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Number of arms increased without introducing undue complexity. Strategy and computer architecture developed for simultaneous control of positions of number of robot arms manipulating same object and of forces and torques that arms exert on object. Scheme enables coordinated manipulation of object, causing it to move along assigned trajectory and be subjected to assigned internal forces and torques.
The structure of a flexible arm derived from concatenation of the Stewart-Table-based links were studied. Solid modeling provides not only a realistic simulation, but is also essential for studying vision algorithms. These algorithms could be used for the adaptive control of the arm, using the well-known algorithms such as shape from shading, edge detection, orientation, etc. Details of solid modeling and its relation to vision based adaptive control are discussed.
Soft robots are challenging to model and control due to their poorly defined kinematics and nonlinear dynamics. Recently, Koopman operator theory has been shown capable of constructing control-oriented soft robot models from data. However, building these models requires extensive data collection and they do not necessarily generalize well outside of the training observations. This paper presents a more data-efficient and generalizable approach to soft robot modeling that first identifies a physics-based Koopman model then supplements it with a data-driven residual Koopman model. The resulting combined model is linear and thus compatible with real-time model-based control techniques such as Model Predictive Control (MPC). The efficacy of the approach is demonstrated on several simulated systems and on a real soft robot arm, where it is shown to generate models that are more accurate than purely physics-based models and require less data to construct than purely data-driven models. Using a model-based controller, the soft arm is able to successfully track end effector trajectories, perform a pick-and-place task, and write on a dry-erase board, showcasing the applicability of this framework to increase the capabilities of soft robotic systems.
A robotic arm that consists of three joints with four degrees of freedom (DOF) has been developed. It can carry an end-effector to acquire and transfer samples by using active control and comply with surface topology in a passive mode during a brief surface contact. The three joints are arranged in such a way that one joint of two DOFs is located at the shoulder, one joint of one DOF is located at the elbow, and one joint of one DOF is located at the wrist. Operationally, three DOFs are moved in the same plane, and the remaining one on the shoulder is moved perpendicular to the other three for better compliance with ground surface and more flexibility of sample handling. Three out of four joints are backdriveable, making the mechanism less complex and more cost effective
A novel neural network approach to robot hand-eye coordination is presented. The approach provides a true sense of visual error servoing, redundant arm configuration control for collision avoidance, and invariant visuo-motor learning under gazing control. A 3-D perception network is introduced to represent the robot internal 3-D metric space in which visual error servoing and arm configuration control are performed. The arm kinematic network performs the bidirectional association between 3-D space arm configurations and joint angles, and enforces the legitimate arm configurations. The arm kinematic net is structured by a radial-based competitive and cooperative network with hierarchical self-organizing learning. The main goal of the present work is to demonstrate that the neural net representation of the robot 3-D perception net serves as an important intermediate functional block connecting robot eyes and arms.
This thesis describes the design and functionality of a Distributed Petri Net Controller (DPNC). The controller runs under X Windows to provide a graphical interface. The DPNC allows users to distribute a Petri Net across several host computers linked together via a TCP/IP interface. A sub-net executes on each host, interacting with the other sub-nets by passing a token vector from host to host. One host has a command window which monitors and controls the distributed controller. The input to the DPNC is a net definition file generated by Great SPN. Thus, a net may be designed, analyzed and verified using this package before implementation. The net is distributed to the hosts by tagging transitions that are host-critical with the appropriate host number. The controller will then distribute the remaining places and transitions to the hosts by generating the local nets, the local marking vectors and the global marking vector. Each transition can have one or more preconditions which must be fulfilled before the transition can fire, as well as one or more post-processes to be executed after the transition fires. These implement the actual input/output to the environment (machines, signals, etc.). The DPNC may also be used to simulate a Great SPN net since stochastic and deterministic firing rates are implemented in the controller for timed transitions.
A force-compensated control method for multiple manipulators is presented that allows coordinated manipulation of a jointly grasped object. In this scheme, each arm independently carries out the motions required to realize the desired motion of a prescribed point on the manipulated object. The approach has been implemented and demonstrated on a laboratory system consisting of two industrial, computer-controller manipulators.
Much of the technology planned for use in NASA's Flight Telerobotic Servicer (FTS) and the Demonstration Test Flight (DTF) is relatively new and untested. To provide the answers needed to design safe, reliable, and fully functional robotics for flight, NASA/GSFC is developing a robotics technology testbed for research of issues such as zero-g robot control, dual arm teleoperation, simulations, and hierarchical control using a high level programming language. The testbed will be used to investigate these high risk technologies required for the FTS and DTF projects. The robotics technology testbed is centered around the dual arm teleoperation of a pair of 7 degree-of-freedom (DOF) manipulators, each with their own 6-DOF mini-master hand controllers. Several levels of safety are implemented using the control processor, a separate watchdog computer, and other low level features. High speed input/output ports allow the control processor to interface to a simulation workstation: all or part of the testbed hardware can be used in real time dynamic simulation of the testbed operations, allowing a quick and safe means for testing new control strategies. The NASA/National Bureau of Standards Standard Reference Model for Telerobot Control System Architecture (NASREM) hierarchical control scheme, is being used as the reference standard for system design. All software developed for the testbed, excluding some of simulation workstation software, is being developed in Ada. The testbed is being developed in phases. The first phase, which is nearing completion, and highlights future developments is described.
A stability analysis for robot manipulators under the influence of external forces is presented. Several control objectives are considered: rejecting the external force as a source of disturbance, complying to the external force as a generalized mass-spring-damper system, and actively controlling the external force when a dynamic model for the environment is available. An explanation of instability is given for the case in which the environment has flexibility and the gains are inappropriately chosen. When the environment is stiff in the force control subspace, robust stability can be achieved via the integral force feedback.
A handling qualities analysis has been performed on two unique side stick controllers in a fixed-base F-18 flight simulator. Each stick, which uses a larger range of motion than is common for similar controllers, has a moving elbow cup that accommodates movement of the entire arm for control. The sticks are compared to the standard center stick in several typical fighter aircraft tasks. Several trends are visible in the time histories, pilot ratings, and pilot comments. The aggressive pilots preferred the center stick, because the side sticks are underdamped, causing overshoots and oscillations when large motions are executed. The less aggressive pilots preferred the side sticks, because of the smooth motion and low breakout forces. The aggressive pilots collectively gave the worst ratings, probably because of increased sensitivity of the simulator (compared to the actual F-18 aircraft), which can cause pilot-induced oscillations when aggressive inputs are made. Overall, the elbow cup is not a positive feature, because using the entire arm for control inhibits precision. Pilots had difficulty measuring their performance, particularly during the offset landing task, and tended to overestimate.
System of modular, reusable hardware and software assembled for use in developing remotely controlled robotic arms. Includes (1) central computer and peripheral equipment at control and monitoring station and (2) remote mechanical platform that supports robotic arm. Central computer controls motor drives of robotic arm, but optically, platform holds on-board computer for autonomous operation. Consists mostly of commercial hardware and software. Simulated results of commands viewed in three dimensions.
Coordination of multiple manipulators requires cooperation at several levels in the control hierarchy. A distributed processing environment with no hardware dependencies except at the motor servo level would provide a flexible architecture for coordination. A system on these lines is being built to control an articulated hand and an arm. The four levels of control envisaged include a task decomposition level, a planning level, a scheduling level, and a server level. The hand will carry both force and tactile sensors, feedback from these are used to provide adaptive control in grasping tasks. The processing of the sensory information is performed by independent processes, with analyzed information being sent to the relevant layer of the system. The manipulators are also controlled by individual processes. All process can open communications with an active process sending commands or data, or receiving them. The scope of the system and the current setup are described along with future lines of development.
A simple strategy for the attitude control and arm coordination of a maneuverable space robot with dual arms is proposed. The basic task for the robot consists of the placement of marked rigid solid objects with specified pairs of gripping points and a specified direction of approach for gripping. The strategy consists of three phases each of which involves only elementary rotational and translational collision-free maneuvers of the robot body. Control laws for these elementary maneuvers are derived by using a body-referenced dynamic model of the dual-arm robot.
Exact equations of motion of an arm with known parameters were developed and analyzed preparatory to designing control systems for robotic manipulators. The design of an experimental one-link arm for testing control designs is presented.
Multiple coordinated robot arms are modeled by considering the arms: (1) as closed kinematic chains, and (2) as a force constrained mechanical system working on the same object simultaneously. In both formulations a new dynamic control method is discussed. It is based on a feedback linearization and simultaneous output decoupling technique. Applying a nonlinear feedback and a nonlinear coordinate transformation, the complicated model of the multiple robot arms in either formulation is converted into a linear and output decoupled system. The linear system control theory and optimal control theory are used to design robust controllers in the task space. The first formulation has the advantage of automatically handling the coordination and load distribution among the robot arms. In the second formulation, by choosing a general output equation, researchers can superimpose the position and velocity error feedback with the force-torque error feedback in the task space simultaneously.
At the present time, myoelectric prostheses perform only one function of the hand: open and close with the thumb, index and middle finger coming together to grasp various shaped objects. To better understand the limitations of the current single-function prostheses and the needs of the individuals who use them, The Institute for Rehabilitation and Research (TIRR), sponsored by the National Institutes of Health (August 1992 - November 1994), surveyed approximately 2500 individuals with upper limb loss. When asked to identify specific features of their current electric prosthesis that needed improvement, the survey respondents overwhelmingly identified the lack of wrist and finger movement as well as poor control capability. Simply building a mechanism with individual finger and wrist motion is not enough. Individuals with upper limb loss tend to reject prostheses that require continuous visual monitoring and concentration to control. Robotics researchers at NASA's Johnson Space Center (JSC) and Rice University have made substantial progress in myoelectric teleoperation. A myoelectric teleoperation system translates signals generated by an able-bodied robot operator's muscles during hand motions into commands that drive a robot's hand through identical motions. Farry's early work in myoelectric teleoperation used variations over time in the myoelectric spectrum as inputs to neural networks to discriminate grasp types and thumb motions. The resulting schemes yielded up to 93% correct classification on thumb motions. More recently, Fernandez achieved 100% correct non-realtime classification of thumb abduction, extension, and flexion on the same myoelectric data. Fernandez used genetic programming to develop functions that discriminate between thumb motions using myoelectric signal parameters. Genetic programming (GP) is an evolutionary programming method where the computer can modify the discriminating functions' form to improve its performance, not just adjust numerical coefficients or weights. Although the function development may require much computational time and many training cases, the resulting discrimination functions can run in realtime on modest computers. These results suggest that myoelectric signals might be a feasible teleoperation medium, allowing an operator to use his or her own hand and arm as a master to intuitively control an anthropomorphic robot in a remote location such as outer space.
Arm inductor in a modular multilevel converter (MMC) is used to limit the circulating current and dc short circuit fault current. The circulating current in MMC is dominated by second-order harmonic, which can be largely reduced with circulating current suppressing control. By analyzing the mechanism of the circulating current suppressing control, it is found that the circulating current at switching frequency becomes the main harmonic when suppression control is implemented. Unlike the second-order harmonic that circulates only within the three phases, switching frequency harmonic also flows through the dc side and may further cause high-frequency dc voltage harmonic. This article develops the theoretical relationship between the arm inductance and switching frequency circulating current, which can be used to guide the arm inductance selection. The experimental results with a downscaled MMC prototype verify the existence of the switching frequency circulating current and its relationship with arm inductance.
The motivation of this research came about when a neural network direct adaptive control scheme was applied to control the tip position of a flexible robotic arm. Satisfactory control performance was not attainable due to the inherent non-minimum phase characteristics of the flexible robotic arm tip. Most of the existing neural network control algorithms are based on the direct method and exhibit very high sensitivity, if not unstable, closed-loop behavior. Therefore, a neural self-tuning control (NSTC) algorithm is developed and applied to this problem and showed promising results. Simulation results of the NSTC scheme and the conventional self-tuning (STR) control scheme are used to examine performance factors such as control tracking mean square error, estimation mean square error, transient response, and steady state response.