Projective Virtual Reality Freund and Rossmann Summarized by Geb Thomas

Objective Create a scalable approach to graphically oriented man-manchine interfaces Suited for robotics and automation in a factory environment and for robot control over long distances

Projective Virtual Reality “project” actions from the virtual world into the real world through robots VR automatically translates actions in VR to change the real world Changes in the real world change things in VR

Basic of Projective VR Quality of man machine interface depends on the user “The less the user in the virtual world needs to know about the means of automation which carry out the task physically, the better is the design of the man machine interface.”

A Straightforward Approach Teleoperation or hand-tracking mode directly track the data-glove with the robot’s TCP flexible, OK if there is no direct contact Three modes: –training –generating robot programs –direct on-line connection

Disadvantages of Teleoperation Time delays precision of graphical model precise position and orientation information hand tremble Online collision avoidance is necessary Versatile sensor control is necessary to avoid unwanted tensions

Task Deduction Inspired by listening to transcripts from ground control “move sample A into slot B” or “replace battery in facility C” Objective: give commands to the robot system on different levels of abstraction Subtasks are automatically recognized through task deduction

Principles of Task Deduction 3 Different classes of action –interactions with the environment –events related to user movements –events related to VR/robot communication

Advantages User is out of “realtime control loop” Subtasks are carried out safely with sensor control Sensors compensate for VR inaccuracies Accuracy of data-glove less important IRCS planning allows multi-robot coordination Multi users No longer a need to show robots Multi automation controlled in a single environment

Limitation Must know the type of task in advance Example tasks set by German Space Agency were 95% successful

Action Planning Translate high level goals to specific actions generates an ordered set of elementary actions Demands are represented by a constraint net Selection is performed by task’s subject or by a selection rule

Images and Metaphors Simplifying the Work Locations of physical objects Visualizing Sensor Information Placement Aids Virtual Time TV-view into reality