Novel Vine-like Continuum Robot for Environmental Exploration Applications

This thesis details a new design and novel operational strategies for nature inspired, thin tendril continuum robots. Instead of taking inspiration for robot design from insects or animals, the novel approach to continuum robotics herein takes inspiration and adapts operational concepts from plant life. In particular, an innovative strategy is developed which mimics behaviors observed in vines and other climbing plants. Specifically, a tendril robot with prickles was developed and deployed to actively seek environmental contact, exploiting the mechanical advantage gained by bracing against the environment using the prickles. The resulting performance enhancements over previously developed smooth backbone tendril robot designs, and use of strategies that do not attempt to interact with the environment are empirically demonstrated with the new robot prototype. Results of further experiments suggest applications in which the new design and approach could prove useful to the scientific and wider communities

Interactive Device that Performs Output Based On Human Movement and/or Human Emotion Detected via Machine Learning

Generally, the present disclosure is directed to a device capable of providing an output that is specific to and/or based on nearby human movement and/or human emotion detected by one or more machine-learned models

Object manipulation without hands

Funding: This work was supported by BBSRC Discovery Fellowship (BB/S01019X/1) to S.S.Our current understanding of manipulation is based on primate hands, resulting in a detailed but narrow perspective of ways to handle objects. Although most other animals lack hands, they are still capable of flexible manipulation of diverse objects, including food and nest materials, and depend on dexterity in object handling to survive and reproduce. Birds, for instance, use their bills and feet to forage and build nests, while insects carry food and construct nests with their mandibles and legs. Bird bills and insect mandibles are much simpler than a primate hand, resembling simple robotic grippers. A better understanding of manipulation in these and other species would provide a broader comparative perspective on the origins of dexterity. Here we contrast data from primates, birds and insects, describing how they sense and grasp objects, and the neural architectures that control manipulation. Finally, we outline techniques for collecting comparable manipulation data from animals with diverse morphologies and describe the practical applications of studying manipulation in a wide range of species, including providing inspiration for novel designs of robotic manipulators.PostprintPeer reviewe

Reconfigurable Soft Robots by Building Blocks

Soft robots are of increasing interest as they can cope with challenges that are poorly addressed by conventional rigid-body robots (e.g., limited flexibility). However, due to their flexible nature, the soft robots can be particularly prone to exploit modular designs for enhancing their reconfigurability, that is, a concept which, to date, has not been explored. Therefore, this paper presents a design of soft building blocks that can be disassembled and reconfigured to build different modular configurations of soft robots such as robotic fingers and continuum robots. First, a numerical model is developed for the constitutive building block allowing to understand their behavior versus design parameters, then a shape optimization algorithm is developed to permit the construction of different types of soft robots based on these soft building blocks. To validate the approach, 2D and 3D case studies of bio-inspired designs are demonstrated: first, soft fingers are introduced as a case study for grasping complex and delicate objects. Second, an elephant trunk is used for grasping a flower. Third, a walking legged robot. These case studies prove that the proposed modular building approach makes it easier to build and reconfigure different types of soft robots with multiple complex shapes

A comparative review of artificial muscles for microsystem applications

Artificial muscles are capable of generating actuation in microsystems with outstanding compliance. Recent years have witnessed a growing academic interest in artificial muscles and their application in many areas, such as soft robotics and biomedical devices. This paper aims to provide a comparative review of recent advances in artificial muscle based on various operating mechanisms. The advantages and limitations of each operating mechanism are analyzed and compared. According to the unique application requirements and electrical and mechanical properties of the muscle types, we suggest suitable artificial muscle mechanisms for specific microsystem applications. Finally, we discuss potential strategies for energy delivery, conversion, and storage to promote the energy autonomy of microrobotic systems at a system level

On the Use of Magnets to Robustify the Motion Control of Soft Hands

In this letter, we propose a physics-based framework to exploit magnets in robotic manipulation. More specifically, we suggest equipping soft and underactuated hands with magnetic elements, which can generate a magnetic actuation able to synergistically interact with tendon-driven and pneumatic actuations, engendering a complementarity that enriches the capabilities of the actuation system. Magnetic elements can act as additional Degrees of Actuation (DoAs), robustifying the motion control of the device and augmenting the hand manipulation capabilities. We investigate the interaction of a soft hand with itself for enriching possible hand shaping, and the interaction of the hand with the environment for enriching possible grasping capabilities. Physics laws and notions reported in the manuscript can be used as a guidance for DoAs augmentation and can provide tools for the design of novel soft hands

Efficient Jacobian-Based Inverse Kinematics With Sim-to-Real Transfer of Soft Robots by Learning

This paper presents an efficient learning-based method to solve the inverse kinematic (IK) problem on soft robots with highly non-linear deformation. The major challenge of efficiently computing IK for such robots is due to the lack of analytical formulation for either forward or inverse kinematics. To address this challenge, we employ neural networks to learn both the mapping function of forward kinematics and also the Jacobian of this function. As a result, Jacobian-based iteration can be applied to solve the IK problem. A sim-to-real training transfer strategy is conducted to make this approach more practical. We first generate a large number of samples in a simulation environment for learning both the kinematic and the Jacobian networks of a soft robot design. Thereafter, a sim-to-real layer of differentiable neurons is employed to map the results of simulation to the physical hardware, where this sim-to-real layer can be learned from a very limited number of training samples generated on the hardware. The effectiveness of our approach has been verified on pneumatic-driven soft robots for path following and interactive positioning

From rituals to magic: Interactive art and HCI of the past, present, and future

The connection between art and technology is much tighter than is commonly recognized. The emergence of aesthetic computing in the early 2000s has brought renewed focus on this relationship. In this article, we articulate how art and Human–Computer Interaction (HCI) are compatible with each other and actually essential to advance each other in this era, by briefly addressing interconnected components in both areas—interaction, creativity, embodiment, affect, and presence. After briefly introducing the history of interactive art, we discuss how art and HCI can contribute to one another by illustrating contemporary examples of art in immersive environments, robotic art, and machine intelligence in art. Then, we identify challenges and opportunities for collaborative efforts between art and HCI. Finally, we reiterate important implications and pose future directions. This article is intended as a catalyst to facilitate discussions on the mutual benefits of working together in the art and HCI communities. It also aims to provide artists and researchers in this domain with suggestions about where to go next

Dynamic Environmental Monitoring using Intelligent Tendril Robots

Traditional robots are constructed from rigid links which facilitate both stiffness and accuracy. However, these systems operate best in open, highly structured spaces, and environments traversable by this technology are inherently restricted to scales and geometries which match the size and shape of the links. Conversely, continuous backbone continuum robots have enormous potential for adaptive exploration of unstructured environments. However, to date there has been very little research on algorithms for learning and adapting to changes in environmental conditions with continuum robots. In this research, we introduce new results in learning policies for novel long, thin, continuously bending continuum tendril robots aimed toward applications such as remote inspection and sensor mobility for improved sample acquisition. The results could also have potential applica tions in defense and security, search and rescue in hazardous environmental conditions, and as an innovative option for sensor placement in environmental monitoring. Using a prototype continuum tendril robot previously developed at Clemson University, we demonstrate the new learning policy for the tendrils adaptive sensor placement and remote inspection within an environment seeded with numerous disparate and slowly (over a matter of hours) time-varying sources, and discuss the potential for use of such robot tendrils in environmental monitoring applications. The learning algorithm implemented in real-time is shown to help the tendril to adapt its sensor placement to changing environmental sources