Structure-Actuation Co-Design of All-Electric Heavy-Duty Robotic Manipulators

Mohammad Bahari; Amir Hossein Barjini; Jouni Mattila

cs.ROOct 27, 2026

Structure-Actuation Co-Design of All-Electric Heavy-Duty Robotic Manipulators

Authors: Mohammad Bahari, Amir Hossein Barjini, Jouni Mattila

Organizations: Engineering and Natural Sciences, Tampere University, Tampere, Finland

Abstract

This paper presents a mixed discrete-continuous multiobjective bilevel framework for the structure-actuation co-design of all-electric heavy-duty robotic manipulators. The upper level optimizes manipulator geometry and actuation topology, while joint-specific lower-level searches construct feasible archives of discrete motor, gearbox, and screw configurations. A system-level coordinator selects actuator packages according to hardware mass, co-design cost, and loss-aware supply energy, and the resulting actuator masses are fed back to the robot model until a mass-consistent fixed point is obtained. Evaluation on a full-pose SE(3) benchmark with a 100-kg tool payload yields eight feasible nondominated designs, all satisfying the 12 prescribed poses. Actuator-mass coupling converges in three iterations for every reported design. A fixed-design ablation shows that one-pass sequential sizing changes hardware mass by up to 2.13%, co-design cost by 10.20%, and supply energy by 9.40% in magnitude. Payload screening further reveals increasing mass-cost-energy demand and changes in competitive mechanism topology with carried load.

Figures & tables

Fig. 1: Geometric parameterization of the candidate linkage mechanisms: (a) three-bar and (b) four-bar mechanisms.

Fig. 2: Full-pose $SE(3)$ benchmark with 12 prescribed TCP positions and orientations and the closed dynamic-sizing trajectory.

Item	Value
$L_{1}$ ; $L_{2},L_{3}$	$[0.8,2.0]$ m; $[2.5,5.0]$ m
$\nu_{2},\nu_{3}$	rotary / 3-bar / 4-bar
$A_{2},A_{3}$ ; $B_{2},B_{3}$	$[0.60,0.90]$ m; $[0.30,0.90]$ m
$\alpha_{2}$ ; $\alpha_{3}$	$[0.01,0.50]$ rad; $[0.01,0.20]$ rad
Motor / $N$ / $p$	68 motors; $N,p\in\{1,\ldots,30\}$ , $p$ in mm/rev
Tool / fixed wrist	100 kg / 50 kg

TABLE I: Principal Design and Optimization Settings

Fig. 3: Nominal 100-kg nondominated set: (a) hardware mass versus normalized co-design cost, (b) hardware mass versus supply energy, and (c) normalized cost versus supply energy.

D_{M}

D_{C}

, and

D_{E}

denote the minimum-mass, minimum-cost, and minimum-energy designs.

Design	$J_{2}/J_{3}$	$[L_{1},L_{2},L_{3}]$ [m]	$[A_{2},B_{2},\alpha_{2}]$	$[A_{3},B_{3},\alpha_{3}]$	$J_{1}$ : M/ $P_{N}$ / $N_{g}$	$J_{2}$ : M/ $P_{N}$ / $N_{g}$ / $p$	$J_{3}$ : M/ $P_{N}$ / $N_{g}$ / $p$
$D_{M}$	4-bar/3-bar	$[1.145,4.961,2.543]$	$[0.772,0.370,0.329]$	$[0.817,0.678,0.100]$	M01/11/15	M02/4/6/7	M03/2.2/3/5
$D_{E}$	4-bar/3-bar	$[1.145,4.961,2.543]$	$[0.683,0.828,0.060]$	$[0.817,0.699,0.100]$	M04/3/26	M01/11/6/7	M03/2.2/3/5
$D_{C}$	4-bar/4-bar	$[1.213,4.961,2.682]$	$[0.683,0.828,0.100]$	$[0.654,0.397,0.100]$	M05/5.5/15	M06/2.2/9/13	M07/2.2/6/9

TABLE II: Representative Nominal Co-Designs

Fig. 4: Payload screening: minimum observed (a) hardware mass, (b) normalized co-design cost, and (c) supply energy within each obtained nondominated set. All cases use the same reduced fixed search budget.

Design	$e_{m}^{(0)}$	$\Delta M$ [%]	$\Delta C$ [%]	$\Delta E_{\rm sup}$ [%]	$\epsilon_{E}$ [%]
$D_{M}$	2.655	+0.47	-0.71	+9.40	+1.89
$D_{E}$	2.836	+2.13	-3.81	+8.09	+1.75
$D_{C}$	3.367	+0.43	+10.20	-8.80	+3.67

TABLE III: Fixed-Design Actuator-Mass-Feedback Ablation

Robotics

Robotics studies the design and control of machines that perceive, move, and act in physical environments.

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The authors are with the Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

Legged Robotics

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Different tasks performed by legged robots impose distinct torque and speed requirements on actuators. Existing robotic actuators are generally optimized at the component level for metrics such as torque or power density, without explicit task guidance. System-level optimization across components such as motors, gearboxes, and sensors is challenging because of the high computational cost and coupling among mechanical, electrical, and electromagnetic behaviors. Consequently, improvements in individual components may not translate into better robot performance in a specific task. To this end, we present a systematic optimization framework for task-oriented co-design of actuator hardware and control. First, surrogate models are employed to accelerate motor evaluation and support global exploration of the coupled design space. Then, a hierarchical mixed-variable optimization strategy is adopted, combining discrete enumeration with continuous search over dimensions and real-valued indices. These indices are rounded to select admissible values for the remaining discrete choices before each evaluation. Within this search, rated output torque density and task performance are jointly optimized, with Bezier-parameterized joint torque profiles determined for each hardware candidate. Finally, the effectiveness of the proposed framework is validated through actuator fabrication and experiments on a two-degree-of-freedom jumping leg. Based on its measured mass, the fabricated prototype achieves a nominal rated output torque density of 35.7 N m/kg, approximately 60% higher than that of a widely used commercial geared joint actuator, while being 18.6% lighter. Under matched bench conditions, it achieves 12.0% greater jump height at twice-rated torque. Together, these results demonstrate a systematic route from task requirements to actuator design and control.

Abstract

Robotics

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