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.

在复杂的生物环境中进行高效准确的路径规划成为纳米机器人研究的挑战。本文首先回顾了当前科学界可用于纳米机器人运行的路径规划算法。算法主要分为四个部分，包括Dijkstra算法、A*算法、Rapidly-exploring Random Tree (RRT)算法和Swarm Intelligence (SI)算法。在SI的应用中，遗传算法（GA）和粒子群优化（PSO）是解决纳米机器人群路径规划最常用的方法。然后，在每个部分中概述了它们的研究现状、优势和局限性。讨论了不同算法在不同环境下的改进，同时充分证明了它们优于其他方法。最后，未来对最优路径规划的研究有望成为纳米机器人高精度控制的下一步。本综述旨在为提高纳米机器人性能提供一些思路，加速路径规划技术在纳米操纵领域的又一次飞跃。