Real Time Expert
System To Control A Robot Ping-Pong Player
Russel Lennart Andersson
GRASP LAB NEWS Ternary Progress Report Vol. 5, No. 1, 1987
MS-CIS-87-59 GRASP LAB 111
A real time `expert' control system has been designed and forms the nucleus of a functioning robot ping-pong player. Robot ping-pong is underconstrained in the task specifi- cation (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.