کنفرانس مهندسی برق ایران

صفحه اصلی / سی امین کنفرانس بین المللی مهندسی برق

Synergy of Deep Learning and Artificial Potential Field Methods for Robot Path Planning in the Presence of Static and Dynamic Obstacles

نویسندگان :

Mohammad Amin Basiri¹ Shirin Chehelgami² Erfan Ashtari³ Mehdi Tale Masouleh⁴ Ahmad Kalhor⁵

1- دانشگاه تهران 2- دانشگاه تهران 3- دانشگاه تهران 4- دانشگاه تهران 5- دانشگاه تهران

کلمات کلیدی :

Robot Path Planning،Deep learning،Artificial Potential field،Global and local Path Planning،Static and Dynamics Obstacles avoidance

چکیده :

In a fast-changing world we are in today, unmanned vehicles are displacing old, obsolete and frustrating tasks. Since unmanned vehicles are intended to work in an environment without any conduction, finding a collision-free path of movement is of paramount importance and a definite asset in practice. In this paper, a complete global and local path planning method is proposed for avoiding both static and dynamic obstacles. First, a method for generating numerous obstacle-free paths from random pairs of start and goal points is introduced. Next, a novel deep-learning approach is proposed in order to train the robot in an environment free of moving obstacles which contains only fixed obstacles. After extracting the desired via points, an efficient Artificial Potential Field(APF) approach for attaining local path planning is introduced with the aim of avoiding dynamic obstacles while the robot travels through the aforementioned via points. The proposed method can be well extended to different platforms such as mobile robots, arm robots, quadrotors, etc; in this paper, both local and global path planning methods are implemented on a simulated quadrotor to examine the robot's performance for both approaches. Furthermore, it has been revealed that implementing both approaches should be implemented seamlessly in order to attain a complete efficient path planning with the presence of both static and dynamics obstacles.