基于改进鲸鱼优化算法的AUV三维路径规划

doi:10.12305/j.issn.1001-506X.2023.07.28

Abstract

Abstract:

Autonomous underwater vehicle (AUV) have become one kind of the most effective equipment for multiple underwater operations in different fields. A solution method for AUV global path planning is proposed based on improved whale optimization algorithm. Firstly, for the modelling of the problem, in the process of environment modelling, in view of the complexity of waypoint setting in 3D space, a modelling method based on the connected rapidly-exploring random tree (RRT-Connect) is presented; in the mathematical optimization model, three evaluation criteria including the path smoothness, the diving gradient and the navigation time are integrated, and the relevant constraints caused by strong currents and obstacles are considered. Then for the above models, an improved whale optimization algorithm is proposed. The optimization idea based on the linkage structure of the problem is introduced. And families of key subsets and effective subsets are constructed online according to it, which are adopted to discover the linkage sets with high criticality and effectiveness in real time, and increase the probability of their reuse rate, so as to improve the convergence speed and accuracy of proposed algorithm. In addition, to take advantage of historical evolution information more comprehensively and effectively, the method of constructing individual leaders with multiple learning sets and the corresponding joint guidance strategies are designed to further enhance the overall performance of the algorithm. Finally, according to the practical seabed terrain information and different ocean current models, several path planning scenarios are set up for simulation experiments. The results show that compared with other whale optimization algorithms and classical intelligent algorithms presented in references, the proposed algorithm is more superior in terms of solving accuracy, stability and convergence speed, which better meet the requirements of AUV path planning.

Key words: path planning, whale optimization algorithm (WOA), autonomous underwater vehicle (AUV), environment modeling

CLC Number:

TP242

Guangqiang LI, Wenchao DONG, Daqing ZHU, Yue YU, Hao CHEN, Shuanghe YU. 3D path planning for AUV based on improved whaleoptimization algorithm[J]. Systems Engineering and Electronics, 2023, 45(7): 2170-2182.

Figures/Tables 10

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References 23

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情形	地形	海流	起始点/km	目标点/km
1	a	m	(2 758.82, 415.89, -1.43)	(2 964.60, 301.25, -4.01)
2	b	n	(1 910.45, 236.25, -0.47)	(2 113.89, 351.25, -3.16)
3	a	n	(2 758.82, 415.89, -3.09)	(2 964.60, 301.25, -4.01)
4	b	m	(1 910.45, 236.25, -1.98)	(2 113.89, 351.25, -3.16)

情形	路径点数	WOA	BMWOA	IWOSSA	HWOAG	PSO	DE	IWOA
1	5	336 161.0	336 414.8	366 179.6	358 860.0	339 030.2	335 388.0	335 451.2
	10	341 506.8	336 081.4	428 444.0	395 198.4	344 247.0	335 874.2	333 080.8
	15	656 472.4	491 059.8	#	498 216.6	458 622.6	341 501.6	332 501.4
2	6	242 808.8	241 502.8	241 340.0	249 229.2	312 066.2	241 209.0	242 209.8
	12	272 312.4	267 106.2	271 696.6	268 900.2	#	239 711.4	238 370.6
	18	#	#	#	#	#	262 821.6	237 803.2
3	6	276 393.6	276 022.8	277 964.8	279 114.0	276 084.8	274 896.6	274 514.0
	12	293 664.4	292 704.6	293 966.2	291 643.0	295 763.0	268 731.4	267 984.8
	18	#	#	#	#	#	270 212.2	266 882.4
4	7	354 772.8	349 677.8	365 973.0	376 550.0	350 856.0	343 702.8	345 397.0
	14	348 429.8	383 541.2	430 427.4	419 801.8	349 842.8	345 915.6	343 171.4
	21	463 174.0	780 443.4	#	437 306.6	369 718.6	353 991.0	344 885.4

情形	路径点数	WOA	BMWOA	IWOSSA	HWOAG	PSO	DE	IWOA
1	5	37 120	34 545	89 620	93 010	34 460	34 670	23 790
2	12	53 690	51 458	95 821	91 161	#	67 350	21 555
3	12	71 522	87 215	93 630	92 012	73 345	51 180	18 185
4	21	59 583	89 338	#	12 240	23 969	24 290	15 205

情形	路径点数	性能指标	WOA	BMWOA	IWOSSA	HWOAG	PSO	DE	IWOA
1	5	成功率/%	100	100	100	100	100	100	100
	5	相关标准差	15 220.0	8 523.0	10 316.2	57 898.4	28 723.2	4 149.2	412.2
	10	成功率/%	100	100	50	100	100	100	100
	10	相关标准差	84 120.0	96 531.8	110 657.2	33 609.4	85 818.8	11 422.2	2 700.0
	15	成功率/%	90	90	0	100	50	100	100
	15	相关标准差	37 331.8	104 553.8	#	13 016.0	103 182.4	18 616.4	7 307.1
2	6	成功率/%	50	90	30	40	20	100	100
	6	相关标准差	6 953.8	1 169.4	7 935.6	9 259.2	10 552.6	881.8	385.4
	12	成功率/%	20	40	30	30	0	90	100
	12	相关标准差	79 273.2	76 167.8	106 734.6	25 870.6	#	3 844.2	457.6
	18	成功率/%	0	0	0	0	0	50	90
	18	相关标准差	#	#	#	#	#	58 781.6	4 809.2
3	6	成功率/%	30	30	40	30	20	100	100
	6	相关标准差	3 187.6	5 630.0	7 450.2	2 852.6	3 867.0	382.4	338.0
	12	成功率/%	30	20	30	30	20	100	100
	12	相关标准差	57 039.6	63 058.2	94 418.4	26 315.4	87 847.8	1 475.4	699.2
	18	成功率/%	0	0	0	0	0	60	100
	18	相关标准差	#	#	#	#	#	15 598.6	1 074.1
4	7	成功率/%	100	100	100	100	90	100	100
	7	相关标准差	15 595.6	14 198.8	9 887.6	9 218.0	8 257.4	1 740.0	1 185.2
	14	成功率/%	90	80	70	90	50	100	100
	14	相关标准差	51 063.6	90 897.0	71 124.6	11 736.0	61 446.6	6 098.2	6 858.7
	21	成功率/%	70	60	0	100	40	100	100
	21	相关标准差	109 533.2	91 635.6	#	28 546.6	55 493.0	16 471.6	13 157.1

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3D path planning for AUV based on improved whaleoptimization algorithm

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情形	基本路径点数目	倍增路径点数目
情形	基本路径点数目	1倍	2倍	3倍
1	5	5	10	15
2	6	6	12	18
3	6	6	12	18
4	7	7	14	21