A "Sidewinding" Locomotion Gait for Hyper-Redundant Robots

This paper considers the kinematics of a novel form of hyper-redundant mobile robot locomotion which is analogous to the 'sidewinding' locomotion of desert snakes. This form of locomotion can be generated by a repetitive travel wave of mechanism bending. Using a continuous backbone curve model, we develop algorithms which enable travel in a uniform direction as well as changes in direction

A Miniature Robot for Isolating and Tracking Neurons in Extracellular Cortical Recordings

This paper presents a miniature robot device and control algorithm that can autonomously position electrodes in cortical tissue for isolation and tracking of extracellular signals of individual neurons. Autonomous electrode positioning can significantly enhance the efficiency and quality of acute electrophysiolgical experiments aimed at basic understanding of the nervous system. Future miniaturized systems of this sort could also overcome some of the inherent difficulties in estabilishing long-lasting neural interfaces that are needed for practical realization of neural prostheses. The paper describes the robot's design and summarizes the overall structure of the control system that governs the electrode positioning process. We present a new sequential clustering algorithm that is key to improving our system's performance, and which may have other applications in robotics. Experimental results in macaque cortex demonstrate the validity of our approach

The Fractal Hand-II: Reviving a Classic Mechanism for Contemporary Grasping Challenges

This paper, and its companion, propose a new fractal robotic gripper, drawing inspiration from the century-old Fractal Vise. The unusual synergistic properties allow it to passively conform to diverse objects using only one actuator. Designed to be easily integrated with prevailing parallel jaw grippers, it alleviates the complexities tied to perception and grasp planning, especially when dealing with unpredictable object poses and geometries. We build on the foundational principles of the Fractal Vise to a broader class of gripping mechanisms, and also address the limitations that had led to its obscurity. Two Fractal Fingers, coupled by a closing actuator, can form an adaptive and synergistic Fractal Hand. We articulate a design methodology for low cost, easy to fabricate, large workspace, and compliant Fractal Fingers. The companion paper delves into the kinematics and grasping properties of a specific class of Fractal Fingers and Hands.Comment: This paper is prepared for ICRA 202

Cognitive based neural prosthetics

Intense activity in neural prosthetic research has recently demonstrated the possibility of robotic interfaces that respond directly to the nervous system. The question remains of how the flow of information between the patient and the prosthetic device should be designed to provide a safe, effective system that maximizes the patient’s access to the outside world. Much recent work by other investigators has focused on using decoded neural signals as low-level commands to directly control the trajectory of screen cursors or robotic end-effectors. Here we review results that show that high-level, or cognitive, signals can be decoded from planned arm movements. These results, coupled with fundamental limitations in signal recording technology, motivate an approach in which cognitive neural signals play a larger role in the neural interface. This proposed paradigm predicates that neural signals should be used to instruct external devices, rather than control their detailed movement. This approach will reduce the effort required of the patient and will take advantage of established and on-going robotics research in intelligent systems and human-robot interfaces

A miniature robot for autonomous single neuron recordings

This paper describes a novel miniature robot that can autonomously position recording electrodes inside cortical tissue to isolate and maintain optimal extracellular action potential recordings. The system consists of a novel motorized miniature recording microdrive and a control algorithm. The microdrive was designed for semi-chronic operation and can independently position four electrodes with micron precision over a 5mm range using small (3mm diameter) piezoelectric linear actuators. The autonomous positioning algorithm is designed to detect, align and cluster action potentials, and then command the microdrive to optimize and maintain the neural signal. This system is shown to be capable of autonomous operation in monkey cortex

Monolithic Silicon Probes with Flexible Parylene Cables for Neural Prostheses

This work presents the first parylene-insulated silicon probes, which are used for neural prostheses to record high-level cognitive neural signals. With parylene technology, our probes have several advantages compared with the current devices. First, instead of inorganic materials (e.g. silicon dioxide, silicon nitride), the electrodes and conduction traces on the probes are insulated by parylene, an easily-deposited polymer with mechanical flexibility and biocompatibility. As a result, the probes exhibit better electrical and mechanical properties. Second, flexible parylene cables are monolithically integrated with the probes, which arm the probes with very high flexibility to be easily assembled to a high density 3-D array and at the same time provide an ideal method to transmit neural signals through skull during chronic recording. The all dry fabrication process and a 4 X 4 probe array (64 electrodes) were demonstrated. The probes were successfully tested electrically and mechanically in rat cortex. Neural signals were properly recorded

The ISS as a Testbed for Future Large Astronomical Observatories: The OpTIIX Demonstration Program

Future large (diameters in excess of approx. 10 m) astronomical observatories in space will need to employ advanced technologies if they are to be affordable. Many of these technologies are ready to be validated on orbit and the International Space Station (ISS) provides a suitable platform for such demonstrations. These technologies include low-cost, low-density, highly deformable mirror segments, coupled with advanced sensing and control methods. In addition, the ISS offers available telerobotic assembly techniques to build an optical testbed that embodies this new cost-effective approach to assemble and achieve diffraction-limited optical performance for very large space telescopes. Given the importance that NASA attaches to the recommendations of the National Academy of Sciences "Decadal Survey" process, essential capabilities and technologies will be demonstrated well in advance of the next Survey, which commences in 2019. To achieve this objective, the Jet Propulsion Laboratory (JPL), NASA Johnson Space Center (JSC), NASA Goddard Space Flight Center (GSFC), and the Space Telescope Science Institute (STScI) are carrying out a Phase A/B study of the Optical Testbed and Integration on ISS eXperiment (OpTIIX). The overarching goal is to demonstrate well before the end of this decade key capabilities intended to enable very large optical systems in the decade of the 2020s. Such a demonstration will retire technical risk in the assembly, alignment, calibration, and operation of future space observatories. The OpTIIX system, as currently designed, is a six-hexagon element, segmented visual-wavelength telescope with an edge-to-edge aperture of 1.4 m, operating at its diffraction limit

A Learning-Based Framework for Safe Human-Robot Collaboration with Multiple Backup Control Barrier Functions

Ensuring robot safety in complex environments is a difficult task due to actuation limits, such as torque bounds. This paper presents a safety-critical control framework that leverages learning-based switching between multiple backup controllers to formally guarantee safety under bounded control inputs while satisfying driver intention. By leveraging backup controllers designed to uphold safety and input constraints, backup control barrier functions (BCBFs) construct implicitly defined control invariance sets via a feasible quadratic program (QP). However, BCBF performance largely depends on the design and conservativeness of the chosen backup controller, especially in our setting of human-driven vehicles in complex, e.g, off-road, conditions. While conservativeness can be reduced by using multiple backup controllers, determining when to switch is an open problem. Consequently, we develop a broadcast scheme that estimates driver intention and integrates BCBFs with multiple backup strategies for human-robot interaction. An LSTM classifier uses data inputs from the robot, human, and safety algorithms to continually choose a backup controller in real-time. We demonstrate our method's efficacy on a dual-track robot in obstacle avoidance scenarios. Our framework guarantees robot safety while adhering to driver intention