By Russell L. Andersson
This travel de strength in experimental robotics paves the best way towards realizing dynamic environments in imaginative and prescient and robotics. It describes the 1st robotic capable of play, or even beat, human ping-pong avid gamers. developing a computing device to play ping-pong used to be proposed years in the past as a very tricky challenge requiring quick, actual sensing and actuation, and the intelligence to play the sport. The study mentioned right here started as a sequence of experiments in development a real real-time imaginative and prescient process. The ping-pong laptop contains sensor and processing innovations in addition to the ideas had to intelligently plan the robot's reaction within the fraction of a moment to be had. it prospers on a relentless move of recent information. Subjectively comparing and enhancing its movement plan because the information arrives, it presages destiny robotic platforms with many joints and sensors that needs to do a similar, it doesn't matter what the duty. Contents: advent. robotic Ping-Pong. procedure layout. Real-Time imaginative and prescient approach robotic Controller. professional Controller Preliminaries. specialist Controller. robotic Ping-Pong program. end. Russell L. Andersson is Member of Technical employees, Robotics platforms examine division, AT&T Bell Laboratories. A robotic Ping-Pong participant is integrated within the synthetic Intelligence sequence, edited through Patrick Winston and Michael Brady.
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Extra info for A Robot Ping-Pong Player: Experiments in Real-Time Intelligent Control
The resulting velocity is affected by the local coefficient of friction, local table orientation (all tables are warped to some extent) , and by the presence of the seam. Robot Ping-Pong 19 As should be apparent, it is difficult to predict the ball's exact velocity after a bounce, even with completely accurate trajectory information. We originally thought that spin could be regarded as a second-order effect, instead, the spin critically affects the bounce off the table (and paddle) . We will now derive the physics of a ball's bounce off of a table, generalizing the results of Brody [ 14] from 2-D to 3-D.
From this derivation we can see that any hit consequentially puts spin on the ball, but that we can not independently control it. To achieve specific spins, we must relax the constraint that a= \lVII, allowing slicing motions. Without loss of generality, we can have: v= va + V;,ans where V;,ans is perpendicular to a. If we are given V;,ans, we can subtract it from T and 0 to obtain these vectors in the slicing coordinate frame. We can then solve for the usual v. An intelligent control system can alter V;,ans to produce the spin it wants, as long as it can generate the appropriate transverse motion.
Log and Demon Scanner. Each demon contains a subroutine for each log entry it wishes to monitor. That subroutine is called with the same arguments as the printlog that generated the log entry, plus the time at which the data was logged. The demon interface calls each subroutine with the logged data as arguments, in the same order as the printlog that created the log entry (Figure 7). Also, it records the time at which the printlog was executed. The format string does not have to be parsed to determine the argument types, instead, the logged data is dumped onto the stack (by a C routine) with the same byte packing the compiler generated for the printlog call that received the data.
A Robot Ping-Pong Player: Experiments in Real-Time Intelligent Control by Russell L. Andersson