Bacterial foraging optimization algorithm based on normal cloud model for forward kinematics of a 4-DOF parallel manipulator

Shenli, Wu; Xiaoming, Liang; Liu, Ling; Zimeng, Yao

doi:10.1080/00150193.2022.2078128

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…The human body normally walks in a periodic motion, to one side of the foot heel landing from the beginning until the side of the foot heel landing, known as a gait cycle. In order to overcome the important problem that the contact point between the foot and the ground is constantly changing in the actual movement, and the contact point has a non-zero acceleration, this paper rotates to derive the human motion law from the center of mass of the upper limb [22].…”

Section: Walking Kinematics Model For Physical Trainingmentioning

confidence: 99%

A Risk Assessment System for Sports Training Injuries Based on Artificial Intelligence and Big Data

Liu

2024

Applied Mathematics and Nonlinear Sciences

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This study helps athletes avoid and reduce the risk of injury in training more effectively by constructing a sports training injury risk assessment system to ensure they can train and compete safely and healthily. Based on the B/S model and .NET framework, this paper successfully develops a sports training injury risk assessment system and proposes a human exercise training detection program. The system integrates the measurement of physiological parameters such as blood oxygen saturation and blood pressure changes. It constructs a kinematic model to analyze the forces in training through inverse dynamics. In the system test, the response time was only 0.09ms/frame and the standby power consumption was as low as 11.43mW, demonstrating superior operational and energy efficiency. In addition, it was found that under specific conditions, such as after holding breath for 27.5s, the non-contact oximetry measurement showed a strong linear relationship with the physiological parameter detection module, which may predict the risk of falling when the peak motion acceleration SMV exceeds 3.23m2/s. Through this system, athletes can understand their body stress and physiological changes in the training process in real time, effectively avoiding potential training injuries, thus safeguarding their training safety and health.

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Section: Walking Kinematics Model For Physical Trainingmentioning

confidence: 99%

A Risk Assessment System for Sports Training Injuries Based on Artificial Intelligence and Big Data

Liu

2024

Applied Mathematics and Nonlinear Sciences

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“…Humanoid robots move using synchronized joint rotations to achieve specific positions, resulting in continuous, coordinated movements [12]- [15]. Inverse kinematics determines body coordinate positions and destination coordinates of other limbs by calculating the input angle [16]- [19], while forward kinematics calculates limb and center of mass (COM) coordinates [20]- [22].…”

Section: Introductionmentioning

confidence: 99%

Humanoid Walking Control Using LQR and ANFIS

Auzan,

Lelono,

Dharmawan

2023

Journal of Robotics and Control

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Humanoid robots possess remarkable mobility and adaptability for diverse environments. Nonetheless, accurate walking pattern tracking remains challenging, especially when employing the linear quadratic regulator (LQR) due to delays in high-mobility setpoint tracking. We propose a novel control approach to address this limitation by integrating an artificial neuro-fuzzy inference system (ANFIS) with the LQR to enhance pattern tracking. The research contributes to developing a control system that combines LQR and ANFIS to enable humanoid robots to follow various walking patterns with increased precision and efficiency and also the scheme to incorporate LQR and ANFIS. The study involves four experiments: step response, walking phase, static straight walking, and varied straight walking. Each test runs for 5 seconds with a 100-millisecond sampling rate, repeated five times, and employs the Integral Absolute Value (IAE) metric for evaluation. The LQR-ANFIS method exhibits superior performance, achieving a maximum overshoot of 0%, a rise time of 0.3 seconds, a settling time of 0.3 seconds, and a steady-state error of 0% in the step response experiment. The proposed control system also enables stable walking with step periods ranging from 0.15 to 4 seconds and step ranges of 0.05 to 0.03 meters. In conclusion, the integration of ANFIS with the LQR significantly enhances the mobility of humanoid robots, enabling them to navigate diverse environments and accurately track various walking patterns proficiently.

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Research on Forward Kinematics Solutions of 3RPUPc-UPS Parallel Mechanism Based on Particle-Artificial Bee Colony Algorithm

Chang,

Zhang,

2023

Lecture Notes in Computer Science

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Bacterial foraging optimization algorithm based on normal cloud model for forward kinematics of a 4-DOF parallel manipulator

Cited by 4 publications

References 8 publications

A Risk Assessment System for Sports Training Injuries Based on Artificial Intelligence and Big Data

A Risk Assessment System for Sports Training Injuries Based on Artificial Intelligence and Big Data

Humanoid Walking Control Using LQR and ANFIS

Research on Forward Kinematics Solutions of 3RPUPc-UPS Parallel Mechanism Based on Particle-Artificial Bee Colony Algorithm

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