An Improved Algorithm for Collision Avoidance in Environments Having U and H Shaped Obstacles

Abstract

This paper proposes a novel obstacle avoidance algorithm for autonomous mobile robot control. The proposed approach brings a solution to the problem of robot traversal in critical shaped environments and offers several advantages compared to the reported approaches. The algorithmic approach, named as, Intelligent Follow the Gap Method (IFGM) is based on improved Intelligent Bug Algorithm (IBA) and Follow the Gap Method (FGM). The robot field of view is taken into consideration. The IBA avoids obstacles by following their edge and scanning the path to destination, thus making the approach goal-oriented avoiding local minimum problem. To characterize the performance of IFGM, various scenarios of obstacles are considered. These scenarios range from having obstacles defined by simple and symmetrical shapes to critical shaped obstacles. The simulation results demonstrate that the algorithm results in safer and smoother trajectories in the presence of obstacles. It offers fast convergence and does not suffer from local minima. Finally, the performance comparison of the proposed algorithm with that of the reported approaches in terms of distance-time plots confirms the efficacy of the presented approach. The proposed algorithm lends itself to future implementations in the navigation of mobile and industrial robots, especially in applications exhibiting crucial time and critical obstacles including disaster management, spy, elderly people assistance and soccer games.

Unmanned Combat Aerial Vehicle Path Planning by Brain Storm Optimization Algorithm

“…Obstacles avoidance algorithm for mobile robot control that combines intelligent bug algorithm and follow the gap method, named intelligent follows the gap method (IFGM), was proposed in [33]. The IFGM has the ability to dynamically adjust path and avoid U and H shape obstacles.…”

Prospects of robotics in food industry

“…Advancements in various technological domains during the last two decades have transformed 'fiction' robots in reality (Zohaib et al, 2014). Robotics lies in the category of industrial automation .…”

Energy Restoration in Mobile Sensor Networks in the Presence of Physical Obstacles

“…Robots in the bug family algorithms have two states while moving: "move to goal ", and "obstacle avoidance" [46]. When a robot is in " move to goal " state, it can move freely.…”

“…In this case, the leaving point is selected when the distance between the next position around the obstacle and the goal position is greater than the distance between the current position and the goal. When the robot finds the leaving point it switches to "move to goal " state [44,46]. This algorithm is a greedy algorithm because it always selects the path that minimize the current distance.…”

“…All the obstacle avoidance strategies described before have been analyzed and compared in several papers like [18,[44][45][46]. From the results obtained, it can be concluded that FGM, and IBA achieve the best results.…”