In this study, a novel strain gauge arrangement and error reduction techniques were proposed to minimize crosstalk reading and simultaneously increase sensitivity on a decoupled six-axis force–moment (F/M) sensor. The calibration process that comprises the least squares method and error reduction techniques was implemented to obtain a robust decoupling matrix. A decoupling matrix is very crucial for minimizing error and crosstalk. A novel strain gauge arrangement that comprised double parallel strain gauges in the decoupled six-axis force–moment sensor was implemented to obtain high sensitivity. The experimental results revealed that the maximum calibration error, F/M sensor measurement error, and crosstalk readings were reduced to 3.91%, 1.78%, and 4.78%, respectively.

Strain gage type six-axis force/moment (F/M) sensors have been largely studied and implemented in industrial applications by using an external data acquisition board (DAQ). The use of external DAQs will ill-affect accuracy and crosstalk due to the possibility of voltage drop through the wire length. The most recent research incorporated DAQ within a relatively small F/M sensor, but only for sensors of the capacitance and optical types. This research establishes the integration of a high-efficiency DAQ on six-axis F/M sensor with a revolutionary arrangement of 32 strain gages. The updated structural design was optimized using the sequential quadratic programming method and validated using Finite Element Analysis (FEA). A new, integrated DAQ system was designed, tested, and compared to commercial DAQ systems. The proposed six-axis F/M sensor was examined with the calibrated jig. The results show that the measurement error and crosstalk have been significantly reduced to 1.15% and 0.68%, respectively, the best published combination at this moment.

In this study, a novel six-axis force/moment (F/M) sensor was developed. The sensor has a novel ring structure comprising a cross-beam elastic body with sliding and rotating mechanisms to achieve complete decoupling. The unique sliding and rotating mechanisms can reduce cross-talk effects caused by minimized structural interconnection. The forces F_x, F_y, and F_z and moments M_x, M_y, and M_z can be measured for the six-axis F/M sensors according to the elastic deformation of strain gauges attached to the cross beam. Herein, we provide detailed descriptions of the mathematical models, model idealizations, model creation, and the mechanical decoupling principle. The paper also presents a theoretical analysis of the strain based on Timoshenko beam theory and the subsequent validation of the analysis results through a comparison of the results with those obtained from a numerical analysis conducted using finite element analysis simulations. The sensor was subjected to experimental testing to obtain the maximum cross-talk errors along the following six axes under different loadings (the errors are presented in parentheses): F_x under SM_y (2.12%), F_y under SM_x (1.88%), F_z under SM_z (2.02%), M_x under SF_z (1.15%), M_y under SF_x (1.80%), and M_z under SF_x (2.63%). The proposed sensor demonstrated a considerably improved cross-talk error performance compared with existing force sensors.

The use of industrial robots for machining operations such as polishing is gaining popularity these days. However, maintaining a constant normal contact on unknown geometrical surfaces still remains a challenge. In order to cope with the issue, this research has developed a unique compliant robotic end-effector that can measure the depth and angles deviation of the robot from the normal complex surfaces in real-time without any prior geometric model of the workpiece. The indigenously developed compliant robotic end-effector is made up of three main components: a novel 3-axis force/torque sensor, a compliant contact pin, and a data acquisition board (DAQ). Initially, the 3-axis force/torque sensor has been validated with both simulation and testing. The advantage of being able to take measurements in real-time is provided by the DAQ’s automatic weight compensation feature. Additionally, the pose correction algorithm receives the estimated contact force as feedback during robot surface tracking in order to assess depth and angles and determine the proper distance and perpendicular relationship between the surface and end-effector. The proposed scheme is then evaluated using a 6-axis industrial robot. The experimental results show that the maximum average depth and angle errors are 0.13 mm and 0.64°, respectively. The developed system has achieved significant improvement in contact-based robot surface measuring.

This study introduces an innovative smart wheelchair designed to improve mobility and health monitoring for individuals with disabilities. Overcoming the limitations of traditional wheelchairs, this smart wheelchair integrates a tri-wheel mechanism, enabling smooth navigation across various terrains, including stairs, thus providing greater autonomy and flexibility. The wheelchair is equipped with two smart Internet of Things (IoT)-based subsystems for control and vital sign monitoring. Besides a joystick, the wheelchair features an electroencephalography (EEG)-based brain–computer interface (BCI) for hands-free control. Utilizing support vector machine (SVM) algorithms has proven effective in classifying EEG signals. This feature is especially beneficial for users with severe physical disabilities, allowing them to navigate more independently. In addition, the smart wheelchair has comprehensive health monitoring capabilities, continuously tracking vital signs such as heart rate, blood oxygen levels (SpO₂), and electrocardiogram (ECG) data. The system implements an SVM algorithm to recognize premature ventricular contractions (PVC) from ECG data. These metrics are transmitted to healthcare providers through a secure IoT platform, allowing for real-time monitoring and timely interventions. In the event of an emergency, the system is programmed to automatically send alerts, including the patient’s location, to caregivers and authorized relatives. This innovation is a step forward in developing assistive technologies that support independent living and proactive health management in smart cities.

The use of robots for machining operations has become very popular in the last few decades. However, the challenge of the robotic-based machining process, such as surface finishing on curved surfaces, still persists. Prior studies (non-contact- and contact-based) have their own limitations, such as fixture error and surface friction. To cope with these challenges, this study proposes an advanced technique for path correction and normal trajectory generation while tracking a curved workpiece’s surface. Initially, a key-point selection approach is used to estimate a reference workpiece’s coordinates using a depth measuring tool. This approach overcomes the fixture errors and enables the robot to track the desired path, i.e., where the surface normal trajectory is needed. Subsequently, this study employs an attached RGB-D camera on the end-effector of the robot for determining the depth and angle between the robot and the contact surface, which nullifies surface friction issues. The point cloud information of the contact surface is employed by the pose correction algorithm to guarantee the robot’s perpendicularity and constant contact with the surface. The efficiency of the proposed technique is analyzed by carrying out several experimental trials using a 6 DOF robot manipulator. The results reveal a better normal trajectory generation than previous state-of-the-art research, with an average angle and depth error of 1.8 degrees and 0.4 mm.