Nicewarner, Keith E. | Search NASA

NASA NTRS1992

Reliable vision-guided grasping

Automated assembly of truss structures in space requires vision-guided servoing for grasping a strut when its position and orientation are uncertain. This paper presents a methodology for efficient and robust vision-guided robot grasping alignment. The vision-guided grasping problem is related to vision-guided 'docking' problems. It differs from other hand-in-eye visual servoing problems, such as tracking, in that the distance from the target is a relevant servo parameter. The methodology described in this paper is hierarchy of levels in which the vision/robot interface is decreasingly 'intelligent,' and increasingly fast. Speed is achieved primarily by information reduction. This reduction exploits the use of region-of-interest windows in the image plane and feature motion prediction. These reductions invariably require stringent assumptions about the image. Therefore, at a higher level, these assumptions are verified using slower, more reliable methods. This hierarchy provides for robust error recovery in that when a lower-level routine fails, the next-higher routine will be called and so on. A working system is described which visually aligns a robot to grasp a cylindrical strut. The system uses a single camera mounted on the end effector of a robot and requires only crude calibration parameters. The grasping procedure is fast and reliable, with a multi-level error recovery system.

Nicewarner, Keith E.↗

NASA NTRS1992

Efficient visual grasping alignment for cylinders

Monocular information from a gripper-mounted camera is used to servo the robot gripper to grasp a cylinder. The fundamental concept for rapid pose estimation is to reduce the amount of information that needs to be processed during each vision update interval. The grasping procedure is divided into four phases: learn, recognition, alignment, and approach. In the learn phase, a cylinder is placed in the gripper and the pose estimate is stored and later used as the servo target. This is performed once as a calibration step. The recognition phase verifies the presence of a cylinder in the camera field of view. An initial pose estimate is computed and uncluttered scan regions are selected. The radius of the cylinder is estimated by moving the robot a fixed distance toward the cylinder and observing the change in the image. The alignment phase processes only the scan regions obtained previously. Rapid pose estimates are used to align the robot with the cylinder at a fixed distance from it. The relative motion of the cylinder is used to generate an extrapolated pose-based trajectory for the robot controller. The approach phase guides the robot gripper to a grasping position. The cylinder can be grasped with a minimal reaction force and torque when only rough global pose information is initially available.

Nicewarner, Keith E.↗

NASA NTRS1992

Reliable vision-guided grasping

Automated assembly of truss structures in space requires vision-guided servoing for grasping a strut when its position and orientation are uncertain. This paper presents a methodology for efficient and robust vision-guided robot grasping alignment. The vision-guided grasping problem is related to vision-guided 'docking' problems. It differs from other hand-in-eye visual servoing problems such as tracking in that the distance from the target is a relevant servo parameter. The methodology described in this paper is a hierarchy of levels in which the vision/robot interface is decreasingly 'intelligent', and increasingly fast. Speed is achieved primarily by information reduction. This reduction exploits the use of region-of-interest windows in the image plane and feature motion prediction. These reductions invariably require stringent assumptions about the image. Therefore, at a higher level, these assumptions are verified using slower, more reliable methods. This hierarchy provides for robust error recovery in that when a lower-level routine fails, the next-higher routine will be called and so on. A working system is described which visually aligns a robot to grasp a cylindrical strut. The system uses a single camera mounted on the end effector of a robot and requires only crude calibration parameters. The grasping procedure is fast and reliable, with a multi-level error recovery system.

Nicewarner, Keith E.↗

NASA NTRS1991

Efficient visual grasping alignment for cylinders

Monocular information from a gripper-mounted camera is used to servo the robot gripper to grasp a cylinder. The fundamental concept for rapid pose estimation is to reduce the amount of information that needs to be processed during each vision update interval. The grasping procedure is divided into four phases: learn, recognition, alignment, and approach. In the learn phase, a cylinder is placed in the gripper and the pose estimate is stored and later used as the servo target. This is performed once as a calibration step. The recognition phase verifies the presence of a cylinder in the camera field of view. An initial pose estimate is computed and uncluttered scan regions are selected. The radius of the cylinder is estimated by moving the robot a fixed distance toward the cylinder and observing the change in the image. The alignment phase processes only the scan regions obtained previously. Rapid pose estimates are used to align the robot with the cylinder at a fixed distance from it. The relative motion of the cylinder is used to generate an extrapolated pose-based trajectory for the robot controller. The approach phase guides the robot gripper to a grasping position. The cylinder can be grasped with a minimal reaction force and torque when only rough global pose information is initially available.

Nicewarner, Keith E.↗

NASA NTRS1991

Vision-guided gripping of a cylinder

The motivation for vision-guided servoing is taken from tasks in automated or telerobotic space assembly and construction. Vision-guided servoing requires the ability to perform rapid pose estimates and provide predictive feature tracking. Monocular information from a gripper-mounted camera is used to servo the gripper to grasp a cylinder. The procedure is divided into recognition and servo phases. The recognition stage verifies the presence of a cylinder in the camera field of view. Then an initial pose estimate is computed and uncluttered scan regions are selected. The servo phase processes only the selected scan regions of the image. Given the knowledge, from the recognition phase, that there is a cylinder in the image and knowing the radius of the cylinder, 4 of the 6 pose parameters can be estimated with minimal computation. The relative motion of the cylinder is obtained by using the current pose and prior pose estimates. The motion information is then used to generate a predictive feature-based trajectory for the path of the gripper.

Nicewarner, Keith E.↗