Innovative Design of Multi-Functional Supernumerary Robotic Limbs with Ellipsoid Workspace Optimization
Jun Huo, Jian Huang, Jie Zuo, Bo Yang, Zhongzheng Fu, Xi Li, Samer Mohammed
AI summary
Problem
Designing a general-purpose supernumerary robotic limb that effectively balances upper-limb grasping flexibility with lower-limb walking support remains challenging due to the lack of a unified design theory and efficient workspace quantification methods.
Approach
The authors propose a multiobjective optimization design theory that quantifies workspace similarity using geometric ellipsoids and optimizes limb link lengths via a novel multisubpopulation correction firefly algorithm to balance grasping, walking, and sit-to-stand support.
Key results
- Geometric ellipsoid quantification method for efficient workspace similarity assessment
- Multiobjective optimization model integrating workspace similarity, braced force, mass, and inertia
- Multisubpopulation correction firefly algorithm for rapid convergence on high-dimensional Pareto fronts
- Experimental validation showing 7.2% higher grasp success and 12.7%/25.1% reduced muscle activity during walking/STS tasks
Why it matters
This framework provides a scalable, theory-driven design pathway for multifunctional wearable robots, directly benefiting rehabilitation engineers and human augmentation developers.
Abstract
Supernumerary robotic limbs (SRLs) offer substan- tial potential in both the rehabilitation of hemiplegic patients and the enhancement of functional capabilities for healthy individuals. Designing a general-purpose SRL device is inherently challenging, particularly when developing a unified theoretical framework that meets the diverse functional requirements of both upper and lower limbs. In this article, we propose a multiobjective optimization (MOO) design theory that integrates grasping workspace simi- larity, walking workspace similarity, braced force for sit-to-stand (STS) movements, and overall mass and inertia. A geometric vector quantification method is developed using an ellipsoid to represent the workspace, aiming to reduce computational complexity and ad- dress quantification challenges. The ellipsoid envelope transforms workspace points into ellipsoid attributes, providing a parametric description of the workspace. Furthermore, the STS static braced force assesses the effectiveness of force transmission. The overall mass and inertia restricts excessive link length. To facilitate rapid and stable convergence of the model to high-dimensional irregu- lar Pareto fronts, we introduce a multisubpopulation correction firefly algorithm. This algorithm incorporates a strategy involving attractive and repulsive domains to effectively handle the MOO task. The optimized solution is utilized to redesign the prototype for experimentation to meet specified requirements. Six healthy Received 6 January 2025; revised 13 May 2025 and 1 July 2025; accepted 8 July 2025. Date of publication 15 July 2025; date of current version 7 August 2025. This work was supported in part by the National Natural Science Foundation of China under Grant U24A20280, Grant 62333007, and Grant U1913207 and in part by the Program for HUST Academic Frontier Youth Team. This article was recommended for publication by Associate Editor A. Luisa Trejos and Editor K. Mombaur upon evaluation of the reviewers’ comments. (Corresponding authors: Jian Huang; Jie Zuo.) This work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by Tongji Medi- cal College, Huazhong University of Science and Technology under Application No. IORG0003571, and performed in line with the Declaration of Helsinki. Jun Huo, Jian Huang, Zhongzheng Fu, and Xi Li are with the Key Lab- oratory of the Ministry of Education for Image Processing and Intelligent Control, Huazhong University of Science and Technology, Wuhan 430074, China, and also with the Hubei Key Laboratory of Brain-inspired Intelli- gent Systems, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China (e-mail: huang_jan@mail.hust.edu.cn). Jie Zuo is with the School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China (e-mail: zuojie@whut.edu.cn). Bo Yang is with the State Key Laboratory of Intelligent Vehicle Safety Tech- nology, Chongqing Changan Automobile Company Ltd., Chongqing 400023, China (e-mail: ybandbob@gmail.com). Samer Mohammed is with the Univ Paris-Est Créteil, F-94400 Vitry, France (e-mail: samer.mohammed@u-pec.fr). This article has supplementary downloadable material available at https://doi.org/10.1109/TRO.2025.3588763, provided by the authors. Digital Object Identifier 10.1109/TRO.2025.3588763 participants and two hemiplegia patients participated in real exper- iments. Compared to the preoptimization results, the average grasp success rate improved by 7.2%, while the muscle activity during walking and STS tasks decreased by an average of 12.7% and 25.1%, respectively. The proposed design theory offers an efficient option for the design of multifunctional SRL mechanisms.