Capturing robot workspace structure: representing robot capabilities

Humans have at some point learned an abstraction of the capabilities of their arms. By just looking at the scene they can decide which places or objects they can easily reach and which are difficult to approach. Possessing a similar abstraction of a robot arm's capabilities in its workspace is important for grasp planners, path planners and task planners. In this paper, we show that robot arm capabilities manifest themselves as directional structures specific to workspace regions. We introduce a representation scheme that enables to visualize and inspect the directional structures. The directional structures are then captured in the form of a map, which we name the capability map. Using this capability map, a manipulator is able to deduce places that are easy to reach. Furthermore, a manipulator can either transport an object to a place where versatile manipulation is possible or a mobile manipulator or humanoid torso can position itself to enable optimal manipulation of an object.

Authors

• Franziska Zacharias
• Christoph Borst 0001
• Gerhard Hirzinger

Keywords

• Humans
• Kinematics
• Intelligent robots
• Layout
• Humanoid robots
• Visualization
• Design optimization
• Manipulator dynamics
• Robotic assembly
• USA Councils
• Robot Capabilities
• Robot Workspace
• Path Planning
• Robotic Arm
• Form Of Map
• Mobile Manipulator
• Cylindrical
• Random Sampling
• Ideal Value
• Singular Value
• Dexterity
• Global Index
• Preferred Direction
• Jacobian Matrix
• Specific Direction
• Configuration Space
• Open Angle
• Cartesian Space
• Humanoid Robot
• Inverse Kinematics
• Robot Design
• Inverse Solution
• Translational Velocity
• Red Spheres
• Shape Fitting
• Center Of The Sphere
• Minimum Singular Value