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Duan S, Wei X, Weng M, Zhao F, Chen P, Hong J, Xiang S, Shi Q, Sun L, Shen G, Wu J. Venus Flytrap-Inspired Data-Center-Free Fast-Responsive Soft Robots Enabled by 2D Ni 3(HITP) 2 MOF and Graphite. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2313089. [PMID: 38748777 DOI: 10.1002/adma.202313089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 04/29/2024] [Indexed: 05/24/2024]
Abstract
The rapid and responsive capabilities of soft robots in perceiving, assessing, and reacting to environmental stimuli are highly valuable. However, many existing soft robots, designed to mimic humans and other higher animals, often rely on data centers for the modulation of mechanoelectrical transduction and electromechanical actuation. This reliance significantly increases system complexity and time delays. Herein, drawing inspiration from Venus flytraps, a soft robot employing a power modulation strategy is presented for active stimulus reaction, eliminating the need for a data center. This robot achieves mechanoelectrical transduction through Ni3(2,3,6,7,10,11-hexaiminotriphenylene)2 (Ni3(HITP)2) metal-organic framework (MOF) with an ultralow time delay (256 ns) and electromechanical actuation via graphite. The Joule heating effect in graphite is effectively modulated by Ni3(HITP)2 before and after the presence of pressure, thus enabling the stimulus reaction of soft robots. As demonstrated, three soft robots are created: low-level edge tongue robots, Venus flytrap robots, and high-level nerve-center-controlled dragonfly robots. This power modulation strategy inspires designs of edge soft robots and high-level robots with a human-like effective fusion of conditioned and unconditioned reflexes.
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Affiliation(s)
- Shengshun Duan
- Joint International Research Laboratory of Information Display and Visualization School of Electronic Science and Engineering, Southeast University, Nanjing, 210096, China
| | - Xiao Wei
- Joint International Research Laboratory of Information Display and Visualization School of Electronic Science and Engineering, Southeast University, Nanjing, 210096, China
| | - Mingcen Weng
- School of Materials Science and Engineering, Fujian Provincial Key Laboratory of Advanced Materials Processing and Application, Key Laboratory of Polymer Materials and Products of Universities in Fujian, Fujian University of Technology, Fuzhou, 350118, China
| | - Fangzhi Zhao
- Joint International Research Laboratory of Information Display and Visualization School of Electronic Science and Engineering, Southeast University, Nanjing, 210096, China
| | - Pinzhen Chen
- Joint International Research Laboratory of Information Display and Visualization School of Electronic Science and Engineering, Southeast University, Nanjing, 210096, China
| | - Jianlong Hong
- Joint International Research Laboratory of Information Display and Visualization School of Electronic Science and Engineering, Southeast University, Nanjing, 210096, China
| | - Shengxin Xiang
- Joint International Research Laboratory of Information Display and Visualization School of Electronic Science and Engineering, Southeast University, Nanjing, 210096, China
| | - Qiongfeng Shi
- Joint International Research Laboratory of Information Display and Visualization School of Electronic Science and Engineering, Southeast University, Nanjing, 210096, China
| | - Litao Sun
- Center for 2D Materials, Southeast University, Nanjing, 211189, China
- SEU-FEI Nano-Pico Center, Key Laboratory of MEMS of Ministry of Education Collaborative Innovation Center for Micro/Nano Fabrication Device and System, Southeast University, Nanjing, 210096, China
| | - Guozhen Shen
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, 100081, China
| | - Jun Wu
- Joint International Research Laboratory of Information Display and Visualization School of Electronic Science and Engineering, Southeast University, Nanjing, 210096, China
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Chen X, You B, Dong Z. Optimization method for human-robot command combinations of hexapod robot based on multi-objective constraints. Front Neurorobot 2024; 18:1393738. [PMID: 38644902 PMCID: PMC11032014 DOI: 10.3389/fnbot.2024.1393738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 03/19/2024] [Indexed: 04/23/2024] Open
Abstract
Due to the heavy burden on human drivers when remotely controlling hexapod robots in complex terrain environments, there is a critical need for robot intelligence to assist in generating control commands. Therefore, this study proposes a mapping process framework that generates a combination of human-robot commands based on decision target values, focusing on the task of robot intelligence assisting drivers in generating human-robot command combinations. Furthermore, human-robot state constraints are quantified as geometric constraints on robot motion and driver fatigue constraints. By optimizing and filtering the feasible set of human-robot commands based on human-robot state constraints, instruction combinations are formed and recommended to the driver in real-time, thereby enhancing the efficiency and safety of human-machine coordination. To validate the effectiveness of the proposed method, a remote human-robot collaborative driving control system based on wearable devices is designed and implemented. Experimental results demonstrate that drivers utilizing the human-robot command recommendation system exhibit significantly improved robot walking stability and reduced collision rates compared to individual driving.
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Affiliation(s)
- Xiaolei Chen
- The Key Laboratory of Intelligent Technology for Cutting and Manufacturing Ministry of Education, Harbin University of Science and Technology, Harbin, China
| | - Bo You
- The Key Laboratory of Intelligent Technology for Cutting and Manufacturing Ministry of Education, Harbin University of Science and Technology, Harbin, China
- The Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin, China
| | - Zheng Dong
- The Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin, China
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Chen G, Qiao L, Zhou Z, Lei X, Zou M, Richter L, Ji A. Biomimetic lizard robot for adapting to Martian surface terrain. BIOINSPIRATION & BIOMIMETICS 2024; 19:036005. [PMID: 38452382 DOI: 10.1088/1748-3190/ad311d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 03/07/2024] [Indexed: 03/09/2024]
Abstract
The exploration of the planet Mars still is a top priority in planetary science. The Mars surface is extensively covered with soil-like material. Current wheeled rovers on Mars have been occasionally experiencing immobilization instances in unexpectedly weak terrains. The development of Mars rovers adaptable to these terrains is instrumental in improving exploration efficiency. Inspired by locomotion of the desert lizard, this paper illustrates a biomimetic quadruped robot with structures of flexible active spine and toes. By accounting for spine lateral flexion and its coordination with four leg movements, three gaits of tripod, trot and turning are designed. The motions corresponding to the three gaits are conceptually and numerically analyzed. On the granular terrains analog to Martian surface, the gasping forces by the active toes are estimated. Then traversing tests for the robot to move on Martian soil surface analog with the three gaits were investigated. Moreover, the traversing characteristics for Martian rocky and slope surface analog are analyzed. Results show that the robot can traverse Martian soil surface analog with maximum forward speed 28.13 m s-1turning speed 1.94° s-1and obstacle height 74.85 mm. The maximum angle for climbing Martian soil slope analog is 28°, corresponding slippery rate 76.8%. It is predicted that this robot can adapt to Martian granular rough terrain with gentle slopes.
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Affiliation(s)
- Guangming Chen
- College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, People's Republic of China
| | - Long Qiao
- College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, People's Republic of China
| | - Zhenwen Zhou
- College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, People's Republic of China
| | - Xiang Lei
- College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, People's Republic of China
| | - Meng Zou
- Key Laboratory of Bionic Engineering (Ministry of Education), Jilin University, Changchun 5988, People's Republic of China
| | - Lutz Richter
- SoftServe GmbH, Brienner Strasse 45, 80333 Munich, Germany
| | - Aihong Ji
- College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, People's Republic of China
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Kulathunga G, Hamed H, Klimchik A. Residual dynamics learning for trajectory tracking for multi-rotor aerial vehicles. Sci Rep 2024; 14:1858. [PMID: 38253651 PMCID: PMC10810356 DOI: 10.1038/s41598-024-51822-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 01/09/2024] [Indexed: 01/24/2024] Open
Abstract
This paper presents a technique to model the residual dynamics between a high-level planner and a low-level controller by considering reference trajectory tracking in a cluttered environment as an example scenario. We focus on minimising residual dynamics that arise due to only the kinematical modelling of high-level planning. The kinematical modelling is sufficient for such scenarios due to safety constraints and aggressive manoeuvres that are difficult to perform when the environment is cluttered and dynamic. We used a simplified motion model to represent the motion of the quadrotor when formulating the high-level planner. The Sparse Gaussian Process Regression-based technique is proposed to model the residual dynamics. Recently proposed Data-Driven MPC is targeting aggressive manoeuvres without considering obstacle constraints. The proposed technique is compared with Data-Driven MPC to estimate the residual dynamics error without considering obstacle constraints. The comparison results yield that the proposed technique helps to reduce the nominal model error by a factor of 2 on average. Further, the proposed complete framework was compared with four other trajectory-tracking approaches in terms of tracking the reference trajectory without colliding with obstacles. The proposed approach outperformed the others with less flight time without losing computational efficiency.
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Affiliation(s)
- Geesara Kulathunga
- Centre for Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia, 420500.
- Lincoln Institute for Agri-Food Technology, University of Lincoln, Lincoln, LN1, UK.
| | - Hany Hamed
- Advanced Institute of Science and Technology (KAIST), Daejeon, 28210, South Korea
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