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The available treatments for cancer (chemo-therapy and radio-therapy) have bad side effects on the healthy tissues. It was proposed to use Nanorobots to deliver drugs directly to the tumor environment without harming the healthy cells. These Nanorobots are injected into human body nearby the target area and begin their journey in the blood vessels. After reaching the tumor area, the Nanorobots start to release the drugs with certain concentration to kill cancer cells (Ezzat et al. 2019). On their way to the cancer area, the Nanorobots may encounter unknown moving obstacles, like for example immune system cells. These cells may disable the movement of the Nanorobots. Therefore, they can’t reach the target area.
To solve this problem, some path-planning methods were proposed to avoid collisions with moving obstacles. But it is not a trivial task, because there is no enough information about this complex environment of the dynamic obstacles. So, we need an online, real time and efficient method for avoiding collisions in such environment. There are many proposed solutions for path-planning in static environments (Jarecki et al. 2017; Korayem et al. 2018; Latombe, 1991; Xin et al. 2005). The path-planning problem in dynamic environments has also been studied (Chang & Yamamoto, 2008; Du et al. 2007; Kundu & Dayal, 2010; Li & Chen, 2005; Miao & Tian, 2008; Naderloo & Naderloo, 2016; Nagata et al. 2010; Reif & Sharir, 1994; Shi & Cui, 2010; Su & Tan, 2005; Willms & Yang, 2008; Xu et al. 2010; Yang et al. 2005; Zhu et al. 2011), but only limited solutions have been proposed to solve this problem. One of these solutions is the probabilistic roadmap (PRM) (Kavraki et al. 1996; Svestka, 1997), but this method is not useful for unknown obstacle motions. To overcome this issue, some researchers try to add some constraints to the path-planning problem (Mobadersany et al. 2014).
The path planning algorithms for dynamic environments are divided into two main categories (Mahajan & Marbate, 2013). In the first category, prior information about the movement of the obstacle is known to the robot. This may facilitate the obstacle avoidance and thus decrease the possibility of collision. In the second category, the movement of obstacles is unknown to the robot, so an efficient method is needed for optimal path planning. The methods used for path planning in dynamic environments may be biologically inspired or graph based (Mahajan & Marbate, 2013). The biologically inspired approaches provide semi-optimal paths. The graph based methods are used for both static and dynamic environments. But these methods have some limitations like time complexity and sensitivity to uncertainty (Mahajan & Marbate, 2013).
It is impossible to achieve the optimal path in ambiguous environments. Instead of the optimal path, all researchers seek to achieve a suboptimal one (Mobadersany et al. 2014). When using analytical methods to avoid dynamic obstacles with unknown behavior in a complex environment, the solution becomes complex and time-consuming. This is not suitable for the online nature of the path planning problems and may lead to collision. The fuzzy logic approach is one of the most powerful methods used in solving such problems efficiently and in a significantly small time (Azam et al. 2017; Lajmi et al. 2020; Chatterjee & MAJI; Rahpeyma & Zarei, 2018). This approach makes decisions like the human brain. It uses some linguistic rules to solve a problem without needing the exact mathematical model of this problem. To solve the problem of avoiding dynamic obstacles in blood vessels, some previous path planning algorithms proposed to use fuzzy logic. These algorithms proved their efficiency in solving this problem.