Abstract
Efficient and accurate path planning in a complex biological environment have become a challenge for nanorobot research. This paper first reviews the current path planning algorithms that can be used in the operation of nanorobots in the scientific community. The algorithms are mainly divided into four parts, including Dijkstra algorithm, A* algorithm, Rapidly-exploring Random Tree (RRT) algorithm, and Swarm Intelligence (SI) algorithm. In the application of SI, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are the most commonly used approaches to solve the path planning of nanorobot swarm. Then, their research status, advantages, and limitations are outlined in each section. The improvement of different algorithms in different environments is discussed while fully demonstrating that they are superior to other methods. Finally, the future research on optimal path planning is expected as the next step in high-precision control of nanorobot. This review aims to provide some ideas for the improvement of nanorobot performance and accelerate another leap of path planning technology in the field of nanomanipulation.
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Acknowledgements
This work was supported by the National Key R&D Program of China (Grant No. 2021YFB3201600), and the National Natural Science Foundation of Liaoning (Grant Nos. 2020-MS-219).
Funding
This work was supported by the National Key R&D Program of China (Grant No. 2021YFB3201600), and the National Natural Science Foundation of Liaoning (Grant No. 2020-MS-219).
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Ke Xu contributed significantly to the conception of the study and helped perform the analysis with constructive discussions. Rong Su performed the literature analysis and wrote the manuscript. All authors read and approved the final manuscript.
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Xu, K., Su, R. Path planning of nanorobot: a review. Microsyst Technol 28, 2393–2401 (2022). https://doi.org/10.1007/s00542-022-05373-x
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DOI: https://doi.org/10.1007/s00542-022-05373-x