Functional Soft Robotic Actuators Based on Dielectric Elastomers

Dielectric elastomer actuators (DEAs) are a promising soft actuator technology for robotics. Adding robotic functionalities--folding, variable stiffness, and adhesion--into their actuator design is a novel method to create functionalized robots with simplified actuator configurations. We first propose a foldable actuator that has a simple antagonistic DEA configuration allowing bidirectional actuation and passive folding. To prove the concept, a foldable elevon actuator with outline size of 70 mm × 130 mm is developed with a performance specification matched to a 400 mm wingspan micro air vehicle (MAV) of mass 130 g. The developed actuator exhibits actuation angles up to ± 26 ° and a torque of 2720 mN·mm in good agreement with a prediction model. During a flight, two of these integrated elevon actuators well controlled the MAV, as proven by a strong correlation of 0.7 between the control signal and the MAV motion. We next propose a variable stiffness actuator consisting of a pre-stretched DEA bonded on a low-melting-point alloy (LMPA) embedded silicone substrate. The phase of the LMPA changes between liquid and solid enabling variable stiffness of the structure, between soft and rigid states, while the DEA generates a bending actuation. A proof-of-concept actuator with dimension 40 mm length × 10mm width × 1mm thickness and a mass of 1 g is fabricated and characterized. Actuation is observed up to 47.5 ° angle and yielding up to 2.4 mN of force in the soft state. The stiffness in the rigid state is ~90 × larger than an actuator without LMPA. We develop a two-finger gripper in which the actuators act as the fingers. The rigid state allows picking up an object mass of 11 g (108 mN), to be picked up even though the actuated grasping force is only 2.4 mN. We finally propose an electroadhesion actuator that has a DEA design simultaneously maximizing electroadhesion and electrostatic actuation, while allowing self-sensing by employing an interdigitated electrode geometry. The concept is validated through development of a two-finger soft gripper, and experimental samples are characterized to address an optimal design. We observe that the proposed DEA design generates 10 × larger electroadhesion force compared to a conventional DEA design, equating to a gripper with a high holding force (3.5 N shear force for 1 cm^2) yet a low grasping force (1 mN). These features make the developed simple gripper to handle a wide range of challenging objects such as highly-deformable water balloons (35.6 g), flat paper (0.8 g), and a raw chicken egg (60.9 g), with its lightweight (1.5 g) and fast movement (100 ms to close fingers). The results in this thesis address the creation of the functionalized robots and expanding the use of DEAs in robotics

Robotic perception and manipulation of garments

This thesis introduces an effective robotic garment flattening pipeline and robotic perception paradigms for predicting garments’ geometric (shape) and physics properties. Robotic garment manipulation is a popular and challenging task in robotic research. Due to the high dimensionality of garments, object states of garments are infinite. Also, garments deform irregularly during manipulations, which makes predicting their deformations difficult. However, robotic garment manipulation is an essential topic in robotic research. Robotic laundry and household sorting play a vital role in an ageing society, and automated manufacturing requires robots to be able to grasp different mechanical components, some of which are deformable objects. Also, robot-aided garment dressing is essential for the community with disabilities. Therefore, designing and implementing effective robotic garment manipulation pipelines are necessary but challenging. This thesis mainly focuses on designing an effective robotic garment flattening pipeline. Therefore, this thesis is divided into two main parts: robotic perception and robotic manipulation. Below is a summary of the research in this PhD thesis: • Robotic perception provides prior knowledge on garment attributes (geometrical (shape) and physics properties) that facilitates robotic garment flattening. Continuous perception paradigms are introduced for predicting shapes and visually perceived garments weights. • A reality-simulation knowledge transferring paradigm for predicting the physics properties of real garments and fabrics has been proposed in this thesis. • The second part of this thesis is robotic manipulation. This thesis suggests learning the known configurations of garments with prior knowledge of garments’ geometric (shape) properties and selecting pre-designed manipulation strategies to flatten garments. The robotic manipulation part takes advantage of the geometric (shape) properties learned from the robotic perception part to recognise the known configurations of garments, demonstrating the importance of robotic perception in robotic manipulation. The experiment results of this thesis revealed that: 1). A robot gains confidence in prediction (shapes and visually perceived weights of unseen garments) from continuously perceiving video frames of unseen garments being grasped, where high accuracies on predictions (93% for shapes and 98.5 % for visually perceived weights) are obtained; 2). Predicting the physics properties of real garments and fabrics can be realised by learning physics similarities between simulated fabrics. The approach in this thesis outperforms SOTA (34 % improvement on real fabrics and 68.1 % improvement for real garments); 3). Compared with state-of-the-art robotic garment flattening, this thesis enables the flattening of garments of various shapes (five shapes) and fast and effective manipulations. Therefore, this thesis advanced SOTA of robotic perception and manipulation (flattening) of garments

Automation and Robotics: Latest Achievements, Challenges and Prospects

This SI presents the latest achievements, challenges and prospects for drives, actuators, sensors, controls and robot navigation with reverse validation and applications in the field of industrial automation and robotics. Automation, supported by robotics, can effectively speed up and improve production. The industrialization of complex mechatronic components, especially robots, requires a large number of special processes already in the pre-production stage provided by modelling and simulation. This area of research from the very beginning includes drives, process technology, actuators, sensors, control systems and all connections in mechatronic systems. Automation and robotics form broad-spectrum areas of research, which are tightly interconnected. To reduce costs in the pre-production stage and to reduce production preparation time, it is necessary to solve complex tasks in the form of simulation with the use of standard software products and new technologies that allow, for example, machine vision and other imaging tools to examine new physical contexts, dependencies and connections