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NASA NTRS2021

IROSA 2B/4B EVA Robotics

The MAGIK Robotic analysis team provides kinematic feasibility assessments and robotic visualizations for the ISS. This animation was created with configurations provided by the CX Team and showcases the robotics for the IROSA install 2B and 4B EVAs. The ExtraVehicular Activities (EVAs) shown include the extraction of the two ISS Roll-Out Solar Arrays (IROSAs) from the ISS Power Augmentation (IPA) carrier by an astronaut on the end of the Space Station Remote Manipulator System (SSRMS) and subsequent handoff to a second astronaut on the ISS port side truss. Extravehicular activities not shown will complete the install of the IROSAs.

Alexander Radford Fryer↗

NASA NTRS2021

Visualization of Upcoming ISS Changes - Featuring MLM Install

The MAGIK Robotic analysis team provides kinematic feasibility assessments and robotic visualizations for the ISS. The latest visualization displays ISS planned operations in increments 65 and 66 as of the 7/6/21 flight plan. Featured events include the arrival of the Multipurpose Laboratory Module (MLM) and surrounding visiting vehicle actions. The complete list of actions shown include: Repeated ISS attitude adjustment maneuvers from: starting position with positive X Velocity Vector (+XVV), yaw to -XVV, and pitch to final position with -ZVV. Then subsequent return to -XVV and nominal position +XVV. Crew-2 relocation from Node 2 Forward (N2F) to Node 2 Zenith (N2Z). 77 Progress with Docking Compartment 1 (DC1) undock and departure. MLM install. Boe-OFT2 arrival and departure at N2F. NG-16 arrival and berth at Node 1 Nadir (N1N). SpX-23 arrival at N2F. 64 Soyuz relocation and fly-around. SpX-23 departure from N2F. 65 Soyuz approach and dock to MRM-1. 64 Soyuz undock and departure. 78 Progress relocation.

ISS↗

NASA NTRS2022

IROSA 3A EVA Robotics

The MAGIK Robotic analysis team provides kinematic feasibility assessments and robotic visualizations for the ISS. This animation was created with configurations provided by the CX Team and showcases the robotics for the IROSA 3A EVA install. The ExtraVehicular Activity (EVA) shown include the extraction of one ISS Roll-Out Solar Array (IROSA) from the ISS Power Augmentation (IPA) carrier by an astronaut on the end of the Space Station Remote Manipulator System (SSRMS) and subsequent install on the ISS starboard side truss. Operations during the EVA not shown will complete the install of the IROSA.

ISS↗

NASA NTRS1988

Calculating Robot-Joint Coordinates From Image Coordinates

Detailed knowledge of robot joints not required. Algorithm generates approximate mathematical models of coordinates of joints of robot as functions of coordinates of points in images of work region viewed by television cameras. Joint coordinates necessary to position and orient end effector calculated by mathematical models fitted to experimentally determined data on positions, orientations, and joint coordinates. Generates models as functions of desired location of end effector of robot. Does not require priori knowledge of kinematic equations of robot.

Source record↗

NASA NTRS1988

Cartesian path control of a two-degree-of-freedom robot manipulator

The problem of cartesian trajectory control of a closed-kinematic chain mechanism robot manipulator with possible space station applications is considered. The study was performed by both computer simulation and experimentation for tracking of three different paths: a straight line, a sinusoid and a circle. Linearization and pole placement methods are employed to design controller gains. Results show that the controllers are robust and there are good agreements between simulation and experimentation. Excellent tracking quality and small overshoots are also evident.

Nguyen, Charles C.↗

NASA NTRS1989

Kinematic functions for the 7 DOF robotics research arm

The Robotics Research Model K-1207 manipulator is a redundant 7R serial link arm with offsets at all joints. To uniquely determine joint angles for a given end-effector configuration, the redundancy is parameterized by a scalar variable which corresponds to the angle between the manipulator elbow plane and the vertical plane. The forward kinematic mappings from joint-space to end-effector configuration and elbow angle, and the augmented Jacobian matrix which gives end-effector and elbow angle rates as a function of joint rates, are also derived.

Kreutz, K.↗

NASA NTRS1987

Computational structures for robotic computations

The computational problem of inverse kinematics and inverse dynamics of robot manipulators by taking advantage of parallelism and pipelining architectures is discussed. For the computation of inverse kinematic position solution, a maximum pipelined CORDIC architecture has been designed based on a functional decomposition of the closed-form joint equations. For the inverse dynamics computation, an efficient p-fold parallel algorithm to overcome the recurrence problem of the Newton-Euler equations of motion to achieve the time lower bound of O(log sub 2 n) has also been developed.

Lee, C. S. G.↗

NASA NTRS1992

Neural joint control for Space Shuttle Remote Manipulator System

Neural networks are being used to control a robot arm in a telerobotic operation. The concept uses neural networks for both joint and inverse kinematics in a robotic control application. An upper level neural network is trained to learn inverse kinematic mappings. The output, a trajectory, is then fed to the Decentralized Adaptive Joint Controllers. This neural network implementation has shown that the controlled arm recovers from unexpected payload changes while following the reference trajectory. The neural network-based decentralized joint controller is faster, more robust and efficient than conventional approaches. Implementations of this architecture are discussed that would relax assumptions about dynamics, obstacles, and heavy loads. This system is being developed to use with the Space Shuttle Remote Manipulator System.

Atkins, Mark A.↗

NASA NTRS1986

Overcoming Robot-Arm Joint Singularities

Kinematic equations allow arm to pass smoothly through singular region. Report discusses mathematical singularities in equations of robotarm control. Operator commands robot arm to move in direction relative to its own axis system by specifying velocity in that direction. Velocity command then resolved into individual-joint rotational velocities in robot arm to effect motion. However, usual resolved-rate equations become singular when robot arm is straightened.

Barker, L. K.↗

NASA NTRS2001

Analysis and experiments with an elephant's trunk robot

The area of tentacle and trunk type biological manipulation is not new, but there has been little progress in the development and application of a physical device to simulate these types of manipulation. Our research in this area is based on using an 'elephant trunk' robot. In this paper, we review the construction of the robot and how it compares to biological manipulators. We then apply our previously designed kinematic model to describe the kinematics of the robot. We finish by providing some examples of motion planning and intelligent manipulation using the robot.

Non-NASA Center↗

NASA NTRS1980

Computer coordination of limb motion for a three-legged walking robot

Coordination of the limb motion of a vehicle which could perform assembly and maintenance operations on large structures in space is described. Manipulator kinematics and walking robots are described. The basic control scheme of the robot is described. The control of the individual arms are described. Arm velocities are generally described in Cartesian coordinates. Cartesian velocities are converted to joint velocities using the Jacobian matrix. The calculation of a trajectory for an arm given a sequence of points through which it is to pass is described. The free gait algorithm which controls the lifting and placing of legs for the robot is described. The generation of commanded velocities for the robot, and the implementation of those velocities by the algorithm are discussed. Suggestions for further work in the area of robot legged locomotion are presented.

Klein, C. A.↗

NASA NTRS1996

Control Of A Serpentine Robot For Inspection Tasks

Efficient, robust kinematic control scheme developed to control serpentine robot designed to inspect complex structure. Takes full advantage of multiple redundant degrees of freedom of robot to provide considerable dexterity for maneuvering through workspace cluttered with stationary obstacles at initially unknown positions. Control scheme produces slithering motion.

Seraji, Homayoun↗

NASA NTRS1994

Control of a Serpentine Robot for Inspection Tasks

This paper presents a simple and robust kinematic control scheme for the JPL serpentine robot system. The proposed strategy is developed using the dampened-least-squares/configuration control methodology, and permits the considerable dexterity of the JPL serpentine robot to be effectively utilized for maneuvering in the congested and uncertain workspaces often encountered in inspection tasks. Computer simulation results are given for the 20 degree-of-freedom (DOF) manipulator system obtained by mounting the twelve DOF serpentine robot at the end-effector of an eight DOF Robotics Research arm/lathe-bed system. These simulations demonstrate that the proposed approach provides an effective method of controlling this complex system.

robotics simulations kinematic control serpentine ↗

NASA NTRS2015

Fast Grasp Contact Computation for a Serial Robot

A system includes a controller and a serial robot having links that are interconnected by a joint, wherein the robot can grasp a three-dimensional (3D) object in response to a commanded grasp pose. The controller receives input information, including the commanded grasp pose, a first set of information describing the kinematics of the robot, and a second set of information describing the position of the object to be grasped. The controller also calculates, in a two-dimensional (2D) plane, a set of contact points between the serial robot and a surface of the 3D object needed for the serial robot to achieve the commanded grasp pose. A required joint angle is then calculated in the 2D plane between the pair of links using the set of contact points. A control action is then executed with respect to the motion of the serial robot using the required joint angle.

Shi, Jianying↗

NASA NTRS1996

On-Line Method and Apparatus for Coordinated Mobility and Manipulation of Mobile Robots

A simple and computationally efficient approach is disclosed for on-line coordinated control of mobile robots consisting of a manipulator arm mounted on a mobile base. The effect of base mobility on the end-effector manipulability index is discussed. The base mobility and arm manipulation degrees-of-freedom are treated equally as the joints of a kinematically redundant composite robot. The redundancy introduced by the mobile base is exploited to satisfy a set of user-defined additional tasks during the end-effector motion. A simple on-line control scheme is proposed which allows the user to assign weighting factors to individual degrees-of-mobility and degrees-of-manipulation, as well as to each task specification. The computational efficiency of the control algorithm makes it particularly suitable for real-time implementations. Four case studies are discussed in detail to demonstrate the application of the coordinated control scheme to various mobile robots.

Seraji, Homayoun↗

NASA NTRS2002

The Summer Robotic Autonomy Course

We offered a first Robotic Autonomy course this summer, located at NASA/Ames' new NASA Research Park, for approximately 30 high school students. In this 7-week course, students worked in ten teams to build then program advanced autonomous robots capable of visual processing and high-speed wireless communication. The course made use of challenge-based curricula, culminating each week with a Wednesday Challenge Day and a Friday Exhibition and Contest Day. Robotic Autonomy provided a comprehensive grounding in elementary robotics, including basic electronics, electronics evaluation, microprocessor programming, real-time control, and robot mechanics and kinematics. Our course then continued the educational process by introducing higher-level perception, action and autonomy topics, including teleoperation, visual servoing, intelligent scheduling and planning and cooperative problem-solving. We were able to deliver such a comprehensive, high-level education in robotic autonomy for two reasons. First, the content resulted from close collaboration between the CMU Robotics Institute and researchers in the Information Sciences and Technology Directorate and various education program/project managers at NASA/Ames. This collaboration produced not only educational content, but will also be focal to the conduct of formative and summative evaluations of the course for further refinement. Second, CMU rapid prototyping skills as well as the PI's low-overhead perception and locomotion research projects enabled design and delivery of affordable robot kits with unprecedented sensory- locomotory capability. Each Trikebot robot was capable of both indoor locomotion and high-speed outdoor motion and was equipped with a high-speed vision system coupled to a low-cost pan/tilt head. As planned, follow the completion of Robotic Autonomy, each student took home an autonomous, competent robot. This robot is the student's to keep, as she explores robotics with an extremely capable tool in the midst of a new community for roboticists. CMU provided undergraduate course credit for this official course, 16-162U, for 13 students, with all other students receiving course credit from National Hispanic University.

Nourbakhsh, Illah R.↗

NASA NTRS1993

Planning collision free paths for two cooperating robots using a divide-and-conquer C-space traversal heuristic

A method was developed to plan feasible and obstacle-avoiding paths for two spatial robots working cooperatively in a known static environment. Cooperating spatial robots as referred to herein are robots which work in 6D task space while simultaneously grasping and manipulating a common, rigid payload. The approach is configuration space (c-space) based and performs selective rather than exhaustive c-space mapping. No expensive precomputations are required. A novel, divide-and-conquer type of heuristic is used to guide the selective mapping process. The heuristic does not involve any robot, environment, or task specific assumptions. A technique was also developed which enables solution of the cooperating redundant robot path planning problem without requiring the use of inverse kinematics for a redundant robot. The path planning strategy involves first attempting to traverse along the configuration space vector from the start point towards the goal point. If an unsafe region is encountered, an intermediate via point is identified by conducting a systematic search in the hyperplane orthogonal to and bisecting the unsafe region of the vector. This process is repeatedly applied until a solution to the global path planning problem is obtained. The basic concept behind this strategy is that better local decisions at the beginning of the trouble region may be made if a possible way around the 'center' of the trouble region is known. Thus, rather than attempting paths which look promising locally (at the beginning of a trouble region) but which may not yield overall results, the heuristic attempts local strategies that appear promising for circumventing the unsafe region.

Weaver, Johnathan M.↗

NASA NTRS2005

Mapping From an Instrumented Glove to a Robot Hand

An algorithm has been developed to solve the problem of mapping from (1) a glove instrumented with joint-angle sensors to (2) an anthropomorphic robot hand. Such a mapping is needed to generate control signals to make the robot hand mimic the configuration of the hand of a human attempting to control the robot. The mapping problem is complicated by uncertainties in sensor locations caused by variations in sizes and shapes of hands and variations in the fit of the glove. The present mapping algorithm is robust in the face of these uncertainties, largely because it includes a calibration sub-algorithm that inherently adapts the mapping to the specific hand and glove, without need for measuring the hand and without regard for goodness of fit. The algorithm utilizes a forward-kinematics model of the glove derived from documentation provided by the manufacturer of the glove. In this case, forward-kinematics model signifies a mathematical model of the glove fingertip positions as functions of the sensor readings. More specifically, given the sensor readings, the forward-kinematics model calculates the glove fingertip positions in a Cartesian reference frame nominally attached to the palm. The algorithm also utilizes an inverse-kinematics model of the robot hand. In this case, inverse-kinematics model signifies a mathematical model of the robot finger-joint angles as functions of the robot fingertip positions. Again, more specifically, the inverse-kinematics model calculates the finger-joint commands needed to place the fingertips at specified positions in a Cartesian reference frame that is attached to the palm of the robot hand and that nominally corresponds to the Cartesian reference frame attached to the palm of the glove. Initially, because of the aforementioned uncertainties, the glove fingertip positions calculated by the forwardkinematics model in the glove Cartesian reference frame cannot be expected to match the robot fingertip positions in the robot-hand Cartesian reference frame. A calibration must be performed to make the glove and robot-hand fingertip positions correspond more precisely. The calibration procedure involves a few simple hand poses designed to provide well-defined fingertip positions. One of the poses is a fist. In each of the other poses, a finger touches the thumb. The calibration subalgorithm uses the sensor readings from these poses to modify the kinematical models to make the two sets of fingertip positions agree more closely.

Goza, Michael↗