A comprehensive survey on humanoid robot development

Saeedvand, Saeed; Jafari, Masoumeh; Aghdasi, Hadi S.; Baltes, Jacky

doi:10.1017/s0269888919000158

Cited by 60 publications

(25 citation statements)

References 75 publications

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“…The choice has been made to implement the Apache Kafka broker, while it has a universal protocol, enables to be implemented in any component of system, including sensor nodes. A sensor node consists of a hardware sensor (e.g., a microphone or passive infrared sensor [14]) and a communication enabling Embedded System. A sensor node (ES with a hardware sensor) is designed to take advantage of the Smart Infrastructure, so that it would return high-level result.…”

Section: Communication Between Components and Remote Sas Reliabilitymentioning

confidence: 99%

Human Interaction Smart Subsystem—Extending Speech-Based Human-Robot Interaction Systems with an Implementation of External Smart Sensors

Podpora

Gardecki

Beniak

et al. 2020

Sensors

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This paper presents a more detailed concept of Human-Robot Interaction systems architecture. One of the main differences between the proposed architecture and other ones is the methodology of information acquisition regarding the robot's interlocutor. In order to obtain as much information as possible before the actual interaction took place, a custom Internet-of-Things-based sensor subsystems connected to Smart Infrastructure was designed and implemented, in order to support the interlocutor identification and acquisition of initial interaction parameters. The Artificial Intelligence interaction framework of the developed robotic system (including humanoid Pepper with its sensors and actuators, additional local, remote and cloud computing services) is being extended with the use of custom external subsystems for additional knowledge acquisition: device-based human identification, visual identification and audio-based interlocutor localization subsystems. These subsystems were deeply introduced and evaluated in this paper, presenting the benefits of integrating them into the robotic interaction system. In this paper a more detailed analysis of one of the external subsystems-Bluetooth Human Identification Smart Subsystem-was also included. The idea, use case, and a prototype, integration of elements of Smart Infrastructure systems and the prototype implementation were performed in a small front office of the Weegree company as a decent test-bed application area.The days, when simply having a robot was impressive, are gone. The technology should be more than just presenting content. It should be truly interactive. The authors of this work evaluated methods for the purpose of measurement and influencing the Human-Robot interaction User Experience, (see Reference [1]), concluding that the robot's interaction is perceived as more natural if the robot is able to personalize its communication content towards a particular interlocutor. It became clear, that the system should be able to get as much information as possible during and before the conversation takes place [2]. One of the key aspects in a healthy conversation is emotional state recognition. All the improvements of the robots presented in this paper are aiming for natural, real integration with human customers with the implementation of Artificial Intelligence [3,4].The authors of this work have indicated extending the robot's capabilities by adding additional external wireless sensors in Reference [2], however without Smart Infrastructure. Implementation of a remote PID (Passive Infrared Detector) sensor in order to get a signal "someone is coming" seemed to be enough at that stage.In this paper an extended approach is being presented, introducing Human Identification Smart Subsystems (HISS)-a set of external sensors, which enable us to obtain much more valuable information, resulting from the communication with the Smart Infrastructure [5] or databases of the customer. The idea presented in this paper has been successfully verified and implemented and the con...

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Section: Communication Between Components and Remote Sas Reliabilitymentioning

confidence: 99%

Human Interaction Smart Subsystem—Extending Speech-Based Human-Robot Interaction Systems with an Implementation of External Smart Sensors

Podpora

Gardecki

Beniak

et al. 2020

Sensors

View full text Add to dashboard Cite

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“…On the other hand, bipedal robots are also of interest to researchers due to their humanoid structures and hence the ability to mimic agile human locomotion [6]. In comparison to quadruped robots, humanoid robots usually have a higher level of flexibility and adaptability to different environments and applications.…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal Legged Locomotion Framework with Automated Residual Reinforcement Learning

Chen¹,

Rosendo²

2022

Preprint

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While quadruped robots usually have good stability and load capacity, bipedal robots offer a higher level of flexibility / adaptability to different tasks and environments. A multi-modal legged robot can take the best of both worlds. In this paper, we propose a multi-modal locomotion framework that is composed of a hand-crafted transition motion and a learning-based bipedal controller-learnt by a novel algorithm called Automated Residual Reinforcement Learning. This framework aims to endow arbitrary quadruped robots with the ability to walk bipedally. In particular, we 1) design an additional supporting structure for a quadruped robot and a sequential multi-modal transition strategy; 2) propose a novel class of Reinforcement Learning algorithms for bipedal control and evaluate their performances in both simulation and the real world. Experimental results show that our proposed algorithms have the best performance in simulation and maintain a good performance in a real-world robot. Overall, our multi-modal robot could successfully switch between biped and quadruped, and walk in both modes. Experiment videos and code are available at https://chenaah.github.io/multimodal/.

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“…In the past 50 years, research institutions and scholars at home and abroad have carried out much research on biped robots and have achieved remarkable progress. 1,2 For example, humanoid robot HRP-4 can drive vehicles in the middle of the road, 3 and ATLAS can traverse obstacles and climb stairs. 4 Bipedal walking is the most important and challenging problem in research on biped robot motion.…”

Section: Introductionmentioning

confidence: 99%

A grid gradient approximation method of energy-efficient gait planning for biped robots

Zhiqiang

Yuanbing

Chai

et al. 2021

International Journal of Advanced Robotic Systems

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In this article, an energy-efficient gait planning algorithm that utilizes both 3D body motion and an allowable zero moment point region (AZR) is presented for biped robots based on a five-mass inverted pendulum model. The product of the load torque and angular velocity of all joint motors is used as an energy index function (EIF) to evaluate the energy consumption during walking. The algorithm takes the coefficients of the finite-order Fourier series to represent the motion space of the robot body centroid, and the motion space is gridded by discretizing these coefficients. Based on the geometric structure of the leg joints, an inverse kinematics method for calculating grid intersection points is designed. Of the points that satisfy the AZR constraints, the point with the lowest EIF value in each network line is selected as the seed. In the neighborhood of the seed, the point with the minimum EIF value in the motion space is successively approximated by the gradient descent method, and the corresponding joint angle sequence is stored in the database. Given a distance to be traveled, our algorithm plans a complete walking trajectory, including two starting steps, multiple cyclic steps, and two stopping steps, while minimizing the energy consumption. According to the preset AZR, the joint angle sequences of the robot are read from the database, and these sequences are adjusted for each step according to the zero-moment-point feedback during walking. To determine the effectiveness of the proposed algorithm, both dynamic simulation and walking experiment in the real environment were carried out. The experimental results show that compared with algorithms based on the fixed body height or vertical body motion, our gait algorithm has a significant energy-saving effect.

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A comprehensive survey on humanoid robot development

Cited by 60 publications

References 75 publications

Human Interaction Smart Subsystem—Extending Speech-Based Human-Robot Interaction Systems with an Implementation of External Smart Sensors

Human Interaction Smart Subsystem—Extending Speech-Based Human-Robot Interaction Systems with an Implementation of External Smart Sensors

Multi-Modal Legged Locomotion Framework with Automated Residual Reinforcement Learning

A grid gradient approximation method of energy-efficient gait planning for biped robots

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