet. The proposed magnetic tactile sensor shows perfectly linear behaviour with a low hysteresis error value of 8.3% and for the repeatability test of the sensor an error of 6.4% is achieved. To assess behaviour of sensor for the dynamic input force the proposed sensor is also tested for the frequency of 4 Hz. Similarly, the sensor having elastomer as RTV-528 silicone rubber works well for a force range of 50 N in normal direction, 5.5 N in shear direction and 4 N in angular direction with sensitivities of 2.52 mV/N, 3.4 mV/N and 25 mV/N for normal, shear and angular directions respectively. The sensor having elastomer as Ecoflex 00-30 works well for a normal force of 20 N in this direction, 3.5 N in shear direction and 1.5 N in angular direction with sensitivities of 16 mV/N, 30 mV/N and 81 mV/N for normal and shear angular force directions respectively. The sensor is fabricated using two types of elastomers based on their stiffness values. FEM simulations are carried out for robust location estimation of the embedded magnets in the elastomer. The movement of magnets due to the applied force causes the change in magnetic flux and thus causing a voltage change in the Hall sensors. The magnetic tactile sensor consists of four SMD Hall sensors with four magnets embedded in the soft elastomer. This thesis aims to address the afore mentioned limitations using design optimization through FEM simulations, decoupling of force using mathematical model and by testing the sensor according to real time applications. Tactile force sensors developed to date lacked the capability to detect multi-axial forces, flexibility, high dynamic and static force range and frequency response. To increase the awareness of surgical robotic systems a magnetic transduction mechanism based tactile force sensor is proposed. The major limitation of surgical robotic systems is that they lack sense of tactile force feedback during object grasping and tissue manipulation. Robotic surgical procedures have gained a lot more importance in the previous years.
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