Robots that can move autonomously and can make intelligent decisions by perceiving their environments and surrounding objects are known as autonomous mobile robots. Such robots have rapidly moved from laboratories to automated industries to fill a variety of roles in our lives, homes, offices, hospitals, industries, and even on the streets. The interest in mobile robots is growing rapidly, prompting an enormous amount of research over the last 30 years, on critical factors of mobile robots such as locomotion, perception, localization, mapping, ego-motion tracking, and dynamic navigation. This article surveys these essential factors of autonomous mobile robots in terms of mathematical modeling, control issues, and challenging factors. Brief discussions are provided on the fundamentals of these technologies, popular algorithms in comprehensive mode, future challenges, and promising directions to guide the construction of an autonomous mobile robot with high accuracy and effectiveness. Since it is difficult to find complete coverage of those topics in a single location, this article provides a guideline for researchers entering the field or for innovators in the mobile robotics sector. The paper also examines open challenges in indoor mobile robots and identifies potential futures for autonomous mobile robots.

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Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review

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

Intelligent and safe overtaking maneuvering is always a challenging task for autonomous vehicles. This paper proposes and experimentally implements a novel approach of overtaking maneuvering using modified form of Rendezvous Guidance (RG) algorithm for trajectory planning and obstacle avoidance, considering driver safety and comfort during autonomous overtaking. The simulations for all possible scenarios are conducted to ensure the effectiveness of proposed modified RG algorithm. These scenarios involved presence and absence of obstacle vehicle in overtaking lane alongside leading vehicle in driving lane. In addition, the enhanced performance of modified RG algorithm is established over conventional RG algorithm by comparative analysis. The results indicate that overtaking maneuvering period could be decreased by 10% using a modified RG algorithm and vehicle will cover less distance to complete overtaking. The efficacy of proposed method is justified by performing experiments using mobile robots. The experimental results and simulation results of modified RG algorithm are compared, and their plots are almost identical.

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A novel approach of overtaking maneuvering using modified RG method

Obstacle avoidance methods guarantee the robot’s safety during the tracking of the planned path. Follow the Gap Method known (FGM) is a geometry-based obstacle avoidance method that continuously leads the robot to the goal point by selecting the largest gap existing around the robot. This approach calculates the heading angle by considering the distance to the closest obstacles, the angle to the goal, and the center of the gap. In this paper, a new procedure is developed to improve the gap selection in FGM, where the gaps are selected based on the prediction of gap changes during the time, considering the distance between the robot and obstacles in the future. In order to test the proposed methodology, Monte-Carlo simulations are used and the results are presented for comparison. The results demonstrate that the new procedure leads the robot to safer trajectories in comparison with classical FGM. 

A New Gap Selection Strategy for Follow the Gap Method

This paper proposes a solution for one of the most important components of autonomous driving: "overtaking maneuver". Follow the Gap method (FGM) is oneof the most popular obstacle avoidance algorithms and directly obtains steering angle from position of the obstacles around. This paper is the first study using FGM to solve the overtaking problem. Different from previous studies where a trajectory is planned and then a controller is designed to track it; we use FGM for motion planning and control together. This paper focuses on overtaking maneuver in a challenging environment like highway traffic where the safety and fast response are the key factors. After we adapt the FGM to overtaking problem, we compare it with an existing overtaking method X-sin functions from literature. Since X-sin functions method requires a path tracker (controller), Stanley method is combined with X-sin functions. At the end of this work, we show several advantages of FGM comparing to the X-sin functions based overtaking approach.

Munire Damla Demir

Papers

Autonomous Overtaking Maneuver Design based on Follow the Gap Method.