Dynamic local path planning method based on neutrosophic set theory for a mobile robot

Path planning is among the basic tasks required in the navigation of a mobile robot. The aim of path planning is for a mobile robot to reach the target position from the starting position by taking the optimum path by avoiding obstacle. Moreover, the calculation cost of the path planning method used by the autonomous robot is among the important parameters that affect the path planning time. In this study, an adaptive, optimal and local path planning method based on neutrosophic set theory is presented for autonomous mobile robots. This method uses a low-cost sensor model of the autonomous mobile robot and has low-cost computational. The proposed neutrosophic-based method uses the sensor inputs truth and falsity as well as the indeterminacy membership functions. These results from the fitness functions determine the angular velocity of the mobile robot to reach the target optimally. Simulations have performed in an environment with static and dynamic obstacles to investigate the performance of the proposed path planning method. In addition, the proposed method is compared with the recently proposed path planning methods. The proposed path planning method has adaptive and optimal path discovery, according to current conditions, low-cost computation and activities that do not require a configuration at the initial stage.