Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Abstract

Mobile robot is an autonomous agent capable of navigating intelligently anywhere using sensor-actuator control techniques. The applications of the autonomous mobile robot in many fields such as industry, space, defence and transportation, and other social sectors are growing day by day. The mobile robot performs many tasks such as rescue operation, patrolling, disaster relief, planetary exploration, and material handling, etc. Therefore, an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments. Several techniques have been applied by the various researchers for mobile robot navigation and obstacle avoidance. The present article focuses on the study of the intelligent navigation techniques, which are capable of navigating a mobile robot autonomously in static as well as dynamic environments. Keywords: IntroductionThis article introduces the literature survey of the various techniques used for mobile robot navigation. Navigation and obstacle avoidance are one of the fundamental problems in mobile robotics, which are being solved by the various researchers in the past two decades. The aim of navigation is to search an optimal or suboptimal path from the start point to the goal point with obstacle avoidance competence. Basically, the mobile robot navigation has been done by the Deterministic algorithm and Nondeterministic (Stochastic) algorithm. Nowadays, the hybridization of both the algorithms called as an Evolutionary algorithm is being used to solve the mobile robot navigation problem. Figure 1 shows the general classification of the Deterministic algorithm, Nondeterministic (Stochastic) algorithm, and Evolutionary algorithm, which are implemented for mobile robot navigation by various authors.

Learning-based Intelligent Attack against Formation Control with Obstacle-avoidance

“…Algorithm 3 is composed of three parts: i) First, before R v comes near to p e , R a needs to wait for the best attack time [Line 17 -21]; ii) Then, after R a begins its initial attack, every iteration of following attacks are based on sampling to explore the motion space of both R a and R v and select a best attack input from a feasible attack set [Line [6][7][8][9][10][11][12][13][14][15][16]; iii) In the end, when R v is close enough from p trap , R a stops and we call the attack is successful. The bottleneck of Algorithm 3 is that the sampling set can be large such that the total computation is time-consuming [Line 8 -14].…”

Research on Local Dynamic Path Planning Method for Intelligent Vehicle Lane-Changing

“…In [3], the researchers used the fusion of deterministic algorithm and nondeterministic algorithm with neural network to solve the obstacle avoidance problem of mobile robot and carried out computer simulation. However, little consideration was given to the dynamic environment and the experimental results of the actual physical model were not given.…”

Improving a Wall-Following Robot Performance with a PID-Genetic Algorithm Controller

“…Consequently, the role of the controller in maintaining the movement has been becoming the core of the problem to be accomplished. There are many control methods have been attempted to address the common issue of the wall-following robot [5], such as Fuzzy Logic [6] [7], Genetic Algorithm, Neural Network or hybrid of them [8]. But, most of these approaches have to utilize the orientation sensor featured in the robot (compass, GPS, etc.)…”