Prediction-Based Assembly Assistance System

An Empirical Evaluation of Prediction by Partial Matching in Assembly Assistance Systems

“…After evaluating the two implementations of the PPM algorithm, with and without neighboring states, it can be observed that the optimal order for both algorithms is 3. The optimal configurations of these two algorithms will now be compared with two prediction methods presented and evaluated in previous works: the Markov model with padding [7], enhanced to use human characteristics, and the LSTM [8]. For the Markov model with padding, the optimal order is 2.…”

“…Unfortunately, this scheme provided insignificant improvement, and because it used supplementary information and additional steps in the prediction process, it was considered less efficient than the scheme without automata. The Markov predictor presented in [6,7] is another two-level predictor which can store multiple next states, together with their number of occurrences for each pattern. The state with the highest number of occurrences is extracted from the prediction table entry selected with the left-shift state register and is then provided as the predicted one.…”

“…. , q t−R ], where q t is the state at time t. As presented in [6,7], the prediction with Markov models can be performed using a table trained with past data, which stores pairs of patterns of a certain length and the corresponding next states, together with their occurrence frequencies. Figure 4 presents the structure of a Markov predictor of order R. The patterns in this work are composed of human worker characteristics (C 1 , .…”

“…The PPM's prediction mechanism is depicted in Figure 5. For the PPM implementation, the Markov predictor with padding (presented in [7]) is used. The Markov predictor was enhanced to also use the workers' characteristics.…”

“…In the previous works [5][6][7][8], several context-based predictors were investigated. These predictors provide assembly instructions based on the current and some past assembly states.…”