Dr. Jiaming Fu

The versatility of robotic grippers enables them to handle various objects in diverse applications, primarily when operations need flexible handling of different materials and shapes. The research introduces a new grasping apparatus named Compliant Self-Adaptive Variable Stiffness Robotic Gripper (CS-VSRG) that unifies shape-conforming and adaptive compliance features to improve handling operations. Key to this innovation is the design of Self-Adaptive Variable Stiffness fingers which are constructed by the combination of three separate layers for high-stiffness outer and medium-stiffness middle and low-stiffness inner components. Each layer has been engineered to choose specific objects when gripping forces are applied. Fragile objects with a small grasping force are needed only to contact the low-stiffness layer. As the required grasping force increases, the deformation leads to the contact layer extending to the middle or outer layers through self-adaptive stiffness transition. This allows for adapting to various types of objects while maintaining a firm grasp. A comprehensive control strategy and system design are also developed and analyzed in this paper. Finite element analysis (FEA) simulations are also performed to validate the gripper. The stress distribution, deformation characteristics, and stiffness regulation of the layered structure are studied. A physical prototype is fabricated and an experimental grasping demonstration is performed to evaluate the actual performance. The results confirm that the gripper can dynamically adjust the stiffness of the finger layers involved in the grasping process, thereby enhancing both adaptability and robustness. Additionally, the proposed design is characterized by low cost, high reliability, and structural simplicity, making it well-suited for large-scale industrial applications that require cost-effective robotic operations. It can also be integrated as a component of a dexterous robotic hand.

The construction industry heavily uses many different categories of heavy equipment on a daily basis. Generally, such equipment is used extensively and lasts a long time with limited to no possibility of updating or enhancing. Especially, with the current advancements of technology, there is a need to modernize such mechanical equipment. With regard to equipment control, most equipment requires users to stay close to or directly control the equipment. In this paper, the authors propose an upgrading system that allows the remote operation of heavy equipment through push and pull mechanism. By utilizing a single-board computer such as Raspberry Pi with linear actuators, the system can allow the users to control the heavy equipment remotely. The proposed system is successfully demonstrated by upgrading an indoor tower crane in the D. Dorsey Moss Construction Lab at Purdue University. This type of upgrade could support other heavy equipment in the construction field through integrating various sensors and actuators for digital enhancement.

Stroke patients have a pressing demand for innovative hand rehabilitation devices with multiple finger joint positions. In this research, a novel soft variable stiffness actuator is proposed for alternative hand rehabilitation therapies. The design allows targeted stiffness modifications above specific joints, enabling a range of motions from a simple actuation source, supplemented by a smaller actuator determining internal stiffness. Four configurations were tested, adjusting stiffness regions within the actuator. Initial testing employed Finite Element Analysis simulations with hyper-elastic non-linear material settings, predicting actuator behavior under varying pressure. Comparison of simulation and experimental results, despite differing actuation pressure due to air leaks, revealed an acceptable replication of Finite Element Analysis simulations, with minor differences in tip trajectory. Validation involved testing a single prototype, laying the groundwork for a new form of variable stiffness actuator. The results indicate that the proposed soft variable stiffness actuator can achieve diverse hand rehabilitation guidance postures under the desired force, making it applicable to next-generation hand rehabilitation gloves and other devices.

Robotic grippers are receiving increasing attention in various industries as essential components of robots for interacting and manipulating objects. While significant progress has been made in the past, conventional rigid grippers still have limitations in handling irregular objects and can damage fragile objects. We have shown that soft grippers offer deformability to adapt to a variety of object shapes and maximize object protection. At the same time, dynamic vision sensors (e.g., event-based cameras) are capable of capturing small changes in brightness and streaming them asynchronously as events, unlike RGB cameras, which do not perform well in low-light and fast-moving environments. In this paper, a dynamic-vision-based algorithm is proposed to measure the force applied to the gripper. In particular, we first set up a DVXplorer Lite series event camera to capture twenty-five sets of event data. Second, motivated by the impressive performance of the Vision Transformer (ViT) algorithm in dense image prediction tasks, we propose a new approach that demonstrates the potential for force estimation and meets the requirements of real-world scenarios. We extensively evaluate the proposed algorithm on a wide range of scenarios and settings, and show that it consistently outperforms recent approaches.

Variable Stiffness Grippers (VSGs) represent a groundbreaking advancement in robotic manipulation, embodying the seamless integration of flexibility and rigidity to meet the multifaceted challenges of modern automation. These devices leverage the adaptability of compliant modes for handling a wide range of objects yet can switch to a rigid mode for tasks requiring high strength and precision. The management of variable stiffness poses significant challenges, especially in achieving precise control over the gripper's adaptability to objects of varying compliance. This paper proposes a method to provide a combination of position control and force control of a VSG by exploiting the dynamic model and the different stiffness levels. Our research examines active disturbance rejection control (ADRC) and deterministic robust control (DRC), demonstrating their advantages over PID in managing stiffness variations in robotic grippers. We highlight ADRC and DRC's enhanced robustness and adaptability through a comparative analysis, at different stiffness levels and grasping processes. These efforts highlight the importance of sophisticated control systems, in distinguishing between stiff and rigid modes effectively, enabling VSGs to handle objects ranging from fragile.

In Virtual Reality (VR), adversarial attack remains a significant se-curity threat. Most deep learning-based methods for physical and digital adversarial attacks focus on enhancing attack performance by crafting adversarial examples that contain large printable distortions that are easy for human observers to identify. However, attackers rarely impose limitations on the naturalness and comfort of the appearance of the generated attack image, resulting in a no-ticeable and unnatural attack. To address this challenge, we propose a framework to incorporate style transfer to craft adversarial inputs of natural styles that exhibit minimal detectability and maximum natural appearance, while maintaining superior attack capabilities.

Large deflection modeling is a crucial field of study in the analysis and design of compliant mechanisms (CM). This paper proposes a machine learning (ML) approach for predicting the deflection of discrete variable stiffness units (DSUs) that cover a range from small to large deflections. The primary structure of a DSU consists of a parallel guide beam with a hollow cavity that can change stiffness discretely by inserting or extracting a solid block. The principle is based on changing the cross-sectional area properties of the hollow section. Prior to model training, a large volume of data was collected using finite element analysis (FEA) under different loads and various dimensional parameters. Additionally, we present three widely used machine learning-based models for predicting beam deflection, taking into account prediction accuracy and speed. Several experiments are conducted to evaluate the performance of the ML models that were compared with the FEA and analytical model results. The optimal ML model, multilayer perceptron (MLP), can achieve a 7.9% maximum error compared to FEA. Furthermore, the model was employed in a practical application for inverse design, with various cases presented depending on the number of solved variables. This method provides a innovative perspective for studying the modeling of compliant mechanisms and may be extended to other mechanical mechanisms.

This paper presents the development of a novel Actuation-Coordinated Mobile Parallel Robot (ACMPR), with a focus on studying the kinematics of the mobile parallel robot with three limbs (3-mPRS) comprising mobile prismatic joint-revolute joint-spherical joint. The objective of this research is to explore the feasibility and potential of utilizing omnidirectional mobile robots to construct a parallel mechanism with a mobile platform. To this end, a prototype of the 3-mPRS is built, and several experiments are conducted to identify the proposed kinematic parameters. The system identification of the 3-mPRS mobile parallel mechanism is conducted by analyzing the actuation inputs from the three mobile base robots. To track the motion of the robot, external devices such as the Vicon Camera are employed, and the data is fed through ROS. The collected data is processed based on the geometric properties, CAD design, and established kinematic equations in MATLAB, and the results are analyzed to evaluate the accuracy and effectiveness of the proposed calibration methods. The experiment results fall within the error range of the proposed calibration methods, indicating the successful identification of the system parameters. The differences between the measured values and the calculated values are further utilized to calibrate the 3-mPRS to better suit the experiment environment.

Abstract
Collaborative robots, or cobots, have been developed as a solution to the growing need for robots that can work alongside humans safely and effectively. One emerging technology in robotics is the use of Discrete Variable Stiffness Actuators (DVSAs), which enable robots to adjust their stiffness in a fast-discrete manner. This enables cobots to work in both low and high stiffness modes, allowing for safe collaboration with human workers or operation behind safety barriers. However, achieving good performance with different stiffness modes of DVSAs is a challenging problem. This paper proposes a method to provide force control of a DVSA by exploiting the dynamic model and the discrete stiffness levels. The two-mass dynamic model, a widely accepted model of flexible systems, is used to model and analyze the DVSA. The proposed method involves using Gain-scheduling and Deterministic Robust Control (DRC) controllers as modelbased control algorithms for the DVSA to achieve high-precision force control. We also conducted a comparison with the commonly used proportional integral derivative (PID) control algorithms. The paper presents a detailed analysis of the dynamic behavior of the DVSA and demonstrates the effectiveness of the proposed control algorithms through simulation with different scenario comparisons, even in the presence of external disturbances.

Variable stiffness manipulators balance the trade-off between manipulation performance needing high stiffness and safe human-robot interaction desiring low stiffness. Variable stiffness compliant links provide a solution to enable this flexible manipulation function in human-robot co-working scenarios. In this paper, we propose a novel variable stiffness link based on discrete variable stiffness units (DSUs). A DSU is a parallel guided beam that can adjust stiffness discretely by changing the cross-sectional area properties of the hollow beam segments. The variable stiffness link (named Tri-DSU) consists of three tandem DSUs to achieve eight stiffness modes and a maximum stiffness change ratio of 31. To optimize the design, stiffness analysis of the DSU and Tri-DSU under various configurations and forces was performed by a derived theoretical model compared with finite element analysis (FEA). The analytical stiffness model is derived using the approach of serially connected beams and superposition combinations. It works not only for thin-walled flexure beams but also for general thick beam models. 3-D printed prototypes were built to verify the feature and performance of the Tri-DSU in comparison with the FEA and analytical model results. It's demonstrated that our analytical model can accurately predict the stiffnesses of the DSU and Tri-DSU within a certain range of parameters. The developed variable stiffness link method and analytical model are extendable to multiple DSUs with different sizes and parameter configurations to achieve modularization and customization. The advantages of the stiffness change mechanism are rapid actuation, simple structure, and compact layout. These methods and results provide a new conceptual and theoretical basis for the development of new reconfigurable cobot manipulators, variable stiffness structures, and compliant mechanisms.