BUGs algorithms are the most well-known navigation algorithms, which are used to deal the problems of searching for an unpredictable moving target, using a robot that lacks a map of the environment, lacks the ability to construct a map, and has imperfect navigation ability. BUGs algorithms are designed to seek of a target in a plane that contains obstacles. Many new navigation algorithms have been inspired from them and their applications can be found in mobile robots, e.g., self driving vehicles. These algorithms are inspired from insects and are comparable to the motion of ants, which yields motion strategies for the robot that guarantees the elusive target will be detected, if such strategies exist. However, these algorithms have not been formally verified using existing formal verification tools. Therefore, the aim of this paper is to apply model checking for verifying the correctness of BUGs algorithms and draw conclusions for future uses of formal methods in the design and model checking of navigation algorithms. This study can help organizations to reduce the errors of their systems, increase the safety of their systems, make their systems more efficient, and reduce the cost of the organizations.