Follow the Gap with Dynamic Window Approach Aykut Özdemir * and Volkan Sezer † Mechatronics Education and Research Center Istanbul Technical University, Istanbul, Turkey * ozdemirayk@itu.edu.tr † sezerv@itu.edu.tr Follow the Gap Method (FGM) is an obstacle avoidance method which uses gap arrays. This method recursively directs the robot to the goal state while avoiding the obstacles through the safest gap. Since FGM is a geometric method, it does not consider the robot dynamics. For this reason, oscillations or collisions due to robot dynamics are possible. On the other hand, FGM calculates the desired heading angle, but it does not give linear and angular velocity reference. Dynamic Window Approach (DWA) is one of the most popular obstacle avoidance algorithms which does take robot dynamics into consideration. It calcu- lates best angular and linear velocity pair which is chosen by an objective function. In this paper, an FGM-DW approach which uses the strongest elements of FGM and DWA methods to achieve safe, smooth and fast navigation is proposed. The FGM-DW approach provides these concerns and meets the low level angular and rotational velocity requirement of FGM. In this paper, the performance and analysis of FGM-DW are shown by both simulations and real-world experimental tests. Keywords: Follow the gap method; dynamic window approach; obstacle avoidance; robot operating system. 1. Introduction Motion planning techniques are designed to ¯nd geometrically admissible trajectory pairs connecting the robot initial position and the goal location without collisions. These methods can be divided into two major parts: global and reactive motion planning. Global motion planning methods use prior information of obstacles and generate trajectories between the initial position and the goal position inside collision free space. These types of methods assume that the obstacles are static and the map is not updated using the sensory information. Probabilistic roadmaps (PRMs) [1], rapidly-exploring random trees (RRTs) [2], potential ¯eld methods [3] and cell de- composition-based methods [4] belong to this category. These methods are prob- lematic if the information of obstacles are inaccurate or not available. Moreover, their execution time increases exponentially as a consequence of model and world complexities. International Journal of Semantic Computing Vol. 12, No. 1 (2018) 43–57 ° c World Scienti¯c Publishing Company DOI: 10.1142/S1793351X18400032 43 Int. J. Semantic Computing 2018.12:43-57. Downloaded from www.worldscientific.com by 104.144.222.164 on 07/15/19. Re-use and distribution is strictly not permitted, except for Open Access articles.