Research Article
A Fusion Method of Local Path Planning for Mobile Robots Based on LSTM Neural Network and Reinforcement Learning
Table 4
Orthogonal experimental design and results.
| No. | LR | BS | TT | HL | Training set | Test set | Success rate (%) | R2 | RMSE | Loss | R2 | RMSE | Loss |
| 1 | 0.1 | 10 | 200 | L128 | 0.6246 | 0.0944 | 0.0089 | 0.66 | 0.0941 | 0.0089 | 31 | 2 | 0.1 | 20 | 300 | L150 | 0.1955 | 0.1383 | 0.0191 | 0.198 | 0.1445 | 0.0209 | 25 | 3 | 0.1 | 50 | 400 | L100 F50 | 0.0447 | 0.1507 | 0.0227 | 0.0739 | 0.1553 | 0.0241 | 21 | 4 | 0.1 | 100 | 500 | L128 F64 | ā0.2102 | 0.1696 | 0.0288 | ā0.2188 | 0.1782 | 0.0317 | 9 | 5 | 0.1 | 150 | 600 | L100 L50 | 0.8379 | 0.0621 | 0.0039 | 0.8082 | 0.0707 | 0.005 | 42 | 6 | 0.05 | 10 | 300 | L100 F50 | 0.5611 | 0.1021 | 0.0104 | 0.5769 | 0.105 | 0.011 | 33 | 7 | 0.05 | 20 | 400 | L128 F64 | 0.7094 | 0.0831 | 0.0069 | 0.7613 | 0.0789 | 0.0062 | 35 | 8 | 0.05 | 50 | 500 | L100 L50 | 0.8929 | 0.0504 | 0.0025 | 0.84 | 0.0646 | 0.0042 | 47 | 9 | 0.05 | 100 | 600 | L128 | 0.907 | 0.047 | 0.0022 | 0.8528 | 0.0619 | 0.0038 | 60 | 10 | 0.05 | 150 | 200 | L150 | 0.7686 | 0.0742 | 0.0055 | 0.7701 | 0.0774 | 0.006 | 28 | 11 | 0.01 | 10 | 400 | L100 L50 | 0.9327 | 0.04 | 0.0016 | 0.8741 | 0.0573 | 0.0033 | 75 | 12 | 0.01 | 20 | 500 | L128 | 0.9177 | 0.0442 | 0.002 | 0.8757 | 0.0569 | 0.0032 | 63 | 13 | 0.01 | 50 | 600 | L150 | 0.9381 | 0.0384 | 0.0015 | 0.8841 | 0.055 | 0.003 | 72 | 14 | 0.01 | 100 | 200 | L100 F50 | 0.9128 | 0.0455 | 0.0021 | 0.8714 | 0.0579 | 0.0033 | 75 | 15 | 0.01 | 150 | 300 | L128 F64 | 0.9359 | 0.039 | 0.0015 | 0.8748 | 0.0571 | 0.0033 | 92 | 16 | 0.005 | 10 | 500 | L150 | 0.9351 | 0.0393 | 0.0015 | 0.8918 | 0.0531 | 0.0028 | 80 | 17 | 0.005 | 20 | 600 | L100 F50 | 0.9336 | 0.0397 | 0.0016 | 0.8836 | 0.0551 | 0.003 | 91 | 18 | 0.005 | 50 | 200 | L128 F64 | 0.8797 | 0.0535 | 0.0029 | 0.8629 | 0.0598 | 0.0036 | 76 | 19 | 0.005 | 100 | 300 | L100 L50 | 0.945 | 0.0362 | 0.0013 | 0.8875 | 0.0541 | 0.0029 | 86 | 20 | 0.005 | 150 | 400 | L128 | 0.9463 | 0.0357 | 0.0013 | 0.8778 | 0.0564 | 0.0032 | 97 | 21 | 0.001 | 10 | 600 | L128 F64 | 0.9593 | 0.0311 | 9.67E-04 | 0.8887 | 0.0538 | 0.0029 | 88 | 22 | 0.001 | 20 | 200 | L100 L50 | 0.9213 | 0.0432 | 0.0019 | 0.8676 | 0.0587 | 0.0034 | 78 | 23 | 0.001 | 50 | 300 | L128 | 0.9107 | 0.0461 | 0.0021 | 0.8703 | 0.0581 | 0.0034 | 91 | 24 | 0.001 | 100 | 400 | L150 | 0.9053 | 0.0474 | 0.0022 | 0.8428 | 0.064 | 0.0041 | 82 | 25 | 0.001 | 150 | 500 | L100 F50 | 0.8798 | 0.0534 | 0.0029 | 0.8481 | 0.0629 | 0.004 | 74 |
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