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Zhao W, Zhang Y, Lim KM, Yang L, Wang N, Peng L. Research on control strategy of pneumatic soft bionic robot based on improved CPG. PLoS One 2024; 19:e0306320. [PMID: 38968177 PMCID: PMC11226027 DOI: 10.1371/journal.pone.0306320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 06/14/2024] [Indexed: 07/07/2024] Open
Abstract
To achieve the accuracy and anti-interference of the motion control of the soft robot more effectively, the motion control strategy of the pneumatic soft bionic robot based on the improved Central Pattern Generator (CPG) is proposed. According to the structure and motion characteristics of the robot, a two-layer neural network topology model for the robot is constructed by coupling 22 Hopfield neuron nonlinear oscillators. Then, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS), the membership functions are offline learned and trained to construct the CPG-ANFIS-PID motion control strategy for the robot. Through simulation research on the impact of CPG-ANFIS-PID input parameters on the swimming performance of the robot, it is verified that the control strategy can quickly respond to input parameter changes between different swimming modes, and stably output smooth and continuous dynamic position signals, which has certain advantages. Then, the motion performance of the robot prototype is analyzed experimentally and compared with the simulation results. The results show that the CPG-ANFIS-PID motion control strategy can output coupled waveform signals stably, and control the executing mechanisms of the pneumatic soft bionic robot to achieve biological rhythms motion propulsion waveforms, confirming that the control strategy has accuracy and anti-interference characteristics, and enable the robot have certain maneuverability, flexibility, and environmental adaptability. The significance of this work lies in establishing a CPG-ANFIS-PID control strategy applicable to pneumatic soft bionic robot and proposing a rhythmic motion control method applicable to pneumatic soft bionic robot.
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Affiliation(s)
- Wenchuan Zhao
- School of Information Science and Engineering, Shenyang University of Technology, Shenyang, China
| | - Yu Zhang
- School of Mechanical Engineering, Shenyang University of Technology, Shenyang, China
| | - Kian Meng Lim
- Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore
| | - Lijian Yang
- School of Information Science and Engineering, Shenyang University of Technology, Shenyang, China
| | - Ning Wang
- School of Mechanical Engineering, Shenyang University of Technology, Shenyang, China
| | - Linghui Peng
- School of Mechanical Engineering, Shenyang University of Technology, Shenyang, China
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Zhang Y, Thor M, Dilokthanakul N, Dai Z, Manoonpong P. Hybrid learning mechanisms under a neural control network for various walking speed generation of a quadruped robot. Neural Netw 2023; 167:292-308. [PMID: 37666187 DOI: 10.1016/j.neunet.2023.08.030] [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] [Received: 10/31/2022] [Revised: 06/01/2023] [Accepted: 08/18/2023] [Indexed: 09/06/2023]
Abstract
Legged robots that can instantly change motor patterns at different walking speeds are useful and can accomplish various tasks efficiently. However, state-of-the-art control methods either are difficult to develop or require long training times. In this study, we present a comprehensible neural control framework to integrate probability-based black-box optimization (PIBB) and supervised learning for robot motor pattern generation at various walking speeds. The control framework structure is based on a combination of a central pattern generator (CPG), a radial basis function (RBF) -based premotor network and a hypernetwork, resulting in a so-called neural CPG-RBF-hyper control network. First, the CPG-driven RBF network, acting as a complex motor pattern generator, was trained to learn policies (multiple motor patterns) for different speeds using PIBB. We also introduce an incremental learning strategy to avoid local optima. Second, the hypernetwork, which acts as a task/behavior to control parameter mapping, was trained using supervised learning. It creates a mapping between the internal CPG frequency (reflecting the walking speed) and motor behavior. This map represents the prior knowledge of the robot, which contains the optimal motor joint patterns at various CPG frequencies. Finally, when a user-defined robot walking frequency or speed is provided, the hypernetwork generates the corresponding policy for the CPG-RBF network. The result is a versatile locomotion controller which enables a quadruped robot to perform stable and robust walking at different speeds without sensory feedback. The policy of the controller was trained in the simulation (less than 1 h) and capable of transferring to a real robot. The generalization ability of the controller was demonstrated by testing the CPG frequencies that were not encountered during training.
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Affiliation(s)
- Yanbin Zhang
- Institute of Bio-inspired Structure and Surface Engineering, College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Mathias Thor
- Embodied AI and Neurorobotics Laboratory, SDU Biorobotics, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense M, Denmark
| | - Nat Dilokthanakul
- King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand
| | - Zhendong Dai
- Institute of Bio-inspired Structure and Surface Engineering, College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Poramate Manoonpong
- Institute of Bio-inspired Structure and Surface Engineering, College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China; Embodied AI and Neurorobotics Laboratory, SDU Biorobotics, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense M, Denmark; Bio-inspired Robotics & Neural Engineering Laboratory, School of Information Science & Technology, Vidyasirimedhi Institute of Science & Technology, Rayong, Thailand.
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Wang B, Wang Y, Huang J, Zeng Y, Liu X, Zhou K. Computed torque control and force analysis for mechanical leg with variable rotation axis powered by servo pneumatic muscle. ISA TRANSACTIONS 2023; 140:385-401. [PMID: 37391291 DOI: 10.1016/j.isatra.2023.06.014] [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: 01/15/2022] [Revised: 06/08/2023] [Accepted: 06/16/2023] [Indexed: 07/02/2023]
Abstract
It is difficult for a humanoid leg driven by two groups of antagonistic pneumatic muscles (PMs) to achieve a flexible humanoid gait, and its inherent strong coupling nonlinear characteristics make it hard to achieve good tracking performance in a large range of motion. Therefore, a four-bar linkage bionic knee joint structure with a variable axis and a double closed-loop servo position control strategy based on computed torque control are designed to improve anthropomorphic characteristics and the dynamic performance of the bionic mechanical leg powered by servo pneumatic muscle (SPM). Firstly, the relationship between the joint torque, the initial jump angle and the bounce height of the mechanical leg is established, and then we design a double-joint PM bionic mechanical leg containing a four-bar linkage mechanism of the knee joint. Secondly, a cascade position control strategy is developed, which consists of the outer position loop and the inner contraction force loop, and the mapping relationship is designed between joint torque and antagonistic PM contraction force. Finally, we further project bounce action timing of mechanical leg to realize the periodic jumping movement of the mechanical leg, and simulation and physical experiments of the real-style machine platform have been provided to demonstrate the effectiveness of the designed SPM controller.
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Affiliation(s)
- Binrui Wang
- College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
| | - Youcao Wang
- College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
| | - Jiqing Huang
- College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
| | - Yuxin Zeng
- School of Engineering, Faculty of Applied Science, The University of British Columbia, Kelowna, BC V1V 1V7, Canada
| | - Xiaolong Liu
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21211, USA
| | - Kun Zhou
- College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
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Zhou Q, Xu J, Fang H. A CPG-Based Versatile Control Framework for Metameric Earthworm-Like Robotic Locomotion. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2206336. [PMID: 36775888 DOI: 10.1002/advs.202206336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 01/08/2023] [Indexed: 05/18/2023]
Abstract
Annelids such as earthworms are considered to have central pattern generators (CPGs) that generate rhythms in neural circuits to coordinate the deformation of body segments for effective locomotion. At present, the states of earthworm-like robot segments are often assigned holistically and artificially by mimicking the earthworms' retrograde peristalsis wave, which is unable to adapt their gaits for variable environments and tasks. This motivates the authors to extend the bioinspired research from morphology to neurobiology by mimicking the CPG to build a versatile framework for spontaneous motion control. Here, the spatiotemporal dynamics is exploited from the coupled Hopf oscillators to not only unify the two existing gait generators for restoring temporal-symmetric phase-coordinated gaits and discrete gaits but also generate novel temporal-asymmetric phase-coordinated gaits. Theoretical and experimental tests consistently confirm that the introduction of temporal asymmetry improves the robot's locomotion performance. The CPG-based controller also enables seamless online switching of locomotion gaits to avoid abrupt changes, sharp stops, and starts, thus improving the robot's adaptability in variable working scenarios.
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Affiliation(s)
- Qinyan Zhou
- Institute of AI and Robotics, State Key Laboratory of Medical Neurobiology, MOE Engineering Research Center of AI & Robotics, Fudan University, Shanghai, 200433, China
| | - Jian Xu
- Institute of AI and Robotics, State Key Laboratory of Medical Neurobiology, MOE Engineering Research Center of AI & Robotics, Fudan University, Shanghai, 200433, China
| | - Hongbin Fang
- Institute of AI and Robotics, State Key Laboratory of Medical Neurobiology, MOE Engineering Research Center of AI & Robotics, Fudan University, Shanghai, 200433, China
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Sun T, Dai Z, Manoonpong P. Robust and reusable self-organized locomotion of legged robots under adaptive physical and neural communications. Front Neural Circuits 2023; 17:1111285. [PMID: 37063383 PMCID: PMC10102392 DOI: 10.3389/fncir.2023.1111285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 03/16/2023] [Indexed: 04/03/2023] Open
Abstract
IntroductionAnimals such as cattle can achieve versatile and elegant behaviors through automatic sensorimotor coordination. Their self-organized movements convey an impression of adaptability, robustness, and motor memory. However, the adaptive mechanisms underlying such natural abilities of these animals have not been completely realized in artificial legged systems.MethodsHence, we propose adaptive neural control that can mimic these abilities through adaptive physical and neural communications. The control algorithm consists of distributed local central pattern generator (CPG)-based neural circuits for generating basic leg movements, an adaptive sensory feedback mechanism for generating self-organized phase relationships among the local CPG circuits, and an adaptive neural coupling mechanism for transferring and storing the formed phase relationships (a gait pattern) into the neural structure. The adaptive neural control was evaluated in experiments using a quadruped robot.ResultsThe adaptive neural control enabled the robot to 1) rapidly and automatically form its gait (i.e., self-organized locomotion) within a few seconds, 2) memorize the gait for later recovery, and 3) robustly walk, even when a sensory feedback malfunction occurs. It also enabled maneuverability, with the robot being able to change its walking speed and direction. Moreover, implementing adaptive physical and neural communications provided an opportunity for understanding the mechanism of motor memory formation.DiscussionOverall, this study demonstrates that the integration of the two forms of communications through adaptive neural control is a powerful way to achieve robust and reusable self-organized locomotion in legged robots.
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Affiliation(s)
- Tao Sun
- Neurorobotics Technology for Advanced Robot Motor Control Lab, The College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
- Wearable Systems Lab, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhendong Dai
- Neurorobotics Technology for Advanced Robot Motor Control Lab, The College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Poramate Manoonpong
- Neurorobotics Technology for Advanced Robot Motor Control Lab, The College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
- Bio-Inspired Robotics and Neural Engineering Lab, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
- *Correspondence: Poramate Manoonpong ;
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A Trust-Assist Framework for Human–Robot Co-Carry Tasks. ROBOTICS 2023. [DOI: 10.3390/robotics12020030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023] Open
Abstract
Robots are increasingly being employed for diverse applications where they must work and coexist with humans. The trust in human–robot collaboration (HRC) is a critical aspect of any shared-task performance for both the human and the robot. The study of a human-trusting robot has been investigated by numerous researchers. However, a robot-trusting human, which is also a significant issue in HRC, is seldom explored in the field of robotics. Motivated by this gap, we propose a novel trust-assist framework for human–robot co-carry tasks in this study. This framework allows the robot to determine a trust level for its human co-carry partner. The calculations of this trust level are based on human motions, past interactions between the human–robot pair, and the human’s current performance in the co-carry task. The trust level between the human and the robot is evaluated dynamically throughout the collaborative task, and this allows the trust to change if the human performs false positive actions, which can help the robot avoid making unpredictable movements and causing injury to the human. Additionally, the proposed framework can enable the robot to generate and perform assisting movements to follow human-carrying motions and paces when the human is considered trustworthy in the co-carry task. The results of our experiments suggest that the robot effectively assists the human in real-world collaborative tasks through the proposed trust-assist framework.
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Shao D, Wang Z, Ji A, Dai Z, Manoonpong P. A gecko-inspired robot with CPG-based neural control for locomotion and body height adaptation. BIOINSPIRATION & BIOMIMETICS 2022; 17:036008. [PMID: 35236786 DOI: 10.1088/1748-3190/ac5a3c] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 03/02/2022] [Indexed: 06/14/2023]
Abstract
Today's gecko-inspired robots have shown the ability of omnidirectional climbing on slopes with a low centre of mass. However, such an ability cannot efficiently cope with bumpy terrains or terrains with obstacles. In this study, we developed a gecko-inspired robot (Nyxbot) with an adaptable body height to overcome this limitation. Based on an analysis of the skeletal system and kinematics of real geckos, the adhesive mechanism and leg structure design of the robot were designed to endow it with adhesion and adjustable body height capabilities. Neural control with exteroceptive sensory feedback is utilised to realise body height adaptability while climbing on a slope. The locomotion performance and body adaptability of the robot were tested by conducting slope climbing and obstacle crossing experiments. The gecko robot can climb a 30° slope with spontaneous obstacle crossing (maximum obstacle height of 38% of the body height) and can climb even steeper slopes (up to 60°) without an obstacle or bump. Using 3D force measuring platforms for ground reaction force analysis of geckos and the robot, we show that the motions of the developed robot driven by neural control and the motions of geckos are dynamically comparable. To this end, this study provides a basis for developing climbing robots with adaptive bump/obstacle crossing on slopes towards more agile and versatile gecko-like locomotion.
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Affiliation(s)
- Donghao Shao
- Institute of Bio-Inspired Structure and Surface Engineering, College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, People's Republic of China
| | - Zhouyi Wang
- Institute of Bio-Inspired Structure and Surface Engineering, College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, People's Republic of China
| | - Aihong Ji
- Institute of Bio-Inspired Structure and Surface Engineering, College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, People's Republic of China
| | - Zhendong Dai
- Institute of Bio-Inspired Structure and Surface Engineering, College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, People's Republic of China
| | - Poramate Manoonpong
- Institute of Bio-Inspired Structure and Surface Engineering, College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, People's Republic of China
- Bio-Inspired Robotics and Neural Engineering Laboratory, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
- Embodied AI and Neurorobotics Laboratory, SDU Biorobotics, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense M, Denmark
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Shi H, Zhou B, Zeng H, Wang F, Dong Y, Li J, Wang K, Tian H, Meng MQH. Reinforcement Learning With Evolutionary Trajectory Generator: A General Approach for Quadrupedal Locomotion. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3145495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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10
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Srisuchinnawong A, Homchanthanakul J, Manoonpong P. NeuroVis: Real-Time Neural Information Measurement and Visualization of Embodied Neural Systems. Front Neural Circuits 2021; 15:743101. [PMID: 35027885 PMCID: PMC8751631 DOI: 10.3389/fncir.2021.743101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Accepted: 11/29/2021] [Indexed: 11/13/2022] Open
Abstract
Understanding the real-time dynamical mechanisms of neural systems remains a significant issue, preventing the development of efficient neural technology and user trust. This is because the mechanisms, involving various neural spatial-temporal ingredients [i.e., neural structure (NS), neural dynamics (ND), neural plasticity (NP), and neural memory (NM)], are too complex to interpret and analyze altogether. While advanced tools have been developed using explainable artificial intelligence (XAI), node-link diagram, topography map, and other visualization techniques, they still fail to monitor and visualize all of these neural ingredients online. Accordingly, we propose here for the first time "NeuroVis," real-time neural spatial-temporal information measurement and visualization, as a method/tool to measure temporal neural activities and their propagation throughout the network. By using this neural information along with the connection strength and plasticity, NeuroVis can visualize the NS, ND, NM, and NP via i) spatial 2D position and connection, ii) temporal color gradient, iii) connection thickness, and iv) temporal luminous intensity and change of connection thickness, respectively. This study presents three use cases of NeuroVis to evaluate its performance: i) function approximation using a modular neural network with recurrent and feedforward topologies together with supervised learning, ii) robot locomotion control and learning using the same modular network with reinforcement learning, and iii) robot locomotion control and adaptation using another larger-scale adaptive modular neural network. The use cases demonstrate how NeuroVis tracks and analyzes all neural ingredients of various (embodied) neural systems in real-time under the robot operating system (ROS) framework. To this end, it will offer the opportunity to better understand embodied dynamic neural information processes, boost efficient neural technology development, and enhance user trust.
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Affiliation(s)
- Arthicha Srisuchinnawong
- Bio-inspired Robotics and Neural Engineering Laboratory, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
- Embodied Artificial Intelligence and Neurorobotics Laboratory, SDU Biorobotics, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense, Denmark
| | - Jettanan Homchanthanakul
- Bio-inspired Robotics and Neural Engineering Laboratory, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
| | - Poramate Manoonpong
- Bio-inspired Robotics and Neural Engineering Laboratory, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
- Embodied Artificial Intelligence and Neurorobotics Laboratory, SDU Biorobotics, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense, Denmark
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Thor M, Strohmer B, Manoonpong P. Locomotion Control With Frequency and Motor Pattern Adaptations. Front Neural Circuits 2021; 15:743888. [PMID: 34899196 PMCID: PMC8655109 DOI: 10.3389/fncir.2021.743888] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 11/03/2021] [Indexed: 11/16/2022] Open
Abstract
Existing adaptive locomotion control mechanisms for legged robots are usually aimed at one specific type of adaptation and rarely combined with others. Adaptive mechanisms thus stay at a conceptual level without their coupling effect with other mechanisms being investigated. However, we hypothesize that the combination of adaptation mechanisms can be exploited for enhanced and more efficient locomotion control as in biological systems. Therefore, in this work, we present a central pattern generator (CPG) based locomotion controller integrating both a frequency and motor pattern adaptation mechanisms. We use the state-of-the-art Dual Integral Learner for frequency adaptation, which can automatically and quickly adapt the CPG frequency, enabling the entire motor pattern or output signal of the CPG to be followed at a proper high frequency with low tracking error. Consequently, the legged robot can move with high energy efficiency and perform the generated locomotion with high precision. The versatile state-of-the-art CPG-RBF network is used as a motor pattern adaptation mechanism. Using this network, the motor patterns or joint trajectories can be adapted to fit the robot's morphology and perform sensorimotor integration enabling online motor pattern adaptation based on sensory feedback. The results show that the two adaptation mechanisms can be combined for adaptive locomotion control of a hexapod robot in a complex environment. Using the CPG-RBF network for motor pattern adaptation, the hexapod learned basic straight forward walking, steering, and step climbing. In general, the frequency and motor pattern mechanisms complement each other well and their combination can be seen as an essential step toward further studies on adaptive locomotion control.
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Affiliation(s)
- Mathias Thor
- Embodied AI and Neurorobotics Lab, SDU Biorobotics, The Mærsk Mc-Kinney Møller Institute, The University of Southern Denmark, Odense, Denmark
| | - Beck Strohmer
- Embodied AI and Neurorobotics Lab, SDU Biorobotics, The Mærsk Mc-Kinney Møller Institute, The University of Southern Denmark, Odense, Denmark
| | - Poramate Manoonpong
- Embodied AI and Neurorobotics Lab, SDU Biorobotics, The Mærsk Mc-Kinney Møller Institute, The University of Southern Denmark, Odense, Denmark.,Bio-Inspired Robotics and Neural Engineering Laboratory, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
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Manoonpong P, Patanè L, Xiong X, Brodoline I, Dupeyroux J, Viollet S, Arena P, Serres JR. Insect-Inspired Robots: Bridging Biological and Artificial Systems. SENSORS (BASEL, SWITZERLAND) 2021; 21:7609. [PMID: 34833685 PMCID: PMC8623770 DOI: 10.3390/s21227609] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/26/2021] [Accepted: 10/27/2021] [Indexed: 12/18/2022]
Abstract
This review article aims to address common research questions in hexapod robotics. How can we build intelligent autonomous hexapod robots that can exploit their biomechanics, morphology, and computational systems, to achieve autonomy, adaptability, and energy efficiency comparable to small living creatures, such as insects? Are insects good models for building such intelligent hexapod robots because they are the only animals with six legs? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying relevant and future directions in the field of hexapod robotics over the next decade. After an introduction in section (1), the sections will respectively cover the following three key areas: (2) biomechanics focused on the design of smart legs; (3) locomotion control; and (4) high-level cognition control. These interconnected and interdependent areas are all crucial to improving the level of performance of hexapod robotics in terms of energy efficiency, terrain adaptability, autonomy, and operational range. We will also discuss how the next generation of bioroboticists will be able to transfer knowledge from biology to robotics and vice versa.
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Affiliation(s)
- Poramate Manoonpong
- Embodied Artificial Intelligence and Neurorobotics Laboratory, SDU Biorobotics, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, 5230 Odense, Denmark;
- Bio-Inspired Robotics and Neural Engineering Laboratory, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong 21210, Thailand
| | - Luca Patanè
- Department of Engineering, University of Messina, 98100 Messina, Italy
| | - Xiaofeng Xiong
- Embodied Artificial Intelligence and Neurorobotics Laboratory, SDU Biorobotics, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, 5230 Odense, Denmark;
| | - Ilya Brodoline
- Department of Biorobotics, Aix Marseille University, CNRS, ISM, CEDEX 07, 13284 Marseille, France; (I.B.); (S.V.)
| | - Julien Dupeyroux
- Faculty of Aerospace Engineering, Delft University of Technology, 52600 Delft, The Netherlands;
| | - Stéphane Viollet
- Department of Biorobotics, Aix Marseille University, CNRS, ISM, CEDEX 07, 13284 Marseille, France; (I.B.); (S.V.)
| | - Paolo Arena
- Department of Electrical, Electronic and Computer Engineering, University of Catania, 95131 Catania, Italy
| | - Julien R. Serres
- Department of Biorobotics, Aix Marseille University, CNRS, ISM, CEDEX 07, 13284 Marseille, France; (I.B.); (S.V.)
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Eslamy M, Schilling AF. Estimation of knee and ankle angles during walking using thigh and shank angles. BIOINSPIRATION & BIOMIMETICS 2021; 16:066012. [PMID: 34492652 DOI: 10.1088/1748-3190/ac245f] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 09/07/2021] [Indexed: 06/13/2023]
Abstract
Estimation of joints' trajectories is commonly used in human gait analysis, and in the development of motion planners and high-level controllers for prosthetics, orthotics, exoskeletons and humanoids. Human locomotion is the result of the cooperation between leg joints and limbs. This suggests the existence of underlying relationships between them which lead to a harmonic gait. In this study we aimed to estimate knee and ankle trajectories using thigh and shank angles. To do so, an estimation approach was developed that continuously mapped the inputs to the outputs, which did not require switching rules, speed estimation, gait percent identification or look-up tables. The estimation algorithm was based on a nonlinear auto-regressive model with exogenous inputs. The method was then combined with wavelets theory, and then the two were used in a neural network. To evaluate the estimation performance, three scenarios were developed which used only one source of inputs (i.e., only shank angles or only thigh angles). First, knee anglesθk(outputs) were estimated using thigh anglesθth(inputs). Second, ankle anglesθa(outputs) were estimated using thigh anglesθsh(inputs), and third, the ankle angles were estimated using shank angles (inputs). The proposed approach was investigated for 22 subjects at different walking speeds and the leave-one-subject-out procedure was used for training and testing the estimation algorithm. Average root mean square errors were 3.9°-5.3° and 2.1°-2.3° for knee and ankle angles, respectively. Average mean absolute errors (MAEs) MAEs were 3.2°-4° and 1.7°-1.8°, and average correlation coefficientsρccwere 0.95-0.98 and 0.94-0.96 for knee and ankle angles, respectively. The limitations and strengths of the proposed approach are discussed in detail and the results are compared with several studies.
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Affiliation(s)
- Mahdy Eslamy
- Applied Rehabilitation Technology ART Lab, Department for Trauma Surgery, Orthopaedics and Plastic Surgery, Universitätsmedizin Göttingen (UMG), 37075, Göttingen, Germany
| | - Arndt F Schilling
- Applied Rehabilitation Technology ART Lab, Department for Trauma Surgery, Orthopaedics and Plastic Surgery, Universitätsmedizin Göttingen (UMG), 37075, Göttingen, Germany
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Haomachai W, Shao D, Wang W, Ji A, Dai Z, Manoonpong P. Lateral Undulation of the Bendable Body of a Gecko-Inspired Robot for Energy-Efficient Inclined Surface Climbing. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3101519] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Distributed-force-feedback-based reflex with online learning for adaptive quadruped motor control. Neural Netw 2021; 142:410-427. [PMID: 34139657 DOI: 10.1016/j.neunet.2021.06.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 05/04/2021] [Accepted: 06/01/2021] [Indexed: 11/24/2022]
Abstract
Biological motor control mechanisms (e.g., central pattern generators (CPGs), sensory feedback, reflexes, and motor learning) play a crucial role in the adaptive locomotion of animals. However, the interaction and integration of these mechanisms - necessary for generating the efficient, adaptive locomotion responses of legged robots to diverse terrains - have not yet been fully realized. One issue is that of achieving adaptive motor control for fast postural adaptation across various terrains. To address this issue, this study proposes a novel distributed-force-feedback-based reflex with online learning (DFRL). It integrates force-sensory feedback, reflexes, and learning to cooperate with CPGs in producing adaptive motor commands. The DFRL is based on a simple neural network that uses plastic synapses modulated online by a fast dual integral learner. Experimental results on different quadruped robots show that the DFRL can (1) automatically and rapidly adapt the CPG patterns (motor commands) of the robots, enabling them to realize appropriate body postures during locomotion and (2) enable the robots to effectively accommodate themselves to various slope terrains, including steep ones. Consequently, the DFRL-controlled robots can achieve efficient adaptive locomotion, to tackle complex terrains with diverse slopes.
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Srisuchinnawong A, Wang B, Shao D, Ngamkajornwiwat P, Dai Z, Ji A, Manoonpong P. Modular Neural Control for Gait Adaptation and Obstacle Avoidance of a Tailless Gecko Robot. J INTELL ROBOT SYST 2021. [DOI: 10.1007/s10846-020-01285-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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