# Design and Calibration of a Sensory System of an Adaptive Gripper
## 5 Future Works  
Tests to optimize the gripper's operation will continue with parts of other shapes and sizes, with the gripper being mounted on a Cartesian robot that will be integrated into a robotic assembly line of subassemblies used in the automotive industry. Future improvements to the sensor system, with a view to further implementing machine learning (AI) functionalities, focus on the following:  
Advanced sensor integration: Higher-resolution and -precision force sensors: Replacing existing force sensors with higher-resolution and improved-precision sensors, reducing measurement noise and increasing the accuracy of force detection. Sensors with integrated thermal compensation will improve reliability in various environmental conditions. Higher-resolution incremental optical encoders: Using higher-resolution encoders to improve the accuracy of measuring probe movement, reducing slip detection errors. Speed sensors:Adding speed sensors to measure the slip rate, improving the accuracy of control algorithms and allowing faster reaction to slip. Proximity sensors: Integrating proximity sensors to detect the approach of the object before contact, allowing for more precise gripping and avoiding collisions. Computer vision: Integrating a camera to obtain visual information about the shape and orientation of the object. These data can be used by machine learning algorithms for a more precise and robust grip. * Improving data processing algorithms: Implementing more sophisticated digital filtering techniques (e.g., Kalman filters, weighted moving average filters) to reduce noise in sensory data and improve measurement accuracy; combining data from different sensors (force, slip, computer vision) to obtain a more complete picture of the system state and improve the accuracy and reliability of slip detection. * Implementing machine learning (AI) algorithms: Training a machine learning model by imitating the movements of a human operator manipulating objects. The model will learn to adapt the gripping force and movements depending on the shape and texture of the object; Using reinforcement learning techniques to optimize the parameters of the control algorithm and improve the performance of the gripper in various situations. The algorithm will learn to adjust the gripping force and movements based on feedback from the environment; Using shape classification techniques to automatically identify the shape of the object and adapt the gripping strategy. Computer vision is essential for this functionality; Training a machine learning model to predict slippage before it occurs, allowing for a preventive reaction. The operation of the adaptive gripper in high-speed industrial applications requires specific optimizations to ensure robustness and reliability by adapting to high speeds with appropriate solutions. The following are subjects of future research: the use of force and slip sensors with a very short response time; the PID algorithm, optimized for high processing speeds; the replacement of the stepper motor with a faster drive system, such as a servo motor with precise position and speed control; the inclusion of robust error management, covering the detection of communication errors, sensor failures, and other unexpected events; and the implementation of redundant mechanisms to ensure system operation even in the event of failure of one or more modules. Conceptualization, C.F., I.S. ([PERSON]) and I.S. ([PERSON]); Methodology, C.F., I.S. ([PERSON]) and I.S. ([PERSON]); Software, C.F., I.S. ([PERSON]) and I.S. ([PERSON]); Validation, C.F., I.S. ([PERSON]), S.V. and I.S. ([PERSON]); Formal analysis, C.F., I.S. ([PERSON]), S.V. and I.S. ([PERSON]); Investigation, C.F., I.S. ([PERSON]) and I.S. ([PERSON]); Resources, I.S. ([PERSON]) and I.S. ([PERSON]); Data curation, C.F., I.S. ([PERSON]) and I.S. ([PERSON]); Writing - original draft, C.F., I.S. ([PERSON]) and I.S. ([PERSON]); Writing - review & editing, S.V. and I.S. ([PERSON]); Visualization, C.F., S.V. and I.S. ([PERSON]); Supervision, C.F., S.V. and I.S.