A particle swarm optimization method for fault localization and residue removal in digital microfluidic biochips

Mukherjee, Subhamita; Pan, Indrajit; Samanta, Tuhina

doi:10.1016/j.asoc.2019.105839

Cited by 7 publications

(4 citation statements)

References 42 publications

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“…Each droplet is interpreted as a point robot moving in a discrete two-dimensional configuration space. Under this assumption, path planning of the droplets becomes a motion planning problem with multiple moving robots [31][32][33][34]. erefore, in the future research, we will further try to use ant colony algorithm to solve the droplet path planning and scheduling problem of DMFB.…”

Section: Discussionmentioning

confidence: 99%

Smooth Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

Wang

Zhao

et al. 2021

Journal of Robotics

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Aiming at the problems of slow convergence, easy to fall into local optimum, and poor smoothness of traditional ant colony algorithm in mobile robot path planning, an improved ant colony algorithm based on path smoothing factor was proposed. Firstly, the environment map was constructed based on the grid method, and each grid was marked to make the ant colony move from the initial grid to the target grid for path search. Then, the heuristic information is improved by referring to the direction information of the starting point and the end point and combining with the turning angle. By improving the heuristic information, the direction of the search is increased and the turning angle of the robot is reduced. Finally, the pheromone updating rules were improved, the smoothness of the two-dimensional path was considered, the turning times of the robot were reduced, and a new path evaluation function was introduced to enhance the pheromone differentiation of the effective path. At the same time, the Max-Min Ant System (MMAS) algorithm was used to limit the pheromone concentration to avoid being trapped in the local optimum path. The simulation results show that the improved ant colony algorithm can search the optimal path length and plan a smoother and safer path with fast convergence speed, which effectively solves the global path planning problem of mobile robot.

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Section: Discussionmentioning

confidence: 99%

Smooth Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

Wang

Zhao

et al. 2021

Journal of Robotics

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“…83 Conversely, RL can enhance the learning and adaptation abilities of SI systems, making them more intelligent and capable of solving a wider range of problems. 84 On-chip fault localization and residue removal, 85 single-particle micropatterning, 86 and droplet sorting 87 were refined by ACO and PSO algorithms.…”

Section: Physics-based Deep Learningmentioning

confidence: 99%

AI-enhanced biomedical micro/nanorobots in microfluidics

Dong,

Lin,

Tao

et al. 2024

Lab Chip

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“…Different attempts at quantifying and predicting performance to reduce the probability of dielectric breakdown have been made in previous studies. [139][140]…”

Section: Technical Challengesmentioning

confidence: 99%