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Fuzzy Deep Deterministic Policy Gradient-Based Motion Controller for humanoid robot

INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2022年第5期24卷 2476-2492页

作者： Kuo, Ping-Huan Hu, Jun Lin, Ssu-Ting Hsu, Po-Wei Natl Chung Cheng Univ Dept Mech Engn Chiayi 62102 Taiwan Natl Chung Cheng Univ Adv Inst Mfg High Tech Innovat AIM HI Chiayi 62102 Taiwan Natl Pingtung Univ Dept Intelligent Robot Pingtung 90004 Taiwan

In conventional robot arm control, inverse kinematics (IK) is used as the basis for computing arm joint angles. However, IK can be used to compute joint angles only after the terminal point has been reached, and it cannot optimize arm movements. Furthermore, the singularity problem is sometimes encountered when using IK. For example, if a robot arm in motion passes through a singularity, the next step's movement is incomputable, which results in errors. Therefore, this study did not use IK for computing the joint angles of humanoid robot arms. Instead, this paper proposes a motion controller based on machine learning and fuzzy logic for the aforementioned purpose. Conventional reinforcement learning can provide satisfactory results for a single state but cannot be used to perform optimized calculations for infinite states. To solve this problem, this study used the deep deterministic policy gradient (DDPG) algorithm and allowed a humanoid robot to self-learn and autonomously plan the movement of and joint angles in its arm. A state and its action can be calculated in a hyperplane by using the developed neural network. A continuous mapping relationship exists between the state and its action in this hyperplane. Thus, the humanoid robot obtained optimal learning experiences in multiple self-learning processes. Finally, the concept was incorporated into a visual feedback system to achieve object grasping by the humanoid robot. The humanoid robot exhibited satisfactory learning outcomes-as well as satisfactory motion control performance-in experiments when combining fuzzy logic with the DDPG algorithm.

关键词： DDPG Fuzzy logic humanoid robot Intelligent control

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Real-Time Implementation of Face Recognition and Emotion Recognition in a humanoid robot Using a Convolutional Neural Network

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IEEE ACCESS 2022年 10卷 89876-89886页

作者： Dwijayanti, Suci Iqbal, Muhammad Suprapto, Bhakti Yudho Univ Sriwijaya Dept Elect Engn Indralaya 30662 Indonesia

robots can mimic humans, including recognizing faces and emotions. However, relevant studies have not been implemented in real-time humanoid robot systems. In addition, face and emotion recognition have been considered separate problems. This study proposes a combination of face and emotion recognition for real-time application in a humanoid robot. Specifically, face and emotion recognition systems are developed simultaneously using convolutional neural network architectures. The model is compared to well-known architectures, such as AlexNet and VGG16, to determine which is better for implementation in humanoid robots. Data used for face recognition are primary data taken from 30 electrical engineering students after preprocessing, resulting in 18,900 data points. Emotion data of surprise, anger, neutral, smile, and sad are taken from the same respondents and combined with secondary data for a total of 5,000 data points for training and testing. The test is carried out in real time on a humanoid robot using the two architectures. The face and emotion recognition accuracy is 85% and 64%, respectively, using the AlexNet model. VGG16 yields recognition accuracies of 100% and 73%, respectively. The proposed model architecture shows 87% and 67% accuracies for face recognition and emotion recognition, respectively. Thus, VGG16 performs better in recognizing faces as well as emotions, and it can be implemented in humanoid robots. This study also provides a method for measuring the distance between the recognized object and robot with an average error rate of 2.52%.

关键词： Face recognition Emotion recognition humanoid robots robots Convolutional neural networks Real-time systems Feature extraction Accuracy convolutional neural network emotion recognition face recognition humanoid robot

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Route outlining of humanoid robot on flat surface using MFO aided artificial potential field approach

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2022年第6-7期236卷 758-769页

作者： Kashyap, Abhishek Kumar Parhi, Dayal R. Kumar, Priyadarshi Biplab Natl Inst Technol Mech Engn Dept Robot Lab Near Hanuman Mandir Rourkela 769008 Odisha India Natl Inst Technol Mech Engn Dept Hamirpur HP India

humanoid robots, with their overall resemblance to a human body, is modeled for flawless interaction with human-made tools or the environment. In this study, navigation of humanoid robot using hybrid Artificial potential field (APF) and Moth flame optimization (MFO) approach have been performed. The hybrid approach provides the final turning angle (FTA), which is optimum to avoid collision with the hindrances. APF utilizes a negative potential field and a positive potential field to find the location of obstacles and target, respectively. The navigation starts towards the target;when the robot interacts with the obstacle, APF provides an intermediate angle (IA). The IA, along with the position of the obstacle, is fed into MFO as an input. This technique provides the FTA (optimum) to avoid collisions and guide a robot to the target. It is implemented in a single humanoid system and a multi-humanoid system. The presence of multiple humanoids can create the chance of inter-collision. It is dismissed by employing a dining philosopher controller to the proposed technique. Simulations and experiments are accomplished on simulated and real humanoid NAO. The coherency in the behavior of the results evaluated by the simulations and real-time experiments demonstrates the efficiency of the proposed AI technique. Comparisons are performed with a previously used method to validate the robustness of the technique.

关键词： Moth flame optimization artificial potential field obstacle avoidance humanoid robot dining philosopher

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Robust Walking for humanoid robot Based on Divergent Component of Motion

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MICROMACHINES 2022年第7期13卷 1095-1095页

作者： Zhang, Zhao Zhang, Lei Xin, Shan Xiao, Ning Wen, Xiaoyan Beijing Univ Civil Engn & Architecture Coll Elect & Informat Engn Beijing 102612 Peoples R China

In order to perform various complex tasks in place of humans, humanoid robots should walk robustly in the presence of interference. In the paper, an improved model predictive control (MPC) method based on the divergent components of motion (DCM) is proposed. Firstly, the humanoid robot model is simplified to a finite-sized foot-pendulum model. Then, the gait of the humanoid robot in the single-support phase (SSP) and double-support phase (DSP) is planned based on DCM. The center of mass (CoM) of the robot will converge to the DCM, which simplifies the feedback control process. Finally, an MPC controller incorporating an extended Kalman filter (EKF) is proposed to realize the tracking of the desired DCM trajectory. By adjusting the step duration, the controller can compensate for CoM trajectory errors caused by disturbances. Simulation results show that-compared with the traditional method-the method we propose achieves improvements in both disturbed walking and uneven-terrain walking.

关键词： humanoid robot model predictive control divergent component of motion gait planning extended Kalman filter

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A visual and neural evaluation of the affective impression on humanoid robot appearances in free viewing

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INTERNATIONAL JOURNAL OF INDUSTRIAL ERGONOMICS 2022年 88卷 103159-103159页

作者： Li, Mingming Guo, Fu Ren, Zenggen Duffy, Vincent G. Northeastern Univ Sch Business Adm Dept Ind Engn Shenyang 110167 Peoples R China Purdue Univ Sch Ind Engn W Lafayette IN 47906 USA

This study aims to characterize the fixation pattern and neutral dynamics underlying affective impressions on humanoid robot appearances, which have a moderate anthropomorphism, to provide an objective evaluation of the affective impressions. Thirty appearance pictures of existing humanoid robots leaving negative, neutral, or positive impressions were selected online. Users' eye-tracking and electroencephalography signals for the ap-pearances were co-registered in a free viewing paradigm, and eye-tracking metrics and eye fixation-related potentials were extracted. The results showed that the head attracted the most eye-tracking metrics, including glance count, fixation count, fixation time, and fixation duration. The torso attracted the second most, the legs and the hands attracted the third most, and the arms and feet drew the least. humanoid robot appearances giving positive impressions caught less glance counts and shorter fixation duration than the appearances giving negative or neutral impressions on both partial and whole appearances. The appearances giving positive impressions evoked the smallest P1 amplitude, and the appearances giving neutral impressions evoked the largest P1 amplitude, compared with that giving negative impressions. The findings suggest that glance count and fixation duration could distinguish the positive impression on humanoid robot appearances, and the P1 amplitude could distinguish the three affective *** to industry: These findings could promote the understanding of fixation pattern on humanoid robot appearances, which could be referred to directly when designing appearances for humanoid robots. Moreover, this study could provide designers with a visual and neural method of evaluating affective impressions on hu-manoid robot appearances in free viewing.

关键词： Affective impression humanoid robot Appearance evaluation Fixation pattern Eye fixation-related potentials

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Educators' perspectives on working with a humanoid robot in a French preschool class for autistic children

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JOURNAL OF RESEARCH IN SPECIAL EDUCATIONAL NEEDS 2023年第3期23卷 163-174页

作者： Garnier, Philippe Martel, Karine Dachez, Julie Audry, Pauline Bourgoin, Pascal Stawinski, Fleur INSHEA Grhapes EA 7287 Suresnes France INSHEA UPL Grhapes EA 7287 Suresnes France UEMA La Fontaine Jardins Quetigny France DANE Rectorat Acad Dijon Dijon France INSHEA Grhapes EA 7287 58-60 Av Landes F-92150 Suresnes France

humanoid robots have shown to provide interesting perspectives for autistic children. Yet, few studies have looked at educators' point of view on the use of such robots in educational settings. This paper presents a case study of the use of a humanoid robot, NAO, in a French preschool class dedicated to autistic children. Our work focuses on the perspectives of the adults working with these children. We interviewed the professional staff about changes in their teaching due to the presence of the robot, about the effects on the pupils' behaviour, and about the collaboration between the special needs teacher, the special needs support worker and the operational specialist from the digital education department of the district academic head office. Four themes were identified: joint reflection and collaboration when working with NAO, working with NAO, impact of NAO on pupils, impact of NAO on adults. The professionals underscored the necessity of adopting certain attitudes, in particular, the most neutral facial expression possible, so as not to disturb the children's learning process. They were under the impression that the robot had a beneficial effect on the pupils in terms of learning and social skills, and that it facilitated collaboration among them.

关键词： autism spectrum disorder humanoid robot learning professional practices

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LIFTING TASK STABILITY EVALUATION BASED ON BALANCED STATE BASINS OF A humanoid robot

LIFTING TASK STABILITY EVALUATION BASED ON BALANCED STATE BA...

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ASME International Design Engineering Technical Conferences / 43rd Annual Computers and Information in Engineering Conference (IDETC-CIE) / 47th Mechanisms and robotics Conference (MR)

作者： Song, Hyunjong Peng, William Z. Kim, Joo H. NYU Dept Mech & Aerosp Engn Brooklyn NY 10003 USA

ISBN: (纸本)9780791887363

Stability evaluation is a vital aspect of successful balance control and design for humanoid robots. While balance stability has been extensively explored for push recovery during legged locomotion tasks in response to perturbations, less effort has been devoted toward developing a similar understanding for lifting tasks. Lifting involves unique interactions between the robot and lifted object, whose mass can significantly alter the mass distribution of the loaded robot and the upper extremities, which are typically ignored in legged robot balance. In this study, the balance stability of a humanoid robot during a lifting task is evaluated with a partition-based approach in the augmented center-of-mass-state space. The balanced state boundary is computed through an optimization-based method that incorporates the loaded robot's whole-body system properties, such as kinematic and actuation limits, with full-order nonlinear system dynamics in the sagittal plane subject to foot-ground contact interactions and lifting task requirements. The boundaries are constructed for different combinations of object masses and lifting trajectories obtained with a zero-moment point constraint-based pattern generator. Trends in the boundaries and comparisons among them are used to identify the effect of different loading conditions and task parameters on balance stability due to kinematic and actuation limits, linear and angular momenta regulation, and mass distribution.

关键词： balanced state basins humanoid robot lifting upper-extremity momentum strategy whole body

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Online Gait Generation Method Based on Neural Network for humanoid robot Fast Walking on Uneven Terrain

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2022年第3期20卷 941-955页

作者： Zhong, Haoran Xie, Sicheng Li, Xinyu Gao, Liang Lu, Shengyu Huazhong Univ Sci & Technol HUST Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol 1037 Luoyu Rd Wuhan Hubei Peoples R China

Advanced humanoid robots highlight the ability of fast walking and adaptability to uneven terrain. However, owing to the complexity in walking dynamics, disturbances introduced by terrain height variations can adversely affect the bipedal walking performance. Moreover, to generate periodic gaits, most methods require to solve the gait generation problem by using nonlinear optimization approaches, resulting in difficulties for online control. To solve this problem, this paper proposes an online gait generation method to find periodic gaits for fast walking on uneven terrain by using a pre-trained neural network. First, to enhance the terrain adaptability, this paper proposes an improved walking pattern that allows the robots to skip the last single support phase. Such improvement enlarges the feasible step region when stepping down. A compensation strategy is also proposed to reduce the velocity tracking error. Then the improved whale swarm algorithm (IWSA) is applied to generate various datasets that cover the ranges of target velocities and terrain height variations. A back-propagation (BP) network is employed to train these datasets offline to learn the gait dynamics, which is further used to generate the optimal trajectories. Simulation results suggest that, compared with the current methods, the proposed method can solve the walking return map in a short time, with improvements in both maximum walking speed and terrain adaptability.

关键词： Bipedal walking humanoid robot neural network online gait generation uneven terrain

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Intercontinental Bilateral-by-Phases Teleoperation of a humanoid robot

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IEEE LATIN AMERICA TRANSACTIONS 2022年第1期20卷 64-72页

作者： Moya, Viviana Slawinski, Emanuel Mut, Vicente Wagner, Bernardo Univ Nacl San Juan Inst Automat INAUT J5400ARL San Juan Argentina Leibniz Univ Hannover Real Time Syst Grp D-30167 Hannover Germany

This document proposes a control scheme applied to delayed bilateral-by-phases teleoperation of the locomotion and manipulation of a humanoid robot, where each phase works depending on the decision of the human operator. Besides, balance control, adaptive controller, and force feedback are applied. The implementation is described and the results obtained from experiments of intercontinental teleoperation for a pick and place task with a NAO robot-driven at distance with a haptic device, are shown and summarized.

关键词： robots Hip humanoid robots Visualization robot sensing systems IEEE transactions Gold Locomotion manipulation intercontinental bilateral teleoperation time delay humanoid robot

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A System-Level Brain Model for Enactive Haptic Perception in a humanoid robot 32nd

A System-Level Brain Model for Enactive Haptic Perception in...

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32nd International Conference on Artificial Neural Networks (ICANN)

作者： Ingvarsdottir, Kristin Osk Johansson, Birger Tjostheim, Trond A. Balkenius, Christian Lund Univ Cognit Sci Lund Sweden

ISBN: (纸本)9783031442063;9783031442070

Perception is not a passive process but the result of an interaction between an organism and the environment. This is especially clear in haptic perception that depends entirely on tactile exploration of an object. We investigate this idea in a system-level brain model of somatosensory and motor cortex and show how it can use signals from a humanoid robot to categorize different object. The model suggests a number of critical properties that the sensorimotor system must have to support this form of enactive perception. Furthermore, we show that motor feedback during controlled movements is sufficient for haptic object categorization.

关键词： Enactive perception Affordances Haptic perception Object categorization humanoid robot

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