[comp.ai.digest] robot ping-pong player in Philadelphia

humphrey@MCS.NLM.NIH.GOV (Susanne M. HUMPHREY) (01/27/88)

Among the dissertation abstracts in a forthcoming edition of AI-Related
Dissertations in SIGART Newsletter:

AN University Microfilms Order Number ADG87-14001.
AU ANDERSSON, RUSSELL LENNART.
IN University of Pennsylvania Ph.D 1987, 339 pages.
TI REAL TIME EXPERT SYSTEM TO CONTROL A ROBOT PING-PONG PLAYER.
SO DAI v48(05), SecB, pp1412.
DE Computer Science.
AB A real time "expert" control system has been designed and forms
   the nucleus of functioning robot ping-pong player.

   Robot ping-pong is underconstrained in the task specification (hit
   the ball back), and heavily constrained by the manipulator
   capabilities. The expert system must integrate the sensor data,
   robot capabilities, and task constraints to generate an acceptable
   plan of action. The robot ping-pong task demands that the planner
   anticipate environmental changes occurring during planning and
   robot motion. The inability to generate accurate, timely plans
   even in the face of a capricious environment and limited actuator
   performance would result in a nonfunctional system.

   The program must continuously update the task plan as new sensor
   data arrives, selecting appropriate modifications to the existing
   plan, rather than treating each datum independently. The difficult
   task and the stream of sensor data result in an interesting system
   architecture. The expert system operates in the symbolic and
   numeric domains, with a blackboard to enable global optimization
   by local agents. The architecture interrelates initial planning,
   temporal updating, and exception handling for robustness.

   A sensor and processing system produces three dimensional
   position, velocity, and spin vectors plus a time coordinate at 60
   Hz. Novel processing algorithms and careful attention to camera
   modeling were necessary to obtain adequate accuracy.

   A robot controller provides accurate, predictable performance
   close to the envelope of robot capabilities using modeling and
   feed-forward techniques. The controller plans motions in the
   temporal domain including specified terminal velocities, and
   supports smooth changes to motions in progress.

   The performance of the sensor subsystem, actuator and robot
   controller, and expert system have been demonstrated. The system
   successfully plays against both human and machine opponents.