面向桁架抓持的三分支机器人构型优化方法

doi:10.7527/S1000-6893.2024.31033

Abstract

Abstract:

With the advancement of space exploration and technology， the on-orbit construction of large spacecraft has become a key research focus. These spacecraft typically use large space trusses as support structures and rely on space robots to carry out various on-orbit transportation and assembly tasks. Due to the characteristics of the truss structures， which have high flexibility and low damping， vibrations in the truss structure can be easily induced by time-varying dynamics when the space robot climbs and grasps the truss. This can affect the stability of load transportation. To address the holding configuration optimization problem for a three-branchrobot climbing a truss， the “truss-three-branch robot” composite system was used as the research object to analyze its contact and collision characteristics. Evaluation metrics for climbing stability， holding balance， and operability were constructed for the holding configuration. The NSGA-Ⅱ （Non-dominated Sorting Genetic Algorithm-Ⅱ） multi-objective optimization algorithm was used to establish a holding configuration optimization model with the joint angles of the three-branch robot as decision variables. This approach provided an optimized method for holding configuration that balanced contact collision excitation suppression， task efficiency， and robot operability. Finally， the effectiveness of this method was evaluated through comparative simulation experiments， offering analytical means and solutions for the smooth planning of truss climbing motions in large spacecraft.

Key words: three-branch robot, large spacecraft climbing, holding configuration optimization, contact impact impulse, multi-objective optimization

CLC Number:

Yifan WANG, Xiyun GUO, Shiyuan JIA, Gang CHEN, Mo REN. Configuration optimization method of three-branch robot for truss holding[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(7): 431033.

Figures/Tables 13

Fig.1

Fig.2

Table 1

D-H parameter table of three-branch robot

关节 $i$	$α i / (°)$	$a i / m$	$θ i / (°)$	$d i / m$
1	90	0	$θ 1$	$a 0$
2	-90	0	$θ 2$	$a 1$
3	0	0	$- 90 + θ 3$	$a 2$
4	0	$a 3$	$θ 4$	0
5	90	$a 5$	$90 + θ 5$	$a 4$
6	-90	0	$θ 6$	$a 6$
7	90	0	$θ 7$	$a 7$
8	0	$2 l c o s 30 °$	$θ 8$	0
9	90	0	$θ 9$	$a 8$
10	-90	0	$θ 10$	$a 9$
11	0	$a 10$	$- 90 + θ 11$	$a 11$
12	0	$a 12$	$θ 12$	0
13	-90	0	$- 90 + θ 13$	$a 13$
14	-90	0	$θ 14$	$a 14$
$8'$	0	$l c o s 30 °$	$180 + θ 8'$	$1.5 l$
$9'$	90	0	$θ 9'$	$a 8'$
$10'$	-90	0	$θ 10'$	$a 9'$
$11'$	0	$a 10'$	$- 90 + θ 11'$	$a 11'$
$12'$	0	$a 12'$	$θ 12'$	0
$13'$	-90	0	$- 90 + θ 13'$	$a 13'$
$14'$	-90	0	$θ 14'$	$a 14'$

Table 1

Fig.3

Fig.4

Fig.5

Table 2

Spacecraft truss and robot related parameters

参数类型	数值
桁架单元尺寸	2.5 m×1.25 m×1.25 m
桁杆尺寸	外径60 mm，内径55 mm
桁架单元质量	113.291 758 kg
三分支机器人连杆长度	$a 0 = a 15 = a 15' = 0.13 m a 1 = a 14 = a 14' = 0.5 m a 2 = a 13 = a 13' = 0.5 m a 3 = a 12 = a 12' = 1 m a 4 = a 11 = a 11' = 0.2 m a 5 = a 10 = a 10' = 1 m a 6 = a 9 = a 9' = 0.5 m a 7 = a 8 = a 8' = 0.5 m l = 0.5 m$
三分支机器人连杆质量	$m 0 = m 15 = m 15' = 2.915 k g m 1 = m 14 = m 14' = 4.428 k g m 2 = m 13 = m 13' = 4.428 k g m 3 = m 12 = m 12' = 8.855 k g m 4 = m 11 = m 11' = 1.528 k g m 5 = m 10 = m 10' = 8.855 k g m 6 = m 9 = m 9' = 4.428 k g m 7 = m 8 = m 8' = 4.428 k g m l = 17.157 k g$
三分支机器人惯性张量	$I 0 = I 15 = I 15' = d i a g 0.005 9, 0.005 9, 0.003 6 I 1 = I 14 = I 14' = d i a g 0.095 0, 0.095 0, 0.005 5 I 2 = I 13 = I 13' = d i a g 0.095 0, 0.095 0, 0.005 5 I 3 = I 12 = I 12' = d i a g 0.743 5, 0.743 5, 0.011 1 I 4 = I 11 = I 11' = d i a g 0.006 0, 0.006 0, 0.001 9 I 5 = I 10 = I 10' = d i a g 0.743 5, 0.743 5, 0.011 1 I 6 = I 9 = I 9' = d i a g 0.095 0, 0.095 0, 0.005 5 I 7 = I 8 = I 8' = d i a g 0.095 0, 0.095 0, 0.005 5 I l = d i a g 1.083 0, 1.083 0, 0.021 4$

Table 2

Table 3

NSGA-Ⅱ multi-objective optimization algorithm related parameters

参数名称	数值
优化目标	$m i n f = m i n [f 1, f 2, f 3]$
优化目标权重系数	$ζ 1 = 0.5, ζ 2 = 0.5$ $λ 1 = 0.4, λ 2 = 0.6$ $κ 1 = 0.5, κ 2 = 0.5$
种群规模	$p o p = 200$
迭代次数	$g e n = 100$
决策变量数目	$V = 21$
优化目标数目	$M = 3$
交叉概率	$p c = 0.9$
变异概率	$p m = 0.1$

Table 3

Fig.6

Fig.7

Table 4

Configuration optimization results of each group

组别	角度/（°）	$f 1 / (m · s - 1)$	$f 2 / m$	$f 3$	综合指标
0°~90°	20.81	0.098 1	1.650 4	0.115 1	0.41
90°~180°	162.75	0.082 0	1.795 2	0.075 9	0.61
180°~270°	199.26	0.107 3	1.489 4	0.044 1	0.39
270°~360°	352.99	0.113 9	1.775 0	0.069 5	0.49

Table 4

Table 5

Optimal configuration under comprehensive evaluation index

分支	角度/（°）
1	$98.95, 40.72, 158.56, 23.82, - 71.74, 48.38, - 19.31$
2	$- 22.35, - 112.53,10.28, - 42.39,63.05, - 105.86,18.06$
3	$136.50,47.49, - 54.59,96.26, - 51.85,68.63,0.73$

Table 5

Table 6

Controlled experimental results

区间	构型类型	$f 1 / (m · s - 1)$	$f 2 / m$	$f 3$
0°~90°	未经优化	0.122 5	3.235 0	0.136 7
0°~90°	经过优化	0.098 1	1.650 4	0.115 1
90°~180°	未经优化	0.136 1	3.327 3	0.125 0
90°~180°	经过优化	0.082 0	1.795 2	0.075 9
180°~270°	未经优化	0.182 5	3.512 8	0.129 6
180°~270°	经过优化	0.107 3	1.489 4	0.044 1
270°~360°	未经优化	0.116 7	3.372 4	0.164 5
270°~360°	经过优化	0.113 9	1.775 0	0.069 5

Table 6

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Configuration optimization method of three-branch robot for truss holding

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