Planning a Shared Modular Fixture Layout Across Robotic Disassembly Stages

Haohui Pan; Takuya Kiyokawa; Kensuke Harada

cs.ROAug 27, 2026

Planning a Shared Modular Fixture Layout Across Robotic Disassembly Stages

Authors: Haohui Pan, Takuya Kiyokawa, Kensuke Harada

Organizations: Department of Systems Innovation, Graduate School of Engineering Science, The University of Osaka, 1-3 Machikaneyama, Toyonaka, Osaka, Japan · Industrial Cyber-physical Systems Research Center, The National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26 Aomi, Koto-ku, Tokyo, Japan

Abstract

Stable support remains challenging in robotic disassembly of irregularly shaped products. As components are progressively removed, the available support surfaces, mass distribution, and task loads change throughout the process. A fixture layout designed for one workpiece state may therefore become infeasible at later stages, motivating unified support planning over the complete disassembly sequence. This paper presents a modular vacuum-based fixturing system that plans one shared support configuration for the complete disassembly sequence of a screwdriver or shaver, allowing each sequence to proceed without fixture reconfiguration. To search the mixed continuous--discrete layout space under repeated cross-stage evaluation, a denoising diffusion probabilistic model generates physics-informed initial configurations that are refined through Bayesian optimization. Robotic screw and component-removal experiments verified the disassembly feasibility of the planned layouts, while 11 directional-load tests quantified their stability. Comparisons between the measured operational loads and directional responses yielded mean empirical stability margins of 66.9% for the screwdriver and 81.6% for the shaver. These results demonstrate that a product-specific shared layout can provide stable support throughout the tested robotic disassembly sequence.

Figures & tables

Fig. 1 : Robotic disassembly workspace. (a) KUKA LBR iiwa 14 manipulator; (b) OnRobot Screwdriver and Robotiq Hand-E gripper; (c) Panasonic screwdriver and Panasonic shaver; (d) three vacuum-based balloon hands; (e) three height-adjustment modules; (f) magnetic worktable; and (g) robot table.

Fig. 2 : Physics-guided support-planning framework integrating cross-stage evaluation, DDPM refinement, and Bayesian optimization.

Fig. 3 : Predefined disassembly sequences for the screwdriver (top) and shaver (bottom). Stages 0–3 show the intact product and three successive component-removal states; arrows identify the component removed between adjacent stages.

Fig. 4 : Candidate-point classifications for the predefined object states: (a1) screwdriver, stages 0–1; (a2) screwdriver, stages 2–3; and (b) shaver, stages 0–3.

Model	$R_{\mathrm{suc}}$ (mm)	$R_{\mathrm{phy}}$ (mm)	$F_{\mathrm{suc}}$ (N)	$T_{\mathrm{suc}}$ (N $\cdot$ m)
SGB10	5.0	15.0	7.5	0.06
SGB20	10.0	21.5	23.0	0.11
SGB30	15.0	33.0	57.0	0.17
SGB40	20.0	37.5	85.0	0.23

TABLE I : Physical parameters of the balloon-hand models.

Fig. 5 : DDPM-generated support-coordinate distributions over refinement rounds R0–R5 for (a) the screwdriver and (b) the shaver.

Fig. 6 : BO search results for the screwdriver and shaver. Panels (a1) and (b1) show the cumulative counts of unique all-stage-feasible configurations; panels (a2) and (b2) show the explored support locations and selected shared layouts.

Fig. 7 : Qualitative evaluation of conventional vise fixturing for the screwdriver and shaver. p1–p4 denote successive observed poses during vise testing.

Fig. 8 : Screw-operation and component-removal experiments. The plots show the mean resultant force and moment with one-standard-deviation bands; the bars report screw-removal success rates.

		Screwdriver					Shaver
Response	Unit	Removal	Tightening	dis-1	dis-2	dis-3	dis-1	dis-2	dis-3
$\|F_{x}\|$	N	23.44	23.59	1.17	1.39	3.47	3.34	1.81	2.49
$\|F_{y}\|$	N	1.55	1.66	1.71	3.34	2.17	3.76	1.72	1.67
$\|F_{z}\|$	N	11.80	13.62	7.42	13.44	3.13	9.11	12.44	7.60
$\|F\|$	N	26.24	27.20	7.77	13.96	5.06	10.07	12.60	8.05
$\|M_{x}\|$	N $\cdot$ m	0.31	0.30	0.24	0.55	0.35	0.50	0.23	0.38

TABLE II : Mean six-axis operational peak responses during screw operations and component removal.

Fig. 9 : Directional-load responses in the 11 tests for (A) the screwdriver and (B) the shaver. Curves and bands show the mean incremental resultant loads and one standard deviation.

Direction	Peak force response (N)				Peak moment response (N $\cdot$ m)
Direction	$\|\Delta F_{x}\|$	$\|\Delta F_{y}\|$	$\|\Delta F_{z}\|$	$\|\Delta F\|$	$\|\Delta M_{x}\|$	$\|\Delta M_{y}\|$	$\|\Delta M_{z}\|$	$\|\Delta M\|$
$x\text{-p}$	6.07/14.54	36.68/45.36	13.01/70.53	37.20/79.09	5.04/5.01	1.48/0.79	2.16/2.19	5.61/5.47
$x\text{-n}$	7.65/18.52	27.38/24.72	48.80/84.86	56.13/88.75	4.62/4.86	2.72/4.16	1.45/0.35	5.46/6.03
$y\text{-p}$	28.35/36.99	9.44/15.79	42.04/42.53	50.79/52.97	2.18/3.71	4.96/5.00	0.85/0.72	5.29/6.26
$y\text{-n}$	31.12/31.94	15.05/8.45	16.09/53.55	38.64/58.02	1.55/2.42	4.86/4.96	0.90/0.40	5.12/5.45
$z\text{-p}$	9.65/6.52	7.59/4.45	25.33/32.17	27.62/32.49	1.02/0.47	1.74/0.83	0.39/0.44	2.02/1.02

TABLE III : Mean peak responses in the 11 directional-load tests, reported as screwdriver/shaver.

Operation	Translational direction					Rotational direction						Mean
Operation	$x\text{-p}$	$x\text{-n}$	$y\text{-p}$	$y\text{-n}$	$z\text{-p}$	$a\text{-p}$	$a\text{-n}$	$b\text{-p}$	$b\text{-n}$	$c\text{-p}$	$c\text{-n}$	Mean
Removal	29.5 / –	53.3 / –	48.3 / –	32.1 / –	$5.0$ / –	38.5 / –	48.8 / –	58.4 / –	55.0 / –	53.3 / –	54.4 / –	43.3 / –
Tightening	$26.9$ / –	51.5 / –	46.4 / –	29.6 / –	$1.5$ / –	39.3 / –	49.5 / –	58.9 / –	55.6 / –	53.9 / –	55.0 / –	42.6 / –
dis-1	79.1 / 87.3	86.2 / 88.7	84.7 / 81.0	79.9 / 82.6	71.9 / 69.0	89.2 / $51.0$	91.0 / 63.2	$92.7$ / 84.9	$92.1$ / 86.4	91.8 / 88.4	$92.0$ / 85.9	86.4 / 78.9
dis-2	62.5 / 84.1	75.1 / 85.8	72.5 / 76.2	63.9 / 78.3	49.5 / $61.2$	82.6 / 78.8	85.5 / 84.1	88.2 / 93.5	87.3 / $94.1$	86.8 / $95.0$	87.1 / $93.9$	76.4 / 84.1
dis-3	86.4 / 89.8	91.0 / 90.9	90.0 / 84.8	86.9 / 86.1	81.7 / 75.2	81.0 / $55.0$	84.2 / 66.2	87.1 / 86.1	86.1 / 87.5	85.6 / 89.3	85.9 / 87.1	86.0 / 81.6

TABLE IV : Direction-wise empirical stability margins

\xi_{d,o}

(%) under the unfavorable-direction assumption, reported as screwdriver/shaver.

Combination	Statistic	Stage quality				Cross-stage behavior			Objective distribution		Best new support
Combination	Statistic	$Q_{0}$	$Q_{1}$	$Q_{2}$	$Q_{3}$	$r_{4/4}$ (%)	$\bar{r}_{\mathrm{feas}}^{\,10\%}$	$\bar{v}^{\,10\%}$	$\bar{J}_{k}^{\,10\%}$	$\bar{J}_{k}^{\mathrm{top100}}$	$Q_{\mathrm{mean}}^{\mathrm{new},\ast}$
J1 (0.60,0.70,0.60)	Trimmed mean	0.977	0.982	0.183	0.548	51.5	0.795	0.123	0.604	0.820	–
J1 (0.60,0.70,0.60)	Best new	0.966	0.973	0.567	0.939	–	–	–	–	–	0.861
J2 (0.50,0.70,0.60)	Trimmed mean	0.979	0.984	$0.148$	$0.471$	$43.2$	$0.751$	$0.149$	$0.529$	$0.775$	–
J2 (0.50,0.70,0.60)	Best new	0.967	0.973	0.464	0.940	–	–	–	–	–	0.836
J3 (0.70,0.70,0.60)	Trimmed mean	$0.975$	$0.981$	0.194	0.598	55.4	0.818	0.110	0.660	0.865	–

TABLE V : Multi-stage BO objective settings and ablation results.

Initialization	Trimmed-mean Stage quality				$r_{4/4}$ (%)	$\bar{r}_{\mathrm{feas}}^{\,10\%}$	$\bar{v}^{\,10\%}$	$\bar{J}_{k}^{\,10\%}$	$N_{4/4}^{\mathrm{uniq}}$
	$Q_{0}$	$Q_{1}$	$Q_{2}$	$Q_{3}$
Without DDPM seeds	0.965	0.972	0.000	0.000	0.0	0.500	0.386	0.348	0
With DDPM seeds	0.975	0.981	0.203	0.624	57.7	0.831	0.100	0.711	79

TABLE VI : DDPM-seed ablation under the fixed J4 BO setting.

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Robotic Manipulation

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To accelerate automated remanufacturing, robotic disassembly must be considered during the product design phase. However, designers currently lack quantitative feedback to identify which structural elements hinder robotic operations. To address this, this study proposes an analytical framework that provides actionable redesign guidance focused on fastener reduction, as fasteners are numerous and ubiquitous components found in almost all manufactured products. Using a Computer-Aided Design (CAD) model and its automatically generated Contact-Connection-Constraint (CCC) graph, the framework translates robotic disassembly sequence planning outcomes into component influence scores. These scores reflect how often a component causes structural constraint violations or evaluation objective deteriorations in the robotic disassembly sequence. To visually highlight structural hindrances, the framework projects these scores onto the CAD geometry as 3D heatmaps. The system then analytically simulates the removal of highly influential fasteners. It reports the expected reductions in structural constraints, tool changes, and robot travel distances, while preventing structurally unsafe modifications by evaluating geometric stability metrics. Experiments on seven household appliances demonstrate that the framework successfully targets redundant fasteners. Removing the recommended fasteners simplified the structural dependencies by eliminating between 8 and 132 structural constraints on the graph depending on each product's structural configuration. Furthermore, it improved robotic operational efficiency by eliminating unnecessary tool change operations and shortening travel distances by 165 to 1675 millimeters wherever structurally permissible.

Abstract

Robotic Manipulation

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