Path Planning and Tracking Control Method of Deep-Sea Landing Vehicle

doi:10.12382/bgxb.2022.0089

Abstract

Abstract:

A variable parameter ant colony optimization algorithm and an adaptive weight model predictive control algorithm are proposed to optimize the length of the path planned and improve the tracking accuracy of the deep-sea landing vehicle (DSLV) which operates autonomously on the complex seabed. The heuristic operator and pheromone evaporation factor of ant colony optimization are improved to reduce the length of the planned path and the number of iterations required to find the optimal path. Then, the prediction model is established based on the DSLV kinematics equation, and the idea of adaptive weight adjustment is introduced into the tracking objective function. The simulation results show that the planned path length is reduced by 4.60% and the tracking accuracy is improved by 47.6%. Compared with traditional methods, the proposed algorithms have better performance, realizing short-distance path planning and high-precision tracking.

Key words: deep-sea landing vehicle, variable parameter ant colony optimization, adaptive weight, model predictive control, path tracking

CLC Number:

TP242.3

ZHOU Qiu, ZHOU Yue, SUN Hongming, GUO Wei, WU Kai, LAN Yanjun. Path Planning and Tracking Control Method of Deep-Sea Landing Vehicle[J]. Acta Armamentarii, 2023, 44(1): 298-306.

Figures/Tables 12

Fig.1 Schematic diagram of the dual motor drive

Fig.2 Diagram of DSLV differential motion

Fig.3 Overall design framework of VACO-AWMPC

Fig.4 Schematic diagram of MPC path tracking

Fig.5 Comparison of path planning results

Table 1 Comparison of mean values of simulation results

算法及减少率	平均路径长度/m	平均收敛迭代次数
ACO	46.70	99
VACO	44.53	43
减少率/%	4.60	56.57

Table 2 Parameters and configuration of the predictive model

参数	数值	参数	数值
d/m	1.37	$u ˜ m a x$	[1.5,0.8]
D/m	1.61	Δ $u ˜ m i n$	[-0.01,-0.05]
r_d/m	0.1	Δ $u ˜ m a x$	[0.01,0.05]
N_c	5	ξ_max	[30,30,∞]
N_p	10	ξ_min	[0,0,-∞]
Q	diag(6,6,0.1)	L/m	0.5
T/s	0.23	a₁	40
v_r/(m·s^-1)	0.35	b₁	7.41
$u ˜ m i n$	[-1.5,-0.8]	s₁、s_r	0.13

Table 2 Parameters and configuration of the predictive model

参数	数值	参数	数值
d/m	1.37	$u ˜ m a x$	[1.5,0.8]
D/m	1.61	Δ $u ˜ m i n$	[-0.01,-0.05]
r_d/m	0.1	Δ $u ˜ m a x$	[0.01,0.05]
N_c	5	ξ_max	[30,30,∞]
N_p	10	ξ_min	[0,0,-∞]
Q	diag(6,6,0.1)	L/m	0.5
T/s	0.23	a₁	40
v_r/(m·s^-1)	0.35	b₁	7.41
$u ˜ m i n$	[-1.5,-0.8]	s₁、s_r	0.13

Fig.6 Comparison of path tracking results

Fig.7 Comparison of state quantity tracking control effects

Fig.8 Comparison of state quantity error change

Fig.9 Comparison of control quantity change effect

Fig.10 Comparison of tracking distance error

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