Design Of Humanoid Robot Research Articles

Design and Implementation of Humanoid Robot with Artificial Intelligence

Design and Implementation of Humanoid Robot with Artificial Intelligence

Understanding reciprocity in human–robot interactions through completion of a pregiving favor

In today’s world, understanding how people interact with humanoid social robots is important for day-to-day interactions and design purposes. A phasic, between-subjects, psychophysiological experiment (N = 72) examined how the norm of reciprocity influenced interactions with the humanoid social robot, “Pepper”. Facial electromyography (zygomatic and corrugator) was measured to determine participant’s emotional valence during interaction. The level of reciprocity in response to a pregiving favor was measured by the number of raffle tickets purchased by participants at the robot’s request. Results suggest that the social rule of reciprocation exists within human–robot interaction. When Pepper offered a pregiving favor to a participant, that person was more likely to reciprocate via the robot’s later ticket purchase request. Contributions to theory and design of humanoid social robots are discussed, as well as avenues for future research.

Autoencoder-enabled eye-tracking data analytics for objective assessment of user preference in humanoid robot appearance design

Humanoid robots have been extensively utilized in service industries to provide information and product delivery through direct interactions with users. As the design of humanoid robot appearance significantly impacts human-robot interactions, it is crucial to assess user preference towards it. Traditional evaluation tools, such as surveys, field observations, and interviews, are often time-consuming and subjective. Therefore, this study aims to develop a novel eye-tracking-based assessment tool to investigate user preference towards humanoid robot appearance design. We analyze the critical factors influencing user preference from two perspectives: the attributes of robot appearance and users' selective attention distribution. Accordingly, we propose an integrated machine learning method, combining an autoencoder neural network with a support vector machine to handle the collected visual data. This method, named ASVM, extracts several novel indicators from the eye-tracking data via an unsupervised autoencoder neural network and manual entropy analysis. The proposed ASVM achieves an accuracy of 91%, outperforming other classical machine learning methods, including decision tree, naive Bayes, and support vector machine. ASVM can objectively assess user preference towards humanoid robot appearance design with high time resolution. Furthermore, it can enhance humanoid robot design by revealing the visual attention distribution in assessing robot appearance.

Humanoid robot-empowered language learning based on self-determination theory

With the ability to provide feedback and assistance, humanoid educational robots have been proven effective in assisting students to overcome learning challenges and enhancing individual learning outcomes. However, the strength of humanoid robots in promoting social and emotional skills has not been well investigated. Socially supportive behaviour can contribute more to students’ learning engagement than knowledge transfer. This study focuses on the design of humanoid robots to engage students from functional and affective perspectives. To this end, a pilot test is conducted on 64 primary school students in Hong Kong, comprising a control group (N = 33) and an experimental group (N = 31). Questionnaires, observations, and language proficiency test are done to ensure the validity of the findings. The results show that the experimental group, which learned with the humanoid robots, significantly improved their behavioural engagement (+ 13.24%), emotional engagement (+ 13.14%), cognitive engagement (21.56%), and intrinsic motivation (12.07%). The impact of utilizing humanoid robots in education is elucidated through the lens of the self-determination theory (SDT), which pertains to students’ learning motivation and engagement. These findings can provide valuable insights into the development of more captivating humanoid robots for extensive learning purposes.

3-D Tactile-Based Object Recognition for Robot Hands Using Force-Sensitive and Bend Sensor Arrays

Tactile sensing is a particularly important and challenging task for a modern robot to safely manipulate objects, interact with humans in a shared space, and provide various services. This paper presents a 3D tactile glove for robots with the combination of a piezoresistive-based force sensor array (412 sensors) covering the full hand and a resistive bend sensor array (5 sensors) on the back of five fingers. Deep learning-based CNN (Convolutional Neural Network) methods using the designed tactile glove are proposed for object recognition. In the experiment for recognizing 15 objects with a dexterous robot hand, an average classification accuracy of 93.67% has been achieved. Comparison experiments with three other typical classifiers (the Quadratic SVM, Weighted KNN, and Bagged Trees) and our CNN methods show an average recognition accuracy of 91.67% with the 3D tactile glove, revealing an accuracy improvement of 4.17% over only using the force sensor array. We further apply our 3D tactile glove and the multi-modal CNN to identify three other objects and demonstrate their generalization ability of tactile object recognition with an average success accuracy of 78.33%. The proposed 3D tactile glove can be further used in human-robot interactions, the design of prosthetics and humanoid robots, and for improving the intelligence level in brain-computer collaborative systems.

An Autonomous Humanoid Robot Designed to Assist a Human with a Gesture Recognition System

This paper presents the design of an autonomous humanoid robot designed to optimize and enrich customer service in showrooms, e.g., electronic equipment, mobile network operators, and generally in stores with various articles. The proposed humanoid robot design is distinguished by two key components: a sensor-equipped mobile platform with drives and a body featuring a head outfitted with a touch tablet and an RGBD camera. The control system enables autonomous navigation in both known and uncharted environments, with a special focus on diverse, crowded, and cluttered spaces. To enhance its adaptability, this robot is not only fitted with LIDAR sensors but also cliff and ultrasonic sensors. While the interactive ability with humans is an expected functionality, this paper brings forth certain distinct innovations in humanoid robot design for customer service. One of these unique aspects includes the robot’s ability to physically alter its configuration, such as rotating its head and adjusting the height of its torso to maintain line-of-sight with the customer. This capability signifies a novel degree of spatial responsiveness that exceeds static interaction. Moreover, the proposed robot is equipped with a user-friendly gesture recognition system, uniquely designed to detect and recognize simple human hand gestures. This attribute paves the way for understanding simple commands such as requests for assistance. Upon recognizing a request, the robot tailors its services by following the person around the showroom, effectively assisting and answering customer queries or displaying requisite information on its screen. This active assistance model, specifically tailored for human interaction, showcases the robot’s unique capability to respond proactively and dynamically to human inputs.

Design and analysis of smart assistive humanoid robot for isolated patients

ABSTRACT People across the globe witnessed the meteoric rise of the recently discovered coronavirus, which caught governments, healthcare systems, and civilisations off guard. Robotics, self-governing systems, and intelligent devices can contribute positively to a rapid epidemic. Smart assistive robots can help patients and healthcare staff in general and make the anticipation, containment, and exacerbation of coronavirus easier by highlighting the coronavirus burden on healthcare structures during a crisis. In this type of situation, a smart-assistive robot with aid capabilities is promising, especially for those putting their lives in jeopardy by interacting with ill patients in hygienic environments. By providing regular monitoring and patient assistance, the intelligent helper robot could assist in lowering the danger of illness spreading to others. Concerning the application this paper discusses, a robot with a wheeled base and a humanoid structure can aid isolated patients and connect wirelessly anywhere in the world (using the Internet of Things) with patients far away more effectively without any physical touch. The designed robot is incredibly light (approximately 7 kg) and easily transported to the appropriate location (any hospital or medical camp, for example). Its modular design allows quick assembly and disassembly. The robot can comfortably handle 10–15 kg of load on its holding plate. This study also explains the structural capability and method of directing the robot in an unfamiliar area using ultrasonic sensors for obstacle avoidance, a camera module for real-time vision, and a Skype call feature that may enable doctors to keep tabs on a patient’s health.

Foot function enabled by human walking dynamics.

Bipedal walking, the habitual gait for man, is rather unique in nature and poses particular challenges for balance and propulsion. The characteristic double-humped ground reaction force profile has been widely observed but not put into functional context. We propose a mathematical model that captures the dynamics of the human foot in walking including the characteristic motion of the center of pressure. Using this model, we analyze the functional interplay of all essential biomechanical contributors to foot dynamics in walking. Our results demonstrate the intricate interplay of a self-stabilizing mechanism which allows extending a leg's stance phase while simultaneously powering rapid swing by condensing the essentials of foot dynamics into a reductionist, biomechanical model. A theory is presented which identifies the foot to be the key functional element and which explains the global dynamics of human walking. The provided insights will impact gait therapy and rehabilitation, the development of assistive devices, such as leg prostheses and exoskeletons, and provide guidelines for the design and control of versatile humanoid robots.

Gender role stereotypes at work in humanoid robots

ABSTRACT Research on gender role stereotypes activated in interactions with humanoid robots has yet to produce conclusive knowledge. To analyse how much, and in which way, gender role stereotypes used in interactions with humans are also called into play in interactions with humanoid robots, a study was conducted with 240 participants. The study was an online survey in which a scale was used for determining the appropriateness for performing four stereotypically masculine and four feminine by humans and robots. Overall, eight humanoid robots – four judged feminine and four masculine – were considered. Results showed that gender role stereotypes are activated for both genders, but men most strongly activate those pertaining to male roles. These stereotypes are also adopted in reference to humanoid robots, though robots are generally considered less suitable for performing female roles. Furthermore, an increased degree of similarity of robots to humans has a positive effect in assessing the appropriateness to perform female roles only for female robots. The same does not happen with male robots. These results suggest that male and female robots are not categorised in the same way. Robots are essentially perceived as male entities, a particularly relevant hypothesis for the gender-sensitive design of humanoid robots.

User Interface Design of Humanoid Robot at Library: Case of Chemnitz University of Technology

The university library is one of the key places for students at higher education institutions. Some universities support their students with their literature search and research with humanoid robots. The Chemnitz University of Technology (CUT) is one of pioneer universities where a humanoid robot will assist at the university library. This paper describes user interface requirement and its programming, testing and implementation of humanoid robot at the university library. The programmed humanoid robot should support students and visitors with useful guidance and hints in finding materials they need for their research and study. For programming of the robot, the programming language Python was used and the implementation was tested with limited number of users due to the pandemic situation.

Interpretation of Humanoid Design towards ASD Learning Abilities: Theoretical framework

The humanoid robot has been used in part of the intervention programme for Autism Spectrum Disorder (ASD). Some studies address what kind of form and functionality a human-like robot should have to be socially accepted by them as Autism Spectrum Disorder (ASD) children's motivation faces complex challenges. This research aims to study the specific factor, problems and connection elements between the contexts of issues related to the interpretation of humanoid design toward ASD learning abilities. All the studied variables identified from the literature of recent theory models were summarized and arranged accordingly to form the conceptual framework. Keywords: Humanoid Robot Design; Personalisation; New Product Development; ASD Learning Abilities eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v7iSI7%20(Special%20Issue).3807

Action Planning and Design of Humanoid Robot Based on Sports Analysis in Digital Economy Era

<p style="text-align: justify;"><span style="font-family: "times new roman"; font-size: 16px;">With the rapid development of digital economy era, robot will become an important part of the future social development. At present, many researchers at home and abroad are also engaged in the field of robotics. Humanoid robot is one of the most active research hotspots in the field of intelligent robot because of its unique walking mode. Based on the above background, the purpose of this paper is to study the motion planning and design of humanoid robot based on sports analysis. In this study, the motion mode of the robot is generated based on the analysis of human sports movement, and the stability of its gait is controlled based on the ZMP control strategy, and the stable walking of the robot is realized. Firstly, the stable walking conditions of humanoid robot are analyzed, and the ZMP position of multi particle and single particle humanoid robot model is solved; secondly, the real-time ZMP measurement of robot is completed based on the pressure sensor of robot sole, and the algorithm optimization solution of robot hip joint angle is carried out based on ZMP control strategy, The dynamic simulation experiments in webots show that the ZMP point of the robot is always in the stable region during the walking process, which proves the validity of the gait pattern generated based on ZMP. Finally, the humanoid evaluation of the robot walking process is completed. The results of similarity function show that the gait similarity between robot and human body is more than 70% in most of the time, It can meet the human requirements of robot gait.</span>

Design and Acceleration of Humanoid Robot Controlled by Stm32

With the development of the industrial internet, the comprehensive application of big data, the internet of things and artificial intelligence are becoming quietly extensive, and they connect physical things to the digital world tightly. Being smart and intelligent has become one of the most important directions of future social development. As an agent, robots will be widely utilized in every corner of society, and the robot era has come quietly.This paper mainly studies how to let the robot walk according to the prescribed route as a person. Our innovation points are as follows: Firstly, compared with the traditional packaging robot, the steering gear of the legs is similar to that of normal people, and the hands, arms, and heads can also cooperate in the march, just like normal people. Tracking, individual turns, and special gestures can be achieved. Under the combined action of the threshold, CCD, sensor, and 12 steering gear, the robot can complete the patrol walk at a fast speed.

Improvements of SYCU Humanoid Robot

This paper summarizes and describes our SYCU-Legendary team in terms of humanoid robot design and the progress that we have made. Aiming to win a higher ranking on the upcoming 2020 RoboCup Humanoid Kid-Size competition, some changes made on our robot soccer players, such as mechanical design, electrical configuration, sensors and the control system, are introduced. The robot intelligence is improved by optimizing the computation. Also, information about our past participations and honours in humanoid robot soccer championships held in Montreal of Canada, Sydney of Australia, respectively, are presented.

Large-Scale Vision-Based Tactile Sensing for Robot Links: Design, Modeling, and Evaluation

The sense of touch allows individuals to physically interact with and better perceive their environment. Touch is even more crucial for robots, as robots equipped with thorough tactile sensation can more safely interact with their surroundings, including humans. This article describes a recently developed large-scale tactile sensing system for a robotic link, called TacLINK, which can be assembled to form a whole-body tactile sensing robot arm. The proposed system is an elongated structure comprising a rigid transparent bone covered by continuous artificial soft skin. The soft skin of TacLINK not only provides tactile force feedback but can change its form and stiffness by inflation at low pressure. Upon contact with the surrounding environment, TacLINK perceives tactile information through the three-dimensional (3-D) deformation of its skin, resulting from the tracking of an array of markers on its inner wall by a stereo camera located at both ends of the transparent bone. A finite element model (FEM) was formulated to describe the relationship between applied forces and the displacements of markers, allowing detailed tactile information, including contact geometry and distribution of applied forces, to be derived simultaneously, regardless of the number of contacts. TacLINK is scalable in size, durable in operation, and low in cost, as well as being a high-performance system, that can be widely exploited in the design of robotic arms, prosthetic arms, and humanoid robots, etc. This article presents the design, modeling, calibration, implementation, and evaluation of the system.

User-centred design of humanoid robots’ communication

Abstract Interaction between humans and robots will benefit if people have at least a rough mental model of what a robot knows about the world and what it plans to do. But how do we design human-robot interactions to facilitate this? Previous research has shown that one can change people’s mental models of robots by manipulating the robots’ physical appearance. However, this has mostly not been done in a user-centred way, i.e. without a focus on what users need and want. Starting from theories of how humans form and adapt mental models of others, we investigated how the participatory design method, PICTIVE, can be used to generate design ideas about how a humanoid robot could communicate. Five participants went through three phases based on eight scenarios from the state-of-the-art tasks in the RoboCup@Home social robotics competition. The results indicate that participatory design can be a suitable method to generate design concepts for robots’ communication in human-robot interaction.

A Robot Is Not Worth Another: Exploring Children's Mental State Attribution to Different Humanoid Robots.

Recent technological developments in robotics has driven the design and production of different humanoid robots. Several studies have highlighted that the presence of human-like physical features could lead both adults and children to anthropomorphize the robots. In the present study we aimed to compare the attribution of mental states to two humanoid robots, NAO and Robovie, which differed in the degree of anthropomorphism. Children aged 5, 7, and 9 years were required to attribute mental states to the NAO robot, which presents more human-like characteristics compared to the Robovie robot, whose physical features look more mechanical. The results on mental state attribution as a function of children’s age and robot type showed that 5-year-olds have a greater tendency to anthropomorphize robots than older children, regardless of the type of robot. Moreover, the findings revealed that, although children aged 7 and 9 years attributed a certain degree of human-like mental features to both robots, they attributed greater mental states to NAO than Robovie compared to younger children. These results generally show that children tend to anthropomorphize humanoid robots that also present some mechanical characteristics, such as Robovie. Nevertheless, age-related differences showed that they should be endowed with physical characteristics closely resembling human ones to increase older children’s perception of human likeness. These findings have important implications for the design of robots, which also needs to consider the user’s target age, as well as for the generalizability issue of research findings that are commonly associated with the use of specific types of robots.

Inverse optimal control with time-varying objectives: application to human jumping movement analysis

Analysis of complex human movements can provide valuable insights for movement rehabilitation, sports training, humanoid robot design and control, and human–robot interaction. To accomplish complex movement, the central nervous system must coordinate the musculo-skeletal system to achieve task and internal (e.g., effort minimisation) objectives. This paper proposes an inverse optimal control approach for analysing complex human movement that does not assume that the control objective(s) remains constant throughout the movement. The movement trajectory is assumed to be optimal with respect to a cost function composed of the sum of weighted basis cost functions, which may be time varying. The weights of the cost function are recovered using a sliding window. To illustrate the proposed approach, a dataset consisting of standing broad jump to targets at three different distances is collected. The method can be used to extract control objectives that influence task success, identify different motion strategies/styles, as well as to observe how control strategy changes during the motor learning process. Kinematic analysis confirms that the identified control objectives, including centre-of-mass takeoff vector and foot placement upon landing are important to ensure that a given participant lands on the target. The dataset, including nearly 800 jump trajectories from 22 participants is also provided.

Design and Development of Low Cost Humanoid Robot with Thermal Temperature Scanner for COVID-19 Virus Preliminary Identification

Design and Development of Low Cost Humanoid Robot with Thermal Temperature Scanner for COVID-19 Virus Preliminary Identification

Unsupervised Pedestrian Pose Prediction: A Deep Predictive Coding Network-Based Approach for Autonomous Vehicle Perception

Pedestrian pose prediction is an important topic, related closely to robotics and automation. Accurate predictions of human poses and motion can facilitate a more thorough understanding and analysis of human behavior, which benefits real-world applications such as human-robot interaction, humanoid and bipedal robot design, and safe navigation of mobile robots and autonomous vehicles. This article describes a deep predictive coding network (PredNet)-based approach for unsupervised pedestrian pose prediction from 2D camera imagery and provides experimental results of two real-world autonomous vehicle data sets. The article also discusses topics for future work in unsupervised and semisupervised pedestrian pose prediction and its potential applications in robotics and automation systems.