A Review on Path Planning and Obstacle Avoidance Algorithms for Autonomous Mobile Robots

Abstract

Mobile robots have been widely used in various sectors in the last decade. A mobile robot could autonomously navigate in any environment, both static and dynamic. As a result, researchers in the robotics field have offered a variety of techniques. This paper reviews the mobile robot navigation approaches and obstacle avoidance used so far in various environmental conditions to recognize the improvement of path planning strategists. Taking into consideration commonly used classical approaches such as Dijkstra algorithm (DA), artificial potential field (APF), probabilistic road map (PRM), cell decomposition (CD), and meta-heuristic techniques such as fuzzy logic (FL), neutral network (NN), particle swarm optimization (PSO), genetic algorithm (GA), cuckoo search algorithm (CSO), and artificial bee colony (ABC). Classical approaches have limitations of trapping in local minima, failure to handle uncertainty, and many more. On the other hand, it is observed that heuristic approaches can solve most real-world problems and perform well after some modification and hybridization with classical techniques. As a result, many methods have been established worldwide for the path planning strategy for mobile robots. The most often utilized approaches, on the other hand, are offered below for further study.

Obstacle avoidance in aerial pursuit

“…For predators adapted to hunting in clutter, the demands of these two tasks may conflict, requiring effective reconciliation to avoid a hazardous collision or loss of target. Technical approaches to obstacle avoidance rely mainly on path-planning algorithms [12], but these are unlikely to be effective during closed-loop pursuit of a maneuvering target, so collision avoidance must instead be implemented reactively during prey pursuit. For example, the pursuit-avoidance behavior of predatory flies has been successfully modelled by combining feedback on target motion with feedback on obstacle looming [13].…”

A Survey on Navigation Approaches for Automated Guided Vehicle Robots in Dynamic Surrounding

“…This section will go over the two main approaches for navigating an AGV. It consists of local and global are the types of AGV navigation [13]. Consists of two types of classical navigation techniques which are local also known as conventional navigation and global also known as heuristic navigation for path finding purpose.…”

“…The shortest path between any two graph vertices can be discovered by AGV using Dijkstra's technique [13].…”

“…They also have a broad range of applications, including transportation, material handling, searching and rescuing [1]. One of the most visible and important aspects of AGV navigation is path finding [13]. Path finding is defined as the discovery of collision-free handling paths that meet the requirements of AGV operation according to predefined rules.…”

Numerical Analysis of a TCR Adaptive to Different Tree Diameters for Unstructured Forest Environment: A TCR of Variable Diameter with Different Tree Diameters

“…Climbing robot is a challenging research topic, which has attracted the extensive attention of many scientifc and technological researchers [1][2][3]. Most of such robots reported in the literature are designed for climbing on artifcial structures, with only a few designed for climbing unstructured forest natural environments, such as trees [4][5][6].…”