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Dexterity workspace Analysis
Dexterity workspace Analysis
Workspace analysis is crucial in robotics for understanding the capabilities of robotic manipulators or grippers for manipulation. Here, we use the Monte Carlo method to simulate a large number of potential locations for the gripper's fingers' tip, taking into account the physical constraints imposed by the joints.
Workspace analysis is crucial in robotics for understanding the capabilities of robotic manipulators or grippers for manipulation. Here, we use the Monte Carlo method to simulate a large number of potential locations for the gripper's fingers' tip, taking into account the physical constraints imposed by the joints.
Fig.3 Visualization of the reachable workspace analysis for gripper based on Monte Carlo simulation
For visualization, Kernel Density Estimation (KDE) with a Gaussian Kernel is used to create smooth, continuous density estimates. We apply KDE to the workspace visualization of the proposed gripper, which demonstrates where the gripper is most capable of handling objects of various sizes.
Fig. 4 3D view of the dexterity workspace
Fig. 5 3D view of the dexterity workspace's cross-sectional views along the X and Y axes for objects with radius between 60 and 80 mm
For objects with radius of 0-20 mm For objects with radius of 20-40 mm For objects with radius of 40-60 mm
For objects with radius of 60-80 mm For objects with radius of 80-100 mm For objects with radius of 100-120 mm
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