Shotbot change background color verification#

Therefore, our proposed verification routine is suitable for the bound iden- tification of Justin’s TCP end-pose errors.

Shotbot change background color registration#

Experimental results showed that the estimated errors by using 3D registration are consistent with this ground truth. The errors estimated by using this tracking system are considered as the ground truth. This method re- quired a robust calibration of tracking targets to Justin’s head and hands. To evaluate the errors identified by using 3D registration, we implemented a marker-based pose estimation method with the ARTrack IR Optical tracking system. The bounds of the TCP end-pose errors are identified as the maximum translational and rotational er- ror from the set of random poses. The verification routine consists in repeating this procedure with N -random poses. This estimate is compared to the measured TCP end-pose obtained by computing the forward kinematics of the arm using the joint angle measurements. The registration of a random point cloud of the hand to the model generates an estimate of the TCP end-pose. The partial models are created by applying an extended metaview registration method to the remaining subset of point clouds. We proposed a method for the rejection of non-overlapping point clouds based on a statistical analysis of the depth values. Partial models are generated by creating subsets of overlapping point clouds.

Shotbot change background color full#

Be- cause the hand is a self-occluding object a full model is not achievable. The hand’s CAD (Computer-Aided- Design) model does not accurately reflect the data observed by the stereo vision system, therefore we generate models of the hand by registering multiple 3D point clouds. A model of the hand is required for pose estimation. The error identi- fication is based on the pose estimation of 3D point clouds of Justin’s hand using state-of- the-art 3D registration techniques. This thesis proposes a verification routine for the identification of the maximum bounds of the TCP end-pose errors by using the on-board stereo vision system.

The identification of a maximum TCP end-pose error is essential for object manipulation and path planning. However, this approximation is insufficient due to remaining unidentified errors and flexibilities in the joints and links. Torque sensors are used to approximate the deflections of the joints and compensate the TCP end-pose errors. This light-weight design en- ables the robot to achieve a compliant behavior of the arm kinematic chain, with the draw-back of having a low positioning accuracy at the TCP (Tool-Center-Point) end-pose of the hand. To ensure safety and mobility they are built with light-weight structures and flexible mechanical components. Humanoid robots such as DLR’s Justin were designed to interact with unknown environ- ments and cooperate with humans.