We are currently improving our website. Please feel free to contact us regarding any issues.
Artificial muscles, robotic grippers, health care robotics
UC San Diego robotics well represented at ICRA 2018 conference
San Diego, Calif., May 15, 2018 -- From a gripper equipped with gecko-inspired adhesives, to artificial muscles and robotic joints, to talks on human-robot interaction and health care robotics, the University of California San Diego will have a strong presence at the 2018 International Conference on Robotics and Automation, May 21 to 25 in Brisbane, Australia.
The event is the flagship conference of the IEEE Robotics and Automation Society and a premier international forum for robotics researchers to present their work. Established in 1984 and held annually, the conference joins experts in the field of robotics and automation for technical communications through presentations and discussions. Henrik Christensen, director of UC San Diego’s Contextual Robotics Institute, is the co-chair of the conference’s government forum.
“The ICRA conference is the premier venue for presentation of robotics research and it is important for an institute such as the Contextual Robotics Institute at UC San Diego to have a significant presence to promote our research, but also to promote the Institute for future recruiting of students, faculty and industry partners” said Henrik I Christensen, director of the Institute.
Christensen is also a speaker at the conference’s Industry Forum, and co-organizer of a workshop on Robot Teammates Operating in Dynamic, Unstructured Environments. In addition, Nikolai Atanasov, a professor in the UC San Diego Department of Electrical and Computer Engineering, is one of the organizers of a workshop on Perception, Inference, and Learning for Joint Semantic, Geometric, and Physical Understanding.
Laurel Riek, a professor in the UC San Diego Department of Computer Science and Engineering, will give talks at two workshops: New Horizons in Cognitive Robotics and AI and Elderly Care Robotics—Technology and Ethics.
Below is a list of UC San Diego research papers with abstracts, as well as abstracts for Riek’s talks and for the two workshops co-organized by Christensen and Atanasov.
A soft robotic gripper with gecko-inspired adhesive
Paul Glick, Srinivasan A. Suresh , Donald Ruffatto, Mark Cutkosky, Michael T. Tolley, and Aaron Parness
Press release: http://bit.ly/UCSDgeckogripper
Previous work has demonstrated the versatility of soft robotic grippers using simple control inputs. However, these grippers still face challenges in grasping large objects and in achieving high strength grasps. This work investigates the combination of fluidic elastomer actuators and gecko-inspired adhesives to both enhance existing soft gripper properties and generate new capabilities. On rocky or dirty surfaces where adhesion is limited, the gripper retains the functionality of a pneumatically actuated elastomer gripper with no measured loss in performance. Design strategies for using the unique properties of the gecko-inspired adhesives are presented. By modeling fluidic elastomer actuators as a series of joints with associated joint torques, we designed an actuator that takes advantage of the unique properties of the gecko-inspired adhesive. Experiments showed higher strength grasps at lower pressures compared to non-gecko actuators, in many cases enabling the gripper to actuate more quickly and use less energy. The gripper weighs 48.7 g, uses $7.25 of raw materials and can support loads of over 50 N. A second gripper, using three fingers for a larger adhesive surface, demonstrated a grasping force of 111 N (25 lbf) when actuated at an internal pressure of 40 kPa.
"Bundled Super-Coiled Polymer Artificial Muscles: Design, Characterization, and Modeling"
A. Simeonov, T. Henderson, Z. Lan, G. Sundar, A. Factor, J. Zhang and M. C. Yip
Super-coiled polymer (SCP) artificial muscles have many attractive properties such as high energy density, large contractions, and good dynamic range. To fully utilize them for robotic applications, it is necessary to determine how to scale them up effectively. Bundling of SCP actuators, as though they are individual threads in woven textiles, can demonstrate the versatility of SCP actuators and artificial muscles in general. However, this versatility comes with a need to understand how different bundling techniques can be achieved with these actuators and how they may trade off in performance. This paper presents the first quantitative comparison, analysis, and modeling of bundled SCP actuators. By exploiting weaving and braiding techniques, three new types of bundled SCP actuators are created: woven bundles, two-dimensional (2D) and three-dimensional (3D) braided bundles. The bundle performance is adjustable by employing different numbers of individual actuators. Experiments are conducted to characterize and compare the force, strain, and speed of different bundles, and a linear model is proposed to predict their performance. This work lays the foundation for model-based SCP-actuated textiles, and physically scaling robots that employ SCP actuators as the driving mechanism.
Vision-based Force Feedback Estimation for Robot-assisted Surgery using Instrument-constrained Biomechanical 3D Maps.
Nazim Haochine, Winnie Kuang, Stephane Cotin, and Michael Yip
Current robotic surgical systems do not provide significant force feedback for the operator. This paper presents a method for estimating visual and haptic force feedback by using a biomechanical model which is built on-the-fly with 3D reconstruction and meshing techniques from endoscopic images of the organ shape. Contact forces can be estimated at the tip of the surgical tool by considering the tool as boundary conditions acting on the surface of the model. These contact forces also generate stresses onto the environment and the 3D reconstructed model which can be used as visual force feedback for the surgeon. The proposal is demonstrated on in-vivo sequences of a human liver during robotic surgery, and validated using a Deja Vu simulation and ex-vivo experimentation.
A Tensegrity-Inspired Compliant 3-DOF Compliant Joint
Jeffrey M. Friesen, John L. Dean, Thomas Bewley, Vytas Sunspiral
Our Tensegrity-Inspired Compliant Three degree of-freedom (DOF) robotic joint adds omnidirectional compliance to robotic limbs while reducing sprung mass through base mounted actuation. This enables a robotic limb which is safer to operate alongside humans and fragile equipment while still capable of generating quick movements and large forces if required. Unlike many other soft robotic systems which leverage continuously soft materials, our joint is simpler to model with low order dynamic systems and has a host of embedded sensing which provide ample information of its position and velocity. We first discuss geometry selection and optimization to maximize the theoretical configuration space of the joint. We then show several of our mechatronic design solutions, which are easily generalized to a multitude of cable-driven mechanisms, and demonstrate the performance of these mechanisms within the context of our hardware prototype. We then present results on the controllable stiffness of our physical prototype. Finally, we demonstrate the strength of our prototype which is capable of lifting a 7 kg mass at a distance of 0.95 meters from the joint.
Healthcare Robotics: Supporting Older Adults, Caregivers, and the Clinical Workforce: Laurel Riek
Robots are now entering our daily lives - in the home, on the road, in offices, and in hospitals. To operate proximately with people, robots need the ability to dynamically and quickly interpret human activities, understand context, and take appropriate (and safe) actions. They also need to learn from and adapt to people long term. My research focuses on building robots that autonomously solve problems in human environments, particularly those that are safety critical (e.g., hospitals, homes, and factories). Recent contributions include new methods to model stochastic environments and circumvent sensor noise and occlusion, new techniques to enable robots to robustly solve problems under limited computational resources, and methods for robots to perceive and learn from people long term. Our primary application focus is healthcare, and recent projects include supporting older adults in home environments, including people with cognitive impairments, as well as new ways to use robots to support caregivers and the clinical workforce. This talk will describe several recent projects in this space.
Long Term Robot Learning: Modeling and Adaptability: Laurel Riek
In order to build robots that can work longitudinally alongside people, it is important they are adaptive to people and their environments, and personalize their behavior on the fly. This requires a high degree of personalized, long-term preference learning, which incorporates a dynamic understanding of human context, activities, and goals. This talk will discuss our recent work in this area, within the novel application space of robot-assisted occupational therapy.
Robotic systems are beginning to conquer well- to partially-structured environments, such as human-challenging games and self-driving cars operating on well-known streets, but genuinely unstructured and dynamic environments still exceed the capabilities of robotic systems, mainly when robots must also operate with or around humans. This unsolved problem of teams of robots that are capable of adapting to real, unstructured, dynamic environments in real time alongside humans is of high importance to robotics research today, with significant applications for military, search and rescue, and other robot uses in the real world.
To illustrate, imagine a human-robot team operating in an environment that is entirely unknown, such as under a dense tree canopy, at a disaster site, underground, or underwater, where limited or no resources (GPS, comms, power) are available. Compound the difficulty with dynamic events that alter the environment or robotic perception of the environment, such as weather and environmental effects or the actions of other agents.
Successful operation of a robot or group of robots in these most difficult environments requires solutions at the intersection of several of the most challenging areas in modern field robotics: the ability for robots to 1) perceive, reason, and act in dynamic, unstructured environments 2) alongside human teammates and 3) at human-operational speeds.
This workshop will bring together researchers in areas of perception, cognition, planning, control, estimation, machine learning, multi-robot systems, and human-robot interaction to discuss the challenges of robots operating in these contexts, and current and future solutions. Along with this expertise in the solution spaces for this challenge area nexus, this workshop will also feature experts in defining and understanding the real-world problem space from government and industry groups. This combination will be influential in shaping an active conversation and understanding of the obstacles and solutions to building successful robot teammates.
The goal of this workshop is to bring together researchers from robotics, computer vision, machine learning, and neuroscience to examine the challenges and opportunities emerging from the design of *environment representations and perception algorithms* that unify semantics, geometry, and physics. This goal is motivated by two fundamental observations. First, the development of advanced perception and world understanding is a key requirement for robot autonomy in complex, unstructured environments, and an enabling technology for robot use in transportation, agriculture, mining, construction, security, surveillance, and environmental monitoring. Second, despite the unprecedented progress over the past two decades, there is still a large gap between robot and human perception (e.g., expressiveness of representations, robustness, latency). *The workshop aims to bring forward the latest breakthroughs and cutting-edge research on multimodal representations, as well as novel perception, inference, and learning algorithms that can generate such representations.*
Jacobs School of Engineering