A new gap-based obstacle avoidance approach: follow the obstacle circle method

Abstract

One of the most challenging tasks for autonomous robots is avoiding unexpected obstacles during their path following operation. Follow the gap method (FGM) is one of the most popular obstacle avoidance algorithms that recursively guides the robot to the goal state by considering the angle to the goal point and the distance to the closest obstacles. It selects the largest gap around the robot, where the gap angle is calculated by the vector to the midpoint of the largest gap. In this paper, a novel obstacle avoidance procedure is developed and applied to a real fully autonomous wheelchair. This proposed algorithm improves the FGM’s travel safety and brings a new solution to the obstacle avoidance task. In the proposed algorithm, the largest gap is selected based on gap width. Moreover, the avoidance angle (similar to the gap center angle of FGM) is calculated considering the locus of the equidistant points from obstacles that create obstacle circles. Monte Carlo simulations are used to test the proposed algorithm, and according to the results, the new procedure guides the robot to safer trajectories compared with classical FGM. The real experimental test results are in parallel to the simulations and show the real-time performance of the proposed approach.

Obstacle Avoidance and Path Planning Methods for Autonomous Navigation of Mobile Robot

“…The algorithm uses sensory information to identify gaps with the largest angle and works in three steps, as follows [38]:…”

“…The algorithm uses sensory information to identify gaps with the largest angle and works in three steps, as follows [ 38 ]: The initial step involves computing the arrays of gaps. During this phase, the algorithm utilizes the current sensory data, such as information from the LIDAR sensor, to produce a gap array.…”

“… Robot-obstacle configuration, obstacles ( A , B , and C ), the midpoint of the widest angular gap ( ), goal point ( X ), angle to the goal point ( ), final heading angle ( ), and angle to the largest gap’s center point ( ). [ 38 ]. …”

Autonomous navigation and obstacle avoidance in smart robotic wheelchairs

“…These methods generate attractive and repulsive forces based on the proximity of obstacles and desired target location to guide the wheelchair's trajectory away from obstacles and towards the target. Houshyari and Sezer (2022) proposed an obstacle avoidance method based on potential field methods for a mobile robot, which can be adapted for wheelchair navigation. This method allows the wheelchair to reactively adjust its path in real-time based on the sensed obstacles, enabling safe navigation.…”