Hybrid Rigid–Soft Industrial Gripper: Actuation, Design Enhancement, Multi-Modal Sensorization, and Real-Time Coordinated Control for Automotive Assembly

The growing complexity of industrial automation and the shift toward human–robot collaborative manufacturing demand robotic grippers that combine mechanical precision, adaptive compliance, and intelligent sensing. This doctoral thesis addresses these needs through comprehensive research on actuation mechanisms, kinematic analysis, design enhancement, advanced sensorization, and industrial implementation of a versatile universal gripper system capable of handling components from delicate items to complex rigid parts. In parallel, it contributes to industrial robot control within the EU Horizon SESTOSENSO project. The work begins with a detailed kinematic analysis of a novel three-finger gripper architecture. Each rigid mechanical finger integrates a Chebyshev–parallelogram linkage mechanism with a thermoplastic polyurethane (TPU) contact interface. The mechanism produces near-linear trajectories with a deviation of ±0.033 mm and a mechanical advantage of 6.06:1. Independent actuation is achieved using JVL stepper motors with embedded programmable logic controllers (PLCs), communicating via Modbus remote terminal unit (RTU). The control architecture supports torque-based and velocity-based stall detection, both operating in real time with configurable thresholds. These strategies enable reliable grasping without dedicated force sensors by leveraging internal motor feedback parameters. Gripper enhancement is guided by a quantitative deflection coefficient to assess finger wrapping and by quasi-static force–displacement testing. The original four-bar parallelogram was redesigned into a six-bar linkage with compliant pads. This eliminates link interference limitations while preserving the essential kinematics, resulting in adaptive grasping capability. Pull-out tests demonstrated improved force profiles for complex automotive parts and reliable manipulation of objects from 100 g to 7.5 kg. Vision-based sensorization was achieved through an embedded Raspberry Pi Camera V3. The integration of vision-based sensing within the additively manufactured soft finger structure establishes the feasibility of achieving multiple sensing modalities with a single compact embedded system while retaining the characteristic properties of the fingers. The proposed system successfully estimates normal interaction forces, measures internal deformation (Z-displacement), classifies the position of the applied force, and detects slip events with the complete sensing pipeline processed on an embedded platform while avoiding complex signal disambiguation challenges and occlusion issues. Complementing this, a fully flexible resistive sensor was fabricated via fused deposition modeling (FDM) printing and embedded in the finger for contact and bending detection. A novel light-angle sensor array was also developed using a custom four-layer rigid-flex printed circuit board (PCB), where prototype sensors successfully demonstrate distributed tactile sensing capabilities. The universal gripper and sensing systems were validated on a COMAU six-degrees-of-freedom (6-DOF) industrial robot in diverse grasping trials, confirming adaptability, robustness, and sensing reliability. Separately, within the SESTOSENSO project, real-time control strategies were developed for coordinating a KUKA KR150 robot with a UR10 cobot via robot sensor interface (RSI) and robot operating system (ROS) in an automotive roof assembly task. This work addressed control architecture, real-time trajectory correction, and safe human–robot collaboration in confined, visually occluded environments. This thesis advances the state of the art in hybrid gripper systems by integrating rigid precision, soft adaptability, and intelligent sensing with industrially validated control strategies. The outcomes directly support Industry 4.0/5.0 objectives, enabling flexible, high-performance automation adaptable to diverse manufacturing requirements.