An Improved A-Star Path Planning Algorithm Based on Mobile Robots in Medical Testing Laboratories

Cell manipulation using optically induced dielectrophoresis (ODEP) in a microfluidic system has drawn much attention due to its simplicity and being damage-free at the cellular level. Additionally, to improve its manipulation efficiency and accuracy, automatic manipulation methods have been applied in the ODEP system. However, the current automatic manipulation methods of ODEP rarely consider the impact of non-manipulated targets on cell manipulation, thereby reducing the operating efficiency and accuracy. Here, we propose a new, to our knowledge, automatic manipulation method of ODEP based on a path planning algorithm of the improved A-star. First, the maximum influence range of ODEP force generated by the virtual electrode was investigated by a numerical simulation, and it was also taken as the limit to expand the scope of the infeasible region in path planning to avoid the impact of the non-operational target on manipulation accuracy. Then, an improved A-star algorithm with target range constraints was proposed to optimize the manipulation path and improve the operation efficiency. Finally, experiments on cell separation were also carried out to validate the feasibility of the proposed automatic manipulation method. This work provides an automated method to improve the accuracy of ODEP manipulation, which is of great significance to further promote the application of ODEP in cell manipulation.

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Deep Learning-Based Nonparametric Identification and Path Planning for Autonomous Underwater Vehicles

Mei,

Li,

Liu

et al. 2024

JMSE

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As the nonlinear and coupling characteristics of autonomous underwater vehicles (AUVs) are the challenges for motion modeling, the nonparametric identification method is proposed based on dung beetle optimization (DBO) and deep temporal convolutional networks (DTCNs). First, the improved wavelet threshold is utilized to select the optimal threshold and wavelet basis functions, and the raw model test data are denoising. Second, the bidirectional temporal convolutional networks, the bidirectional gated recurrent unit, and the attention mechanism are used to achieve the nonlinear nonparametric model of the AUV motion. And the hyperparameters are optimized by the DBO. Finally, the lazy-search-based path planning and the line-of-sight-based path following control are used for the proposed AUV model. The simulation shows that the prediction accuracy of the DBO-DTCN is better than other artificial intelligence methods and mechanical models, and the path following of AUV is feasible. The methods proposed in this paper can provide an effective strategy for AUV modeling, searching, and rescue cruising.

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An Improved A-Star Path Planning Algorithm Based on Mobile Robots in Medical Testing Laboratories

Cited by 3 publications

References 20 publications

Research on Mobile Device Path Planning Based on Improved A* Algorithm

Research on Mobile Device Path Planning Based on Improved A* Algorithm

Precision manipulation by an optically induced dielectrophoresis system based on an improved A-star algorithm

Deep Learning-Based Nonparametric Identification and Path Planning for Autonomous Underwater Vehicles

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