Abstract:
A machine vision system for a controllable robotic device proximal to a workspace includes an image acquisition sensor arranged to periodically capture vision signal inputs each including an image of a field of view including the workspace. A controller operatively couples to the robotic device and includes a non-transitory memory component including an executable vision perception routine. The vision perception routine includes a focus loop control routine operative to dynamically track a focus object in the workspace and a background loop control routine operative to monitor a background of the workspace. The focus loop control routine executes simultaneously asynchronously in parallel with the background loop control routine to determine a combined resultant including the focus object and the background based upon the periodically captured vision signal inputs. The controller is operative to control the robotic device to manipulate the focus object based upon the focus loop control routine.
Abstract:
A machine vision system for a controllable robotic device proximal to a workspace includes an image acquisition sensor arranged to periodically capture vision signal inputs each including an image of a field of view including the workspace. A controller operatively couples to the robotic device and includes a non-transitory memory component including an executable vision perception routine. The vision perception routine includes a focus loop control routine operative to dynamically track a focus object in the workspace and a background loop control routine operative to monitor a background of the workspace. The focus loop control routine executes simultaneously asynchronously in parallel with the background loop control routine to determine a combined resultant including the focus object and the background based upon the periodically captured vision signal inputs. The controller is operative to control the robotic device to manipulate the focus object based upon the focus loop control routine.
Abstract:
A method of training a robot to autonomously execute a robotic task includes moving an end effector through multiple states of a predetermined robotic task to demonstrate the task to the robot in a set of n training demonstrations. The method includes measuring training data, including at least the linear force and the torque via a force-torque sensor while moving the end effector through the multiple states. Key features are extracted from the training data, which is segmented into a time sequence of control primitives. Transitions between adjacent segments of the time sequence are identified. During autonomous execution of the same task, a controller detects the transitions and automatically switches between control modes. A robotic system includes a robot, force-torque sensor, and a controller programmed to execute the method.
Abstract:
A robotic system includes an end-effector and a control system. The control system includes a processor, a dynamical system module (DSM), and a velocity control module (VCM). Via execution of a method, the DSM processes inputs via a flow vector field and outputs a control velocity command. The inputs may include an actual position, desired goal position, and demonstrated reference path of the end-effector. The VCM receives an actual velocity of the end-effector and the control velocity command as inputs, and transmits a motor torque command to the end-effector as an output command. The control system employs a predetermined set of differential equations to generate a motion trajectory of the end-effector in real time that approximates the demonstrated reference path. The control system is also programmed to modify movement of the end-effector in real time via the VCM in response to perturbations of movement of the end-effector.
Abstract:
A method for localizing and estimating a pose of a known object in a field of view of a vision system is described, and includes developing a processor-based model of the known object, capturing a bitmap image file including an image of the field of view including the known object, extracting features from the bitmap image file, matching the extracted features with features associated with the model of the known object, localizing an object in the bitmap image file based upon the extracted features, clustering the extracted features of the localized object, merging the clustered extracted features, detecting the known object in the field of view based upon a comparison of the merged clustered extracted features and the processor-based model of the known object, and estimating a pose of the detected known object in the field of view based upon the detecting of the known object.
Abstract:
A method for localizing and estimating a pose of a known object in a field of view of a vision system is described, and includes developing a processor-based model of the known object, capturing a bitmap image file including an image of the field of view including the known object, extracting features from the bitmap image file, matching the extracted features with features associated with the model of the known object, localizing an object in the bitmap image file based upon the extracted features, clustering the extracted features of the localized object, merging the clustered extracted features, detecting the known object in the field of view based upon a comparison of the merged clustered extracted features and the processor-based model of the known object, and estimating a pose of the detected known object in the field of view based upon the detecting of the known object.
Abstract:
A robotic system includes an end-effector and a control system. The control system includes a processor, a dynamical system module (DSM), and a velocity control module (VCM). Via execution of a method, the DSM processes inputs via a flow vector field and outputs a control velocity command. The inputs may include an actual position, desired goal position, and demonstrated reference path of the end-effector. The VCM receives an actual velocity of the end-effector and the control velocity command as inputs, and transmits a motor torque command to the end-effector as an output command. The control system employs a predetermined set of differential equations to generate a motion trajectory of the end-effector in real time that approximates the demonstrated reference path. The control system is also programmed to modify movement of the end-effector in real time via the VCM in response to perturbations of movement of the end-effector.
Abstract:
A method of training a robot to autonomously execute a robotic task includes moving an end effector through multiple states of a predetermined robotic task to demonstrate the task to the robot in a set of n training demonstrations. The method includes measuring training data, including at least the linear force and the torque via a force-torque sensor while moving the end effector through the multiple states. Key features are extracted from the training data, which is segmented into a time sequence of control primitives. Transitions between adjacent segments of the time sequence are identified. During autonomous execution of the same task, a controller detects the transitions and automatically switches between control modes. A robotic system includes a robot, force-torque sensor, and a controller programmed to execute the method.
Abstract:
A robotic system includes a robot, sensors which measure status information including a position and orientation of the robot and an object within the workspace, and a controller. The controller, which visually debugs an operation of the robot, includes a simulator module, action planning module, and graphical user interface (GUI). The simulator module receives the status information and generates visual markers, in response to marker commands, as graphical depictions of the object and robot. An action planning module selects a next action of the robot. The marker generator module generates and outputs the marker commands to the simulator module in response to the selected next action. The GUI receives and displays the visual markers, selected future action, and input commands. Via the action planning module, the position and/or orientation of the visual markers are modified in real time to change the operation of the robot.