Simplified Method for Humanoid Robot Gait Generation

Szumowski, Maksymilian; Zurawska, Magdalena; Zielińska, Teresa

doi:10.1007/978-3-030-20131-9_224

Cited by 4 publications

(6 citation statements)

References 5 publications

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“…When generating motion primitives the previously designed and evaluated Motion Generation System (MGS) [14] and [13] is used. Structure of this system is presented in �igure 2.…”

Section: General Concept Of Path Planningmentioning

confidence: 99%

Humanoid Robot Path Planning Using Rapidly Explored Random Tree and Motion Primitives

Szumowski¹,

Zielińska²

2024

JAMRIS

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Path planning is an essential part of the control system of any mobile robot. In this article the path planner for a humanoid robot is presented. The short description of an universal control framework and the Motion Generation System is also presented. Described path planner utilizes a limited number of motions called the Motion Primitives. They are generated by Motion Generation System. Four different algorithms, namely the: Informed RRT, Informed RRT with random bias, and RRT with A* likeheuristics were tested. For the last one the version with biased random function was also considered. All mentioned algorithms were evaluated considering three dif ferent scenarios. Obtained results are described and discussed.

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“…When generating motion primitives the previously designed and evaluated Motion Generation System (MGS) [14] and [13] is used. Structure of this system is presented in �igure 2.…”

Section: General Concept Of Path Planningmentioning

confidence: 99%

Humanoid Robot Path Planning Using Rapidly Explored Random Tree and Motion Primitives

Szumowski¹,

Zielińska²

2024

JAMRIS

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“…For stable walking movements, the preview control method is one technique that is often implemented. Such research was conducted by [10]; the walking pattern on the robot is formed by modeling the robot as an extended cart table. With this modeling, the Zero Moment Point (ZMP) trajectory on the robot is formed based on the footrest point that the robot will pass, or can be simplified by the footrest of the table.…”

Section: Motion Generationmentioning

confidence: 99%

“…Then, these values are used as a reference to form a trajectory for the entire joint of the robot. Unlike the motion formation technique proposed by [10], in this technique, the resulting trajectory is a trajectory in the joint space. The advantage of our proposed method is that there is no need for inverse kinematics equations to convert the world space trajectory to the joint space trajectory.…”

Section: Motion Generationmentioning

confidence: 99%

Automatic Speech Recognition for Human-Robot Interaction on The Humanoid Robot Barelang 7

Mawaddah,

Tamba,

Hutagalung

et al. 2024

Proceedings of the 6th International Conference on Applied Engineering, ICAE 2023, 7 November 2023, Batam, Riau Islands, Indone

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This paper proposes a Human-Robot Interaction (HRI) system through voice commands. The study used the Barelang 7 humanoid dancing robot platform comprising 29 Degrees of Freedom (DoF). Automatic Speech Recognition (ASR) was developed using Python programming language and the VOSK Offline Speech Recognition toolkit to recognize five instructions spoken by random speakers. The voice recognition results in ASR are then sent to the robot via serial communication to realize movements according to spoken instructions. From tests conducted on five speakers randomly with 100 attempts, a Word Error Rate (WER) value of 0.096 was obtained. Meanwhile, testing the entire HRI system achieved a success rate of 92%. These results indicate that the HRI system has been successfully implemented on humanoid robots, although there are still some errors in the ASR system, that affect the overall performance of the HRI system. The results of this research contribute to the research and development of HRI systems, especially in humanoid robots, which are still not widely studied.

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“…Here, we use the Motion Generation System (MGS). This system was developed and tested on our previous platform [28,29]. Fig.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The trajectory of the characteristic points include the feet trajectory for each transfer phase and also hands trajectory for whole motion. More details about the feet and hands motion, and the transfer phases can be found in [29,30].…”

Section: Problem Formulationmentioning

confidence: 99%

Archive of Mechanical Engineering

Mikołajczyk,

Szumowski,

Woliński

2022

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A method of solving the inverse kinematics problem for a humanoid robot modeled as a tree-shaped manipulator is presented. Robot trajectory consists of a set of trajectories of the characteristic points (the robot's center of mass, origins of feet and hands frames) in the discrete time domain. The description of motion in the frame associated with the supporting foot allows one to represent the robot as a composite of several serial open-loop redundant manipulators. Stability during the motion is provided by the trajectory of the robot's center of mass which ensures that the zero moment point criterion is fulfilled. Inverse kinematics solution is performed offline using the redundancy resolution at the velocity level. The proposed method utilizes robot's redundancy to fulfill joint position limits and to reduce gravity-related joint torques. The method have been tested in simulations and experiments on a humanoid robot Melson, and results are presented.

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Simplified Method for Humanoid Robot Gait Generation

Cited by 4 publications

References 5 publications

Humanoid Robot Path Planning Using Rapidly Explored Random Tree and Motion Primitives

Humanoid Robot Path Planning Using Rapidly Explored Random Tree and Motion Primitives

Automatic Speech Recognition for Human-Robot Interaction on The Humanoid Robot Barelang 7

Archive of Mechanical Engineering

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