Obstacle avoidance of mobile robots using modified artificial potential field algorithm

Abstract

In recent years, topics related to robotics have become one of the researching fields. In the meantime, intelligent mobile robots have great acceptance, but the control and navigation of these devices are very difficult, and the lack of dealing with fixed obstacles and avoiding them, due to safe and secure routing, is the basic requirement of these systems. In this paper, the modified artificial potential field (APF) method is proposed for that robot avoids collision with fixed obstacles and reaches the target in an optimal path; using this algorithm, the robot can run to the target in optimal environments without any problems by avoiding obstacles, and also using this algorithm, unlike the APF algorithm, the robot does not get stuck in the local minimum. We are looking for an appropriate cost function, with restrictions that we have, and the goal is to avoid obstacles, achieve the target, and do not stop the robot in local minimum. The previous method, APF algorithm, has advantages, such as the use of a simple math model, which is easy to understand and implement. However, this algorithm has many drawbacks; the major drawback of this problem is at the local minimum and the inaccessibility of the target when the obstacles are in the vicinity of the target. Therefore, in order to obtain a better result and to improve the shortcomings of the APF algorithm, this algorithm needs to be improved. Here, the obstacle avoidance planning algorithm is proposed based on the improvement of the artificial potential field algorithm to solve this local minimum problem. In the end, simulation results are evaluated using MATLAB software. The simulation results show that the proposed method is superior to the existing solution.

Adaptive Leader-Follower Formation Control of Under-actuated Surface Vessels with Model Uncertainties and Input Constraints

“…The authors proposed a supervised learning method to do the classification that is very interesting and can be applied to our method [43,44]. The authors in [45][46][47][48][49][50][51][52][53] gave a novel mobile sink-based method to optimize system performance, which can be referred to.…”

“…only the surge and heave velocities, i.e., u i and r i are important to be controlled and velocity in the direction of v i remains bounded due to the existence of a friction coefficient [51]. Define a new velocity vector as below that only consists the surge and heave velocities ( Figure 3).…”

Effect of Load Priority Modeling on the Size of Fuel Cell as an Emergency Power Unit in a More-Electric Aircraft

“…Also the authors gave a novel method to optimize the system performance using clustering [30][31][32], heuristic [33], genetic [34][35][36], evolution [37], and optimization [38] approaches. Other robust methods have also been used for uncertainty removal [39][40][41][42][43][44].…”

Learning, Generalization, and Obstacle Avoidance with Dynamic Movement Primitives and Dynamic Potential Fields

“…They can be divided into two categories, the local methods and the global methods. The local methods can offer fast response in face of the obstacles with local optimization, mainly including the vector field histogram [26], motion field flow [27], the curvature-velocity method [28], and the artificial potential field approach [29,30]; the global methods can ensure that a valid and whole trajectory optimized solution can be found if it exists, but requires large computation and global representation of the environments and obstacles, mainly containing the path planning algorithms [31,32] and the global search approaches [33,34]. The artificial potential field approach, which was proposed by Khatib [29] in 1986, has been widely studied to avoid obstacles.…”