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Zhang C, Yang L, Wang W, Fan H, Tan W, Wang R, Wang F, Xi N, Liu L. Steering Muscle-Based Bio-Syncretic Robot Through Bionic Optimized Biped Mechanical Design. Soft Robot 2024; 11:484-493. [PMID: 38407843 DOI: 10.1089/soro.2023.0121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2024] Open
Abstract
Bio-syncretic robots consisting of artificial structures and living muscle cells have attracted much attention owing to their potential advantages, such as high drive efficiency, miniaturization, and compatibility. Motion controllability, as an important factor related to the main performance of bio-syncretic robots, has been explored in numerous studies. However, most of the existing bio-syncretic robots still face challenges related to the further development of steerable kinematic dexterity. In this study, a bionic optimized biped fully soft bio-syncretic robot actuated by two muscle tissues and steered with a direction-controllable electric field generated by external circularly distributed multiple electrodes has been developed. The developed bio-syncretic robot could realize wirelessly steerable motion and effective transportation of microparticle cargo on artificial polystyrene and biological pork tripe surfaces. This study may provide an effective strategy for the development of bio-syncretic robots and other related studies, such as nonliving soft robot design and muscle tissue engineering.
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Affiliation(s)
- Chuang Zhang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
| | - Lianchao Yang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Wenxue Wang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
| | - Huijie Fan
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
| | - Wenjun Tan
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Ruiqian Wang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Feifei Wang
- Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong, China
| | - Ning Xi
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
- Emerging Technologies Institute, Department of Industrial and Manufacturing Systems Engineering, University of Hong Kong, Hong Kong, China
| | - Lianqing Liu
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
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Webster-Wood VA, Guix M, Xu NW, Behkam B, Sato H, Sarkar D, Sanchez S, Shimizu M, Parker KK. Biohybrid robots: recent progress, challenges, and perspectives. BIOINSPIRATION & BIOMIMETICS 2022; 18:015001. [PMID: 36265472 DOI: 10.1088/1748-3190/ac9c3b] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 10/20/2022] [Indexed: 06/16/2023]
Abstract
The past ten years have seen the rapid expansion of the field of biohybrid robotics. By combining engineered, synthetic components with living biological materials, new robotics solutions have been developed that harness the adaptability of living muscles, the sensitivity of living sensory cells, and even the computational abilities of living neurons. Biohybrid robotics has taken the popular and scientific media by storm with advances in the field, moving biohybrid robotics out of science fiction and into real science and engineering. So how did we get here, and where should the field of biohybrid robotics go next? In this perspective, we first provide the historical context of crucial subareas of biohybrid robotics by reviewing the past 10+ years of advances in microorganism-bots and sperm-bots, cyborgs, and tissue-based robots. We then present critical challenges facing the field and provide our perspectives on the vital future steps toward creating autonomous living machines.
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Affiliation(s)
- Victoria A Webster-Wood
- Mechanical Engineering, Biomedical Engineering (by courtesy), McGowan Institute of Regenerative Medicine, Carnegie Mellon University, Pittsburgh, PA 15116, United States of America
| | - Maria Guix
- Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology (BIST), Baldiri-Reixac 10-12, 08028 Barcelona, Spain
- Departament de Ciència dels Materials i Química Física, Institut de Química Teòrica i Computacional Barcelona, Universitat de Barcelona, 08028 Barcelona, Spain
| | - Nicole W Xu
- Laboratories for Computational Physics and Fluid Dynamics, U.S. Naval Research Laboratory, Code 6041, Washington, DC, United States of America
| | - Bahareh Behkam
- Department of Mechanical Engineering, Institute for Critical Technology and Applied Science, Blacksburg, VA 24061, United States of America
| | - Hirotaka Sato
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 65 Nanyang Drive, Singapore, 637460, Singapore
| | - Deblina Sarkar
- MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America
| | - Samuel Sanchez
- Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology (BIST), Baldiri-Reixac 10-12, 08028 Barcelona, Spain
- Catalan Institute for Research and Advanced Studies (ICREA), Avda. Lluis Companys 23, 08010 Barcelona, Spain
| | - Masahiro Shimizu
- Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-machi, Toyonaka, Osaka, Japan
| | - Kevin Kit Parker
- Disease Biophysics Group, Wyss Institute for Biologically Inspired Engineering and School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States of America
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Leaman EJ, Sahari A, Traore MA, Geuther BQ, Morrow CM, Behkam B. Data-driven statistical modeling of the emergent behavior of biohybrid microrobots. APL Bioeng 2020; 4:016104. [PMID: 32128471 PMCID: PMC7049295 DOI: 10.1063/1.5134926] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Accepted: 02/10/2020] [Indexed: 12/19/2022] Open
Abstract
Multi-agent biohybrid microrobotic systems, owing to their small size and distributed nature, offer powerful solutions to challenges in biomedicine, bioremediation, and biosensing. Synthetic biology enables programmed emergent behaviors in the biotic component of biohybrid machines, expounding vast potential benefits for building biohybrid swarms with sophisticated control schemes. The design of synthetic genetic circuits tailored toward specific performance characteristics is an iterative process that relies on experimental characterization of spatially homogeneous engineered cell suspensions. However, biohybrid systems often distribute heterogeneously in complex environments, which will alter circuit performance. Thus, there is a critically unmet need for simple predictive models that describe emergent behaviors of biohybrid systems to inform synthetic gene circuit design. Here, we report a data-driven statistical model for computationally efficient recapitulation of the motility dynamics of two types of Escherichia coli bacteria-based biohybrid swarms-NanoBEADS and BacteriaBots. The statistical model was coupled with a computational model of cooperative gene expression, known as quorum sensing (QS). We determined differences in timescales for programmed emergent behavior in BacteriaBots and NanoBEADS swarms, using bacteria as a comparative baseline. We show that agent localization and genetic circuit sensitivity strongly influence the timeframe and the robustness of the emergent behavior in both systems. Finally, we use our model to design a QS-based decentralized control scheme wherein agents make independent decisions based on their interaction with other agents and the local environment. We show that synergistic integration of synthetic biology and predictive modeling is requisite for the efficient development of biohybrid systems with robust emergent behaviors.
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Affiliation(s)
- Eric J. Leaman
- Department of Mechanical Engineering, Virginia Tech, Blacksburg, Virginia 24061, USA
| | - Ali Sahari
- School of Biomedical Engineering and Sciences, Virginia Tech, Blacksburg, Virginia 24061, USA
| | - Mahama A. Traore
- Department of Mechanical Engineering, Virginia Tech, Blacksburg, Virginia 24061, USA
| | - Brian Q. Geuther
- Department of Mechanical Engineering, Virginia Tech, Blacksburg, Virginia 24061, USA
| | - Carmen M. Morrow
- Department of Mechanical Engineering, Virginia Tech, Blacksburg, Virginia 24061, USA
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