A Mobile Robot Localization Method Based on Polar Scan Matching and Adaptive Niching Chaos Optimization Algorithm

Mobile robot localization, which enables a robot to recognize its position and orientation in the environment, is one of the most principal issues in the field of autonomous navigation of mobile robots. Among the various methods to solve robot localization, polar scan matching (PSM) technique has the advantage of much less computational load but has the potential to mismatch scans. Chaos optimization algorithms (COAs), a family of stochastic global optimization algorithms based on chaos theory, has many good features such as easy implementation, short execution time and robust mechanism for escaping from local optima. This paper presents a mobile robot localization method based on polar scan matching and adaptive niching chaos optimization algorithm (ANCOA). First, we define a new error function that fits the characteristics of the polar scan matching problem, and then propose an adaptive version of niching chaos optimization algorithm to increase the search speed and improve solution accuracy. The task of robot localization is performed by optimizing the new error function using the adaptive version of NCOA. The proposed approach is tested and evaluated comprehensively through computer simulations which shows that the proposed approach can significantly improve the performance of polar scan matching.