Other

Gm Global Technolgoy Operations, Inc.

United States of America As Represented by the Administrator of Nasa

Robert E Platt

Muhammad E Abdallah

Charles W Wampler, Ii

Chris A Ihrke

Matthew J Reiland

Lyndon Bridgwater

Douglas Martin Linn

Myron A Diftler

Brian Hargrave

Frank Noble Permenter

Adam M Sanders

Scott R Askew

Joshua S Mehling

Michael C Valvo

David M Reich

Donald R Davis

Nicolaus A Radford

Adam H Parsons

Robert O Ambrose

Bryan Kristian Griffith

S Michael Goza

Craig M Corcoran

Phillip A Strawser

A robotic hand includes a finger with first, second, and third phalanges. A first joint rotatably connects the first phalange to a base structure. A second joint rotatably connects the first phalange to the second phalange. A third joint rotatably connects the third phalange to the second phalange. The second joint and the third joint are kinematically linked such that the position of the third phalange with respect to the second phalange is determined by the position of the second phalange with respect to the first phalange.

Robotic finger assembly

A robotic system includes a robot having a total number of degrees of freedom (DOF) equal to at least n, an underactuated tendon-driven finger driven by n tendons and n DOF, the finger having at least two joints, being characterized by an asymmetrical joint radius in one embodiment. A controller is in communication with the robot, and controls actuation of the tendon-driven finger using force control. Operating the finger with force control on the tendons, rather than position control, eliminates the unconstrained slack-space that would have otherwise existed. The controller may utilize the asymmetrical joint radii to independently command joint torques. A method of controlling the finger includes commanding either independent or parameterized joint torques to the controller to actuate the fingers via force control on the tendons.

Torque control of underactuated tendon-driven robotic fingers

An SEA architecture for controlling the torque applied by an SEA that has particular application for controlling the position of a robot link. The SEA architecture includes a motor coupled to one end of an elastic spring and a load coupled to an opposite end of the elastic spring, where the motor drives the load through the spring. The orientation of the shaft of the motor and the load are measured by position sensors. Position signals from the position sensors are sent to an embedded processor that determines the orientation of the load relative to the motor shaft to determine the torque on the spring. The embedded processor receives reference torque signals from a remote controller, and the embedded processor operates a high-speed servo loop about the desired joint torque. The remote controller determines the desired joint torque based on higher order objectives by their impedance or positioning objectives.

Architecture for robust force and impedance control of series elastic actuators

A humanoid robot includes a torso, a pair of arms, two hands, a neck, and a head. The torso extends along a primary axis and presents a pair of shoulders. The pair of arms movably extend from a respective one of the pair of shoulders. Each of the arms has a plurality of arm joints. The neck movably extends from the torso along the primary axis. The neck has at least one neck joint. The head movably extends from the neck along the primary axis. The head has at least one head joint. The shoulders are canted toward one another at a shrug angle that is defined between each of the shoulders such that a workspace is defined between the shoulders.

Humanoid robot

A robotic system includes a robot having manipulators for grasping an object using one of a plurality of grasp types during a primary task, and a controller. The controller controls the manipulators during the primary task using a multiple-task control hierarchy, and automatically parameterizes the internal forces of the system for each grasp type in response to an input signal. The primary task is defined at an object-level of control, e.g., using a closed-chain transformation, such that only select degrees of freedom are commanded for the object. A control system for the robotic system has a host machine and algorithm for controlling the manipulators using the above hierarchy. A method for controlling the system includes receiving and processing the input signal using the host machine, including defining the primary task at the object-level of control, e.g., using a closed-chain definition, and parameterizing the internal forces for each of grasp type.

Hierarchical robot control system and method for controlling select degrees of freedom of an object using multiple manipulators

A humanoid robot includes a robotic hand having at least one finger. An actuation system for the robotic finger includes an actuator assembly which is supported by the robot and is spaced apart from the finger. A tendon extends from the actuator assembly to the at least one finger and ends in a tendon terminator. The actuator assembly is operable to actuate the tendon to move the tendon terminator and, thus, the finger.

Tendon driven finger actuation system

An improved robotic thumb for a robotic hand assembly is provided. According to one aspect of the disclosure, improved tendon routing in the robotic thumb provides control of four degrees of freedom with only five tendons. According to another aspect of the disclosure, one of the five degrees of freedom of a human thumb is replaced in the robotic thumb with a permanent twist in the shape of a phalange. According to yet another aspect of the disclosure, a position sensor includes a magnet having two portions shaped as circle segments with different center points. The magnet provides a linearized output from a Hall effect sensor.

Robotic thumb assembly

A method for calibrating tension sensors on tendons in a tendon-driven manipulator without disassembling the manipulator and without external force references. The method calibrates the tensions against each other to produce results that are kinematically consistent. The results might not be absolutely accurate, however, they are optimized with respect to an initial or nominal calibration. The method includes causing the tendons to be slack and recording the sensor values from sensors that measure the tension on the tendons. The method further includes tensioning the tendons with the manipulator positioned so that it is not in contact with any obstacle or joint limit and again recording the sensor values. The method then performs a regression process to determine the sensor parameters that both satisfy a zero-torque constraint on the manipulator and minimize the error with respect to nominal calibration values.

In-vivo tension calibration in tendon-driven manipulators

A method is provided for distributing tension among tendons of a tendon-driven finger in a robotic system, wherein the finger characterized by n degrees of freedom and n+1 tendons. The method includes determining a maximum functional tension and a minimum functional tension of each tendon of the finger, and then using a controller to distribute tension among the tendons, such that each tendon is assigned a tension value less than the maximum functional tension and greater than or equal to the minimum functional tension. The method satisfies the minimum functional tension while minimizing the internal tension in the robotic system, and satisfies the maximum functional tension without introducing a coupled disturbance to the joint torques. A robotic system includes a robot having at least one tendon-driven finger characterized by n degrees of freedom and n+1 tendons, and a controller having an algorithm for controlling the tendons as set forth above.

Tension distribution in a tendon-driven robotic finger

A tendon tension sensor that has particular application for measuring tension on a tendon employed in a robotic arm. The tension sensor includes an elastic element having a curved channel through which the tendon is threaded. The elastic element also includes a center portion on which strain gauges are mounted that measure the strain on the elastic element. Tension on the tendon causes the center portion of the elastic element to flex or bend, which is measured by the strain gauges providing an indication of the tension in the tendon.

Tendon tension sensor

A robotic system includes a humanoid robot having a plurality of joints adapted for force control with respect to an object acted upon by the robot, a graphical user interface (GUI) for receiving an input signal from a user, and a controller. The GUI provides the user with intuitive programming access to the controller. The controller controls the joints using an impedance-based control framework, which provides object level, end-effector level, and/or joint space-level control of the robot in response to the input signal. A method for controlling the robotic system includes receiving the input signal via the GUI, e.g., a desired force, and then processing the input signal using a host machine to control the joints via an impedance-based control framework. The framework provides object level, end-effector level, and/or joint space-level control of the robot, and allows for functional-based GUI to simplify implementation of a myriad of operating modes.

Method and apparatus for automatic control of a humanoid robot

A method for identifying the location, orientation and shape of an object that a robot hand touches that includes using a particle filter. The method includes defining an appropriate motion model and a measurement model. The motion model characterizes the motion of the robot hand as it moves relative to the object. The measurement model estimates the likelihood of an observation of contact position, velocity and tactile sensor information given hand-object states. The measurement model is approximated analytically based on a geometric model or based on a corpus of training data. In either case, the measurement model distribution is encoded as a Gaussian or using radial basis functions.

Contact state estimation for multi-finger robot hands using particle filters

A robotic system includes a robot having joints, actuators, and sensors, and a distributed controller. The controller includes command-level controller, embedded joint-level controllers each controlling a respective joint, and a joint coordination-level controller coordinating motion of the joints. A central data library (CDL) centralizes all control and feedback data, and a user interface displays a status of each joint, actuator, and sensor using the CDL. A parameterized action sequence has a hierarchy of linked events, and allows the control data to be modified in real time. A method of controlling the robot includes transmitting control data through the various levels of the controller, routing all control and feedback data to the CDL, and displaying status and operation of the robot using the CDL. The parameterized action sequences are generated for execution by the robot, and a hierarchy of linked events is created within the sequence.

Interactive robot control system and method of use

A system and method for providing multiple priority impedance control for a robot manipulator where impedance laws are realized simultaneously and with a given order of priority. The method includes a control scheme for realizing a Cartesian space impedance objective as a first priority while also realizing a joint space impedance objective as a second priority. The method also includes a control scheme for realizing two Cartesian space impedance objectives with different levels of priority. The method includes instances of the control schemes that use feedback from force sensors mounted at an end-effector and other instances of the control schemes that do not use this feedback.

Multiple priority operational space impedance control

A system and method for controlling tendon-driven manipulators that provide a closed-loop control of joint torques or joint impedances without inducing dynamic coupling between joints. The method includes calculating tendon reference positions or motor commands by projecting a torque error into tendon position space using a single linear operation. The method calculates this torque error using sensed tendon tensions and a reference torque and internal tension. The method can be used to control joint impedance by calculating the reference torque based on a joint position error. The method limits minimum and maximum tendon tensions by projecting the torque error into the tendon tension space and then projecting ii back into joint space.

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Mineral Ridge, OH, United States of America

Robert E Platt