Mobile robots path planning: Electrostatic potential field approach. (15th June 2018)

• Record Type: Journal Article
• Authors: Bayat, Farhad; Najafinia, Sepideh; Aliyari, Morteza
• Is Part Of: Expert systems with applications. Volume 100(2018)
• Page Start: 68
• Page End: 78
• Publication Date: 2018-06-15
• Subjects: Path planning -- Mobile robot -- Potential field -- Obstacle avoidance; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.01.050
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Electrostatic potential field theory is used to solve robot's path planning problem. All obstacles' features are integrated into a scalar potential field to make decision. Collision-free and fast approaching objectives are achieved via an optimization. Using a scalar potential field makes it extremely simple and practically feasible. The proposed method can be simply applied to both static and dynamic environments. Abstract: This paper deals with the mobile robots path planning problem in the presence of scattered obstacles in a visually known environment. Utilizing the theory of charged particles' potential fields and inspired by a key idea of the authors' recent work, an optimization based approach is proposed to obtain an optimal and robust path planning solution. By assigning a potential function for each individual obstacle, the interaction of all scattered obstacles are integrated in a scalar potential surface (SPS) which strongly depends on the physical features of the mobile robot and obstacles. The optimum path is then obtained from a cost function optimization by attaining a trade-off between traversing the shortest path and avoiding collisions, simultaneously. Hence, irrespective of any physical constraints on the obstacles/mobile-robot and the adjacency of the target to the obstacles, the achieved results demonstrate a feasible, fast, oscillation-free and collision-free path planning of the proposed method. Utilizing a scalar decision variable makes it extremely simple in terms of mathematical computations and thus practically feasible that can be applied to both static and dynamic environments. Finally, simulation results verified the performance and fulfillment of the mentioned objectives of the approach.