An Adaptive Tool-Based Telerobot Control System

Modern telerobotics concepts seek to improve the work efficiency and quality of remote operations. The unstructured nature of typical remote operational environments makes autonomous operation of telerobotic systems difficult to achieve. Thus, human operators must always remain in the control loop for safety reasons. Remote operations involve tooling interactions with task environment. These interactions can be strong enough to promote unstable operation sometimes leading to system failures. Interestingly, manipulator/tooling dynamic interactions have not been studied in detail. This dissertation introduces a human-machine cooperative telerobotic (HMCTR) system architecture that has the ability to incorporate tooling interaction control and other computer assistance functions into the overall control system. A universal tooling interaction force prediction model has been created and implemented using grey system theory. Finally, a grey prediction force/position parallel fuzzy controller has been developed that compensates for the tooling interaction forces. Detailed experiments using a full-scale telerobotics testbed indicate: (i) the feasibility of the developed methodologies, and (ii) dramatic improvements in the stability of manipulator – based on band saw cutting operations. These results are foundational toward the further enhancement and development of telerobot

Learning Algorithm Design for Human-Robot Skill Transfer

In this research, we develop an intelligent learning scheme for performing human-robot skills transfer. Techniques adopted in the scheme include the Dynamic Movement Prim- itive (DMP) method with Dynamic Time Warping (DTW), Gaussian Mixture Model (G- MM) with Gaussian Mixture Regression (GMR) and the Radical Basis Function Neural Networks (RBFNNs). A series of experiments are conducted on a Baxter robot, a NAO robot and a KUKA iiwa robot to verify the effectiveness of the proposed design.During the design of the intelligent learning scheme, an online tracking system is de- veloped to control the arm and head movement of the NAO robot using a Kinect sensor. The NAO robot is a humanoid robot with 5 degrees of freedom (DOF) for each arm. The joint motions of the operator’s head and arm are captured by a Kinect V2 sensor, and this information is then transferred into the workspace via the forward and inverse kinematics. In addition, to improve the tracking performance, a Kalman filter is further employed to fuse motion signals from the operator sensed by the Kinect V2 sensor and a pair of MYO armbands, so as to teleoperate the Baxter robot. In this regard, a new strategy is developed using the vector approach to accomplish a specific motion capture task. For instance, the arm motion of the operator is captured by a Kinect sensor and programmed through a processing software. Two MYO armbands with embedded inertial measurement units are worn by the operator to aid the robots in detecting and replicating the operator’s arm movements. For this purpose, the armbands help to recognize and calculate the precise velocity of motion of the operator’s arm. Additionally, a neural network based adaptive controller is designed and implemented on the Baxter robot to illustrate the validation forthe teleoperation of the Baxter robot.Subsequently, an enhanced teaching interface has been developed for the robot using DMP and GMR. Motion signals are collected from a human demonstrator via the Kinect v2 sensor, and the data is sent to a remote PC for teleoperating the Baxter robot. At this stage, the DMP is utilized to model and generalize the movements. In order to learn from multiple demonstrations, DTW is used for the preprocessing of the data recorded on the robot platform, and GMM is employed for the evaluation of DMP to generate multiple patterns after the completion of the teaching process. Next, we apply the GMR algorithm to generate a synthesized trajectory to minimize position errors in the three dimensional (3D) space. This approach has been tested by performing tasks on a KUKA iiwa and a Baxter robot, respectively.Finally, an optimized DMP is added to the teaching interface. A character recombination technology based on DMP segmentation that uses verbal command has also been developed and incorporated in a Baxter robot platform. To imitate the recorded motion signals produced by the demonstrator, the operator trains the Baxter robot by physically guiding it to complete the given task. This is repeated five times, and the generated training data set is utilized via the playback system. Subsequently, the DTW is employed to preprocess the experimental data. For modelling and overall movement control, DMP is chosen. The GMM is used to generate multiple patterns after implementing the teaching process. Next, we employ the GMR algorithm to reduce position errors in the 3D space after a synthesized trajectory has been generated. The Baxter robot, remotely controlled by the user datagram protocol (UDP) in a PC, records and reproduces every trajectory. Additionally, Dragon Natural Speaking software is adopted to transcribe the voice data. This proposed approach has been verified by enabling the Baxter robot to perform a writing task of drawing robot has been taught to write only one character

Collaborative human-machine interfaces for mobile manipulators.

The use of mobile manipulators in service industries as both agents in physical Human Robot Interaction (pHRI) and for social interactions has been on the increase in recent times due to necessities like compensating for workforce shortages and enabling safer and more efficient operations amongst other reasons. Collaborative robots, or co-bots, are robots that are developed for use with human interaction through direct contact or close proximity in a shared space with the human users. The work presented in this dissertation focuses on the design, implementation and analysis of components for the next-generation collaborative human machine interfaces (CHMI) needed for mobile manipulator co-bots that can be used in various service industries. The particular components of these CHMI\u27s that are considered in this dissertation include: Robot Control: A Neuroadaptive Controller (NAC)-based admittance control strategy for pHRI applications with a co-bot. Robot state estimation: A novel methodology and placement strategy for using arrays of IMUs that can be embedded in robot skin for pose estimation in complex robot mechanisms. User perception of co-bot CHMI\u27s: Evaluation of human perceptions of usefulness and ease of use of a mobile manipulator co-bot in a nursing assistant application scenario. To facilitate advanced control for the Adaptive Robotic Nursing Assistant (ARNA) mobile manipulator co-bot that was designed and developed in our lab, we describe and evaluate an admittance control strategy that features a Neuroadaptive Controller (NAC). The NAC has been specifically formulated for pHRI applications such as patient walking. The controller continuously tunes weights of a neural network to cancel robot non-linearities, including drive train backlash, kinematic or dynamic coupling, variable patient pushing effort, or slope surfaces with unknown inclines. The advantage of our control strategy consists of Lyapunov stability guarantees during interaction, less need for parameter tuning and better performance across a variety of users and operating conditions. We conduct simulations and experiments with 10 users to confirm that the NAC outperforms a classic Proportional-Derivative (PD) joint controller in terms of resulting interaction jerk, user effort, and trajectory tracking error during patient walking. To tackle complex mechanisms of these next-gen robots wherein the use of encoder or other classic pose measuring device is not feasible, we present a study effects of design parameters on methods that use data from Inertial Measurement Units (IMU) in robot skins to provide robot state estimates. These parameters include number of sensors, their placement on the robot, as well as noise properties on the quality of robot pose estimation and its signal-to-noise Ratio (SNR). The results from that study facilitate the creation of robot skin, and in order to enable their use in complex robots, we propose a novel pose estimation method, the Generalized Common Mode Rejection (GCMR) algorithm, for estimation of joint angles in robot chains containing composite joints. The placement study and GCMR are demonstrated using both Gazebo simulation and experiments with a 3-DoF robotic arm containing 2 non-zero link lengths, 1 revolute joint and a 2-DoF composite joint. In addition to yielding insights on the predicted usage of co-bots, the design of control and sensing mechanisms in their CHMI benefits from evaluating the perception of the eventual users of these robots. With co-bots being only increasingly developed and used, there is a need for studies into these user perceptions using existing models that have been used in predicting usage of comparable technology. To this end, we use the Technology Acceptance Model (TAM) to evaluate the CHMI of the ARNA robot in a scenario via analysis of quantitative and questionnaire data collected during experiments with eventual uses. The results from the works conducted in this dissertation demonstrate insightful contributions to the realization of control and sensing systems that are part of CHMI\u27s for next generation co-bots

Robotics handbook. Version 1: For the interested party and professional

This publication covers several categories of information about robotics. The first section provides a brief overview of the field of Robotics. The next section provides a reasonably detailed look at the NASA Robotics program. The third section features a listing of companies and organization engaging in robotics or robotic-related activities; followed by a listing of associations involved in the field; followed by a listing of publications and periodicals which cover elements of robotics or related fields. The final section is an abbreviated abstract of referred journal material and other reference material relevant to the technology and science of robotics, including such allied fields as vision perception; three-space axis orientation and measurement systems and associated inertial reference technology and algorithms; and physical and mechanical science and technology related to robotics

Contraction and partial contraction : a study of synchronization in nonlinear networks

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005.Includes bibliographical references (p. 121-128).This thesis focuses on the study of collective dynamic behaviors, especially the spontaneous synchronization behavior, of nonlinear networked systems. We derives a body of new results, based on contraction and partial contraction analysis. Contraction is a property regarding the convergence between two arbitrary system trajectories. A nonlinear dynamic system is called contracting if initial conditions or temporary disturbances are forgotten exponentially fast. Partial contraction, introduced in this thesis, is a straightforward but more general application of contraction. It extends contraction analysis to include convergence to behaviors or to specific properties (such as equality of state components, or convergence to a manifold). Contraction and partial contraction provide powerful analysis tools to investigate the stability of large-scale complex systems. For diffusively coupled nonlinear systems, for instance, a general synchronization condition can be derived which connects synchronization rate to net- work structure explicitly. The results are applied to construct flocking or schooling models by extending to coupled networks with switching topology. We further study the networked systems with different kinds of group leaders, one specifying global orientation (power leader), another holding target dynamics (knowledge leader). In a knowledge-based leader-followers network, the followers obtain dynamics information from the leader through adaptive learning. We also study distributed networks with non-negligible time-delays by using simplified wave variables and other contraction-oriented analysis. Conditions for contraction to be preserved regardless of the explicit values of the time-delays are derived.(cont.) Synchronization behavior is shown to be robust if the protocol is linear. Finally, we study the construction of spike-based neural network models, and the development of simple mechanisms for fast inhibition and de-synchronization.by Wei Wang.Ph.D

The Sixth Annual Workshop on Space Operations Applications and Research (SOAR 1992)

This document contains papers presented at the Space Operations, Applications, and Research Symposium (SOAR) hosted by the U.S. Air Force (USAF) on 4-6 Aug. 1992 and held at the JSC Gilruth Recreation Center. The symposium was cosponsored by the Air Force Material Command and by NASA/JSC. Key technical areas covered during the symposium were robotic and telepresence, automation and intelligent systems, human factors, life sciences, and space maintenance and servicing. The SOAR differed from most other conferences in that it was concerned with Government-sponsored research and development relevant to aerospace operations. The symposium's proceedings include papers covering various disciplines presented by experts from NASA, the USAF, universities, and industry