## 2 Related Work and Contributions
### Strain Gauges for Force Measurements in Robotics  
Simple strain gauges and load cells have found their application in a number of areas including robotics. In [26], four simple 1D 50 N load cells were used to construct force-sensing shoes for the Nao robot. The mechanical construction consisted of two plates between which four load cells were sandwiched; a design similar to that of standard force plates. The sensors were calibrated and were used to measure the centre of pressure and ground reaction forces beneath each foot. Obtained results showed significant improvement in accuracy and precision in both targeted parameters over robots built in force-sensing resistor systems. A similar sandwich-based design with two, four load cell configurations with an 8 N measurement range was used in [27]. The authors designed a 3-axis force sensor for use with robot-assisted minimally invasive surgery applications in a teleoperation setting. The sensor achieved an average error below 0.15 N in all directions, and an average grip force error of 0.156 N.  
Simple load cells can also be used in legged robots for ground reaction force estimation while being embedded in their mechanical construction, as was the case in [28]. In this research, the authors used two 10 kg load cells (TAL220), positioned at fixed angles and rigidly mounted to machine-milled pieces which were part of the robot legs. Measured data were then processed by a neural network which estimated the required 2-dimensional (2D) ground reaction forces. These forces were estimated with an accuracy of above 98.5%. Simple strain gauges can also be used for different applications within wheeled mobile robots, like in [29], where authors used several bent sensors to measure 3-dimensional (3D) forces of payload placed on a mobile robot platform. Several different designs were tested in simulation only, and the best one measured payload mass with an accuracy of 0.2 kg, and lever lengths with an accuracy of 0.1 m.  
More complex sensor structures can be constructed out of simple (single) strain gauge cells, as was carried out in [30], where four strain gauges were used to measure 2D forces and torque. The obtained results demonstrated that force and torque measurements could be decoupled by combining measurements from different strain gauge sets. In their conclusion, the authors state that this type of sensor might be appropriate for robotic applications. Another such example, but of a more complex sensor structure, is presented in [31], where the authors proposed the development of a complex humanoid robot. One part of its sensing and control structure was a 6-axis sensor which made use of load cells mounted on all sides of each spoke, in a three-spoke design. The complex sensor body was machined in order to obtain a linear response in the strain-stress relationship. The sensor performance was not tested in the manuscript. The closest research to our own (in the hardware part) can be found in [32], where a very similar design (with three load cells and four strain gauges in four-spoke architecture) was used to measure robot base forces/torques with the aim of automated tool-changing application. However, the signal processing part is significantly different and more complex since it is based on an accurate robot model with the crucial step of identification of the dynamic parameters. Based on the model, two custom calibration procedures were developed. Also, the authors stated that the mechanical design used is robot-specific, which is not the case in our approach, where neural networks are robot-specific. The paper demonstrated the successful implementation of the approach in tool-changing tasks but did not provide a numerical comparison between estimated and measured forces.