Micro-amplitude vibration measurement using vision-based magnification and tracking

Wang, Xi; Li, Fēi; Du, Qianzheng; Zhang, Yang; Wang, Tao; Fu, Guoqiang; Lu, Caijiang

doi:10.1016/j.measurement.2023.112464

Cited by 7 publications

(2 citation statements)

References 33 publications

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“…This study develops a parameter-tuning method based on the Bayesian optimization algorithm [25]- [29], which is noted for its efficiency in parameter search among various optimization methods. As shown in Table II, various other optimization methods can achieve optimization with a small number of search trials, [17] ✘Required (assumed to be given) -Using high speed camera [14]- [16] ✘Required (obtained by sensors) Using internal sensors [13] [18] SSA-based method* [19] ✘Required (identified by searching) PSO-based method* [20] BO-based method* [21] Hardware-based method Installing additional structures or mechanisms [7]- [10] ✘Required -* PSO, SHA, BO, and SSA refer to particle swarm optimization, S. Lin's heuristic algorithm, Bayesian optimization, and sparrow search algorithm, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, this paper proposes a novel, easily installable, and cost-effective system for measuring the vibrations of a robotic hand, which is a criterion for the optimization. [18]- [22] used expensive high-speed cameras and internal sensors requiring additional structures; conversely, this study uses a generalspecification camera (30 fps, 640×360 pixels), thereby reducing costs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing Motion Parameters in Soft Robotic Hands Using Bayesian Optimization: Enhancing Cycle Time, Addressing Vibration, and Repeatability

Nishimura,

Isogai,

Suzuki

et al. 2024

IEEE Access

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This study proposes a novel motion optimization method for a manipulator equipped with a soft robotic hand for object transportation. The flexibility of the soft robotic hand induces large vibrations. The manipulator must pause until the vibration converges, leading to an increase in the cycle time. The robotic system also has issue with the low motion repeatability in the robotic hand, even under identical operational conditions. In this study, a method based on Bayesian algorithms is developed to optimize the motion of a manipulator. The objective is to minimize the cycle time for object transportation tasks, while considering the challenges of vibration and low repeatability in soft robotic hand systems. The optimization is performed through an exploratory search using actual experiments. To achieve a low-cost and versatile measurement system, this study proposes a method for deriving the cycle time, which is a key metric for optimization, based on the measurement results obtained using a web camera with standard specifications. The proposed optimization method is evaluated through a comparison with existing optimization methods, including the grid-search-based, conventional Bayesian optimization, particle swarm optimization, S. Lin's heuristic algorithm, and sparrow search algorithm. The proposed method achieves optimization results comparable in accuracy to those obtained using the grid-search-based optimization method, whereas requires 95% fewer searches. Furthermore, it provides more stable optimization results than those obtained using the conventional Bayesian optimization method.

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