By Russell L. Andersson
This journey de strength in experimental robotics paves the best way towards realizing dynamic environments in imaginative and prescient and robotics. It describes the 1st robotic in a position to play, or even beat, human ping-pong gamers. developing a laptop to play ping-pong was once proposed years in the past as a very tricky challenge requiring quick, actual sensing and actuation, and the intelligence to play the sport. The learn stated right here begun as a sequence of experiments in development a real real-time imaginative and prescient method. The ping-pong desktop contains sensor and processing recommendations in addition to the ideas had to intelligently plan the robot's reaction within the fraction of a moment on hand. it prospers on a relentless movement of recent information. Subjectively comparing and bettering its movement plan because the facts arrives, it presages destiny robotic platforms with many joints and sensors that needs to do an identical, it doesn't matter what the duty. Contents: advent. robotic Ping-Pong. process layout. Real-Time imaginative and prescient approach robotic Controller. professional Controller Preliminaries. professional Controller. robotic Ping-Pong software. end. Russell L. Andersson is Member of Technical employees, Robotics platforms learn division, AT&T Bell Laboratories. A robotic Ping-Pong participant is integrated within the synthetic Intelligence sequence, edited by means of Patrick Winston and Michael Brady.
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Extra info for A Robot Ping-Pong Player: Experiments in Real-Time Intelligent Control
Because there are so many pixels, processing takes a long time. We should change our 48 Chapter 4 idea of what our primitive should be: rather than examInIng individual pixels, we can take a more holistic, gestalt view and examine whole areas of the picture. It may well be better to overkill with moments than use the gross underkill of pixel examination. We were able to exploit the power of VLSI technology to implement a real-time feature extractor specific to a particular feature. We hope that a catalog of high-performance vision processing chips will evolve, adapting image understanding algorithms to real-time processing.
Two adjacent papers, [3 1] and , respectively propose a quintic polynomial (but mainly linear) and a quadratic dependence of drag on velocity. We have adopted the quadratic assumption, which appears better supported and has at least some support from our experimental data. 3 G, though the value is quite speed sensitive. Spin affects the flight of the ball according to the Magnus Effect, named after its inventor (according to Lord Rayleigh  in the late 1800's) . Even now, the Magnus Effect appears poorly understood, as even the sign of the force can change with speed [131.
The chip was designed using the MULGA symbolic layout system [681. 5 micron CMOS process, contains 10,2 14 transistors, and is packaged in a 40 pin ceramic DIP. A high-level view of the chips is shown in Figure 9. On one side, the chip connects to a digitized video source. An eight bit gray-scale value and a two bit sync code constitute the video input. Data input is synchronized to a two phase non-overlapping clock. The sync code represents the pixel type as follows: 00 - image pixel, 0 1 - horizontal sync, 1 0 - vertical sync, and 1 1 - non-image blanking pixel.