Research Article
Potential Odor Intensity Grid Based UAV Path Planning Algorithm with Particle Swarm Optimization Approach
Table 6
Results of the paired samples
-test.
(a) -test of cost values |
| | Paired differences | | df | Sig. (2-tailed) | | Mean | Std. deviation | Std. error mean | 95% confidence interval of the difference | | Lower | Upper |
| Pair 1 proposed method: PSO | −.05496 | .04522 | .01011 | −.07612 | −.03379 | −5.435 | 19 | .000 | Pair 2 proposed method: FA | −.42184 | .04838 | .01082 | −.44448 | −.39919 | −38.996 | 19 | .000 | Pair 3 proposed method: GA | −.06850 | .02255 | .00504 | −.07905 | −.05794 | −13.582 | 19 | .000 |
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(b) -test of planned path lengths |
| | Paired differences | | df | Sig. (2-tailed) | | Mean | Std. deviation | Std. error mean | 95% confidence interval of the difference | | Lower | Upper |
| Pair 1 proposed method: PSO | −13.54855 | 9.27905 | 2.07486 | −17.89128 | −9.20582 | −6.530 | 19 | .000 | Pair 2 proposed method: FA | −34.36477 | 19.56367 | 4.37457 | −43.52085 | −25.20869 | −7.856 | 19 | .000 | Pair 3 proposed method: GA | −18.12088 | 4.41683 | .98763 | −20.18802 | −16.05374 | −18.348 | 19 | .000 |
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