Patent application number | Description | Published |
20100280663 | METHOD AND APPARATUS FOR AUTOMATIC CONTROL OF A HUMANOID ROBOT - 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. | 11-04-2010 |
20110067521 | HUMANOID ROBOT - 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. | 03-24-2011 |
20110071672 | FRAMEWORK AND METHOD FOR CONTROLLING A ROBOTIC SYSTEM USING A DISTRIBUTED COMPUTER NETWORK - A robotic system for performing an autonomous task includes a humanoid robot having a plurality of compliant robotic joints, actuators, and other integrated system devices that are controllable in response to control data from various control points, and having sensors for measuring feedback data at the control points. The system includes a multi-level distributed control framework (DCF) for controlling the integrated system components over multiple high-speed communication networks. The DCF has a plurality of first controllers each embedded in a respective one of the integrated system components, e.g., the robotic joints, a second controller coordinating the components via the first controllers, and a third controller for transmitting a signal commanding performance of the autonomous task to the second controller. The DCF virtually centralizes all of the control data and the feedback data in a single location to facilitate control of the robot across the multiple communication networks. | 03-24-2011 |
20110071676 | INTERACTIVE ROBOT CONTROL SYSTEM AND METHOD OF USE - 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. | 03-24-2011 |
20110071679 | EMBEDDED DIAGNOSTIC, PROGNOSTIC, AND HEALTH MANAGEMENT SYSTEM AND METHOD FOR A HUMANOID ROBOT - A robotic system includes a humanoid robot with multiple compliant joints, each moveable using one or more of the actuators, and having sensors for measuring control and feedback data. A distributed controller controls the joints and other integrated system components over multiple high-speed communication networks. Diagnostic, prognostic, and health management (DPHM) modules are embedded within the robot at the various control levels. Each DPHM module measures, controls, and records DPHM data for the respective control level/connected device in a location that is accessible over the networks or via an external device. A method of controlling the robot includes embedding a plurality of the DPHM modules within multiple control levels of the distributed controller, using the DPHM modules to measure DPHM data within each of the control levels, and recording the DPHM data in a location that is accessible over at least one of the high-speed communication networks. | 03-24-2011 |
20120072019 | CONCURRENT PATH PLANNING WITH ONE OR MORE HUMANOID ROBOTS - A robotic system includes a controller and one or more robots each having a plurality of robotic joints. Each of the robotic joints is independently controllable to thereby execute a cooperative work task having at least one task execution fork, leading to multiple independent subtasks. The controller coordinates motion of the robot(s) during execution of the cooperative work task. The controller groups the robotic joints into task-specific robotic subsystems, and synchronizes motion of different subsystems during execution of the various subtasks of the cooperative work task. A method for executing the cooperative work task using the robotic system includes automatically grouping the robotic joints into task-specific subsystems, and assigning subtasks of the cooperative work task to the subsystems upon reaching a task execution fork. The method further includes coordinating execution of the subtasks after reaching the task execution fork. | 03-22-2012 |
20130035792 | METHOD AND SYSTEM FOR CONTROLLING A DEXTEROUS ROBOT EXECUTION SEQUENCE USING STATE CLASSIFICATION - A robotic system includes a dexterous robot and a controller. The robot includes a plurality of robotic joints, actuators for moving the joints, and sensors for measuring a characteristic of the joints, and for transmitting the characteristics as sensor signals. The controller receives the sensor signals, and is configured for executing instructions from memory, classifying the sensor signals into distinct classes via the state classification module, monitoring a system state of the robot using the classes, and controlling the robot in the execution of alternative work tasks based on the system state. A method for controlling the robot in the above system includes receiving the signals via the controller, classifying the signals using the state classification module, monitoring the present system state of the robot using the classes, and controlling the robot in the execution of alternative work tasks based on the present system state. | 02-07-2013 |
20130096719 | METHOD FOR DYNAMIC OPTIMIZATION OF A ROBOT CONTROL INTERFACE - A control interface for inputting data into a controller and/or controlling a robotic system is displayed on a human-to-machine interface device. The specific configuration of the control interface displayed is based upon the task to be performed, the capabilities of the robotic system, the capabilities of the human-to-machine interface device, and the level of expertise of the user. The specific configuration of the control interface is designed to optimize the interaction between the user and the robotic system based upon the above described criteria. | 04-18-2013 |
20130218335 | PROCEDURAL MEMORY LEARNING AND ROBOT CONTROL - Methods and apparatus for procedural memory learning to control a robot by demonstrating a task action to the robot and having the robot learn the action according to a similarity matrix of correlated values, attributes, and parameters obtained from the robot as the robot performs the demonstrated action. Learning is done by an artificial neural network associated with the robot controller, so that the robot learns to perform the task associated with the similarity matrix. Extended similarity matrices can contain integrated and differentiated values of variables. Procedural memory learning reduces overhead in instructing robots to perform tasks. Continued learning improves performance and provides automatic compensation for changes in robot condition and environmental factors. | 08-22-2013 |