###### Abstract  
This study introduces a novel calibration method for accurate external wrench measurement using a six-axis FT (force-torque) sensor. We propose a sensor model and calibration method for FT sensors that enable precise separation of the force and torque components without the need for additional devices or sensors by estimating essential parameters: bias, crosstalk, CoM (center of mass), and inclination. By directly utilizing manufacturer-provided data, our approach eliminates the complexities of traditional calibration processes while achieving higher accuracy in force-torque measurements. This method simplifies the calibration workflow and enhances the practicality of FT sensor applications. A mobile manipulator installed with an FT sensor and a gripper is used to demonstrate calibration effectiveness across varying postures and incline conditions, with non-linear optimization based on the gradient descent method applied to minimize sensor-data errors. The tilt of the base is implemented by placing a step under the wheels of the mobile base to simulate roll or pitch scenarios. A digital level was used to measure the angle and verify that our predicted results were accurate. The proposed method addresses typical calibration challenges, including the effects of the end tool and base incline, which are not commonly covered in existing methods. The results show that, on a non-inclined base, crosstalk and CoM calibration reduces the MSE (mean squared error) by 55.8%, 56.2%, and 14.5% for the external force with respect to data without any calibration conducted. On an inclined base, our full calibration process reduces the MSE by a maximum of 98.6% for external mass measurement with respect to no calibration method applied. These findings highlight the importance of incline calibration for achieving accurate external force estimations, especially in mobile manipulator applications where the environment frequently changes. c 2025151510 torque sensors or measure the current flowing through joint modules to estimate and control the forces acting on the robot's end-effector or links. Control strategies that leverage such force and torque feedback include admittance and impedance control [1; 2]. However, these methods require an accurate analysis of the manipulator's model, including the inertia matrix, Coriolis forces, and friction, and they face the challenge of being heavily dependent on these parameters [2; 3]. In particular, errors in friction compensation can lead to performance deviations of up to 25%. Moreover, these methods are complex to implement and require sensors on every link. In general, when a robot is used to perform tasks, the task environment needs to be simplified. Therefore, using an FT sensor to measure forces at the robot's end-effector simplifies the calculation of the required movements for the end-effector. Although this approach requires adding an FT sensor to the robot, it has the advantage of providing force measurements at the end-effector, even in robots without joint torque sensors. To obtain the forces acting on the end-effector, an FT sensor is usually installed at the robot's end-effector, and the end tool is attached after the FT sensor. The external force should be isolated from the FT sensor data. External force refers to the forces and torques acting on the end-effector itself, excluding any effects from the tool attached beyond the sensor. FT sensor calibration is necessary because the tool attached to the end (typically a gripper) has its own weight and inertia, causing the sensor to measure force and torque even when no external forces or torques are applied. For accurate calibration, it is essential to estimate the wrench caused by the tool and separate the external wrench from the sensor's characteristic crosstalk through appropriate compensation. Calibration using FT sensors is still an emerging research area. This is because the users typically use the data provided by the sensor as is.