Exoskeleton-covered soft finger with vision-based proprioception and tactile sensing

Soft robots offer significant advantages in adaptability, safety, and dexterity compared to conventional rigid-body robots. However, it is challenging to equip soft robots with accurate proprioception and tactile sensing due to their high flexibility and elasticity. In this work, we describe the development of a vision-based proprioceptive and tactile sensor for soft robots called GelFlex, which is inspired by previous GelSight sensing techniques. More specifically, we develop a novel exoskeleton-covered soft finger with embedded cameras and deep learning methods that enable high-resolution proprioceptive sensing and rich tactile sensing. To do so, we design features along the axial direction of the finger, which enable high-resolution proprioceptive sensing, and incorporate a reflective ink coating on the surface of the finger to enable rich tactile sensing. We design a highly underactuated exoskeleton with a tendon-driven mechanism to actuate the finger. Finally, we assemble 2 of the fingers together to form a robotic gripper and successfully perform a bar stock classification task, which requires both shape and tactile information. We train neural networks for proprioception and shape (box versus cylinder) classification using data from the embedded sensors. The proprioception CNN had over 99\% accuracy on our testing set (all six joint angles were within 1 degree of error) and had an average accumulative distance error of 0.77 mm during live testing, which is better than human finger proprioception. These proposed techniques offer soft robots the high-level ability to simultaneously perceive their proprioceptive state and peripheral environment, providing potential solutions for soft robots to solve everyday manipulation tasks. We believe the methods developed in this work can be widely applied to different designs and applications.Comment: Accepted to ICRA202

Scalable Tactile Sensing for an Omni-adaptive Soft Robot Finger

Robotic fingers made of soft material and compliant structures usually lead to superior adaptation when interacting with the unstructured physical environment. In this paper, we present an embedded sensing solution using optical fibers for an omni-adaptive soft robotic finger with exceptional adaptation in all directions. In particular, we managed to insert a pair of optical fibers inside the finger's structural cavity without interfering with its adaptive performance. The resultant integration is scalable as a versatile, low-cost, and moisture-proof solution for physically safe human-robot interaction. In addition, we experimented with our finger design for an object sorting task and identified sectional diameters of 94\% objects within the $\pm$6mm error and measured 80\% of the structural strains within $\pm$0.1mm/mm error. The proposed sensor design opens many doors in future applications of soft robotics for scalable and adaptive physical interactions in the unstructured environment.Comment: 8 pages, 6 figures, full-length version of a submission to IEEE RoboSoft 202

Challenges of continuum robots in clinical context: a review

With the maturity of surgical robotic systems based on traditional rigid-link principles, the rate of progress slowed as limits of size and controllable degrees of freedom were reached. Continuum robots came with the potential to deliver a step change in the next generation of medical devices, by providing better access, safer interactions and making new procedures possible. Over the last few years, several continuum robotic systems have been launched commercially and have been increasingly adopted in hospitals. Despite the clear progress achieved, continuum robots still suffer from design complexity hindering their dexterity and scalability. Recent advances in actuation methods have looked to address this issue, offering alternatives to commonly employed approaches. Additionally, continuum structures introduce significant complexity in modelling, sensing, control and fabrication; topics which are of particular focus in the robotics community. It is, therefore, the aim of the presented work to highlight the pertinent areas of active research and to discuss the challenges to be addressed before the potential of continuum robots as medical devices may be fully realised

Commande dynamique de robots déformables basée sur un modèle numérique

This work focuses on modeling and control of soft robots. It covers the entire development of the controller, from the modeling step to the practical experimental validation.From a theoretical point a view, large-scale dynamical systems along with model reduction algorithms are studied. In addition to the theoretical studies, different experimental setups are used to illustrate the results. A cable-driven soft robot and a pressurized soft arm are used to test the control algorithms. Through these different setups, we show that the method can handle different types of actuation, different geometries and mechanical properties. This emphasizes one of the interests of the method, its genericity.Cette thèse s'intéresse à la modélisation et à la commande de robots déformables (robots dont le mouvement se fait par déformation). Nous nous intéressons à la conception de lois de contrôle en boucle fermée répondant aux besoins spécifiques du contrôle dynamique de ces robots, sans restrictions fortes sur leur géométrie. La résolution de ce défi soulève des questions théoriques qui nous amènent au deuxième objectif de cette thèse: développer de nouvelles stratégies pour étudier les systèmes de grandes dimensions

Snake and Snake Robot Locomotion in Complex, 3-D Terrain

Snakes are able to traverse almost all types of environments by bending their elongate bodies in three dimensions to interact with the terrain. Similarly, a snake robot is a promising platform to perform critical tasks in various environments. Understanding how 3-D body bending effectively interacts with the terrain for propulsion and stability can not only inform how snakes move through natural environments, but also inspire snake robots to achieve similar performance to facilitate humans. How snakes and snake robots move on flat surfaces has been understood relatively well in previous studies. However, such ideal terrain is rare in natural environments and little was understood about how to generate propulsion and maintain stability when large height variations occur, except for some qualitative descriptions of arboreal snake locomotion and a few robots using geometric planning. To bridge this knowledge gap, in this dissertation research we integrated animal experiments and robotic studies in three representative environments: a large smooth step, an uneven arena of blocks of large height variation, and large bumps. We discovered that vertical body bending induces stability challenges but can generate large propulsion. When traversing a large smooth step, a snake robot is challenged by roll instability that increases with larger vertical body bending because of a higher center of mass. The instability can be reduced by body compliance that statistically increases surface contact. Despite the stability challenge, vertical body bending can potentially allow snakes to push against terrain for propulsion similar to lateral body bending, as demonstrated by corn snakes traversing an uneven arena. This ability to generate large propulsion was confirmed on a robot if body-terrain contact is well maintained. Contact feedback control can help the strategy accommodate perturbations such as novel terrain geometry or excessive external forces by helping the body regain lost contact. Our findings provide insights into how snakes and snake robots can use vertical body bending for efficient and versatile traversal of the three-dimensional world while maintaining stability

Soft robotic fingers with embedded ionogel sensors and discrete actuation modes for somatosensitive manipulation

Soft robotic grippers enable gentle, adaptive, and bioinspired manipulation that is simply not possible using traditional rigid robots. However, it has remained challenging to create multi-degree-of-freedom soft actuators with appropriate sensory capabilities for soft manipulators requiring greater dexterity and closed-loop control. In this work, we use embedded 3D printing to produce soft robotic fingers with discrete actuation modes and integrated ionogel soft sensors that provide proprioceptive and tactile sensing corresponding to each degree of freedom. With new readout electronics that streamline the measurement of sensor resistance, we evaluate the fingers' sensory feedback through free and blocked displacement experiments. We integrate three of our sensorized fingers together to create a soft manipulator with different grasping poses. Finally, we showcase the importance of the fingers' discrete actuation modes and integrated sensors via a closed-loop grasping study. Our methods demonstrate an enabling manufacturing platform that can be adapted to create other soft multi-DOF manipulators requiring somatosensory feedback for a variety of closed-loop and machine learning-based control algorithms.NSF EFRI (1830901