Mobile robot path planning in complex environments using ant colony optimization algorithm

Uriol, Ronald; Moran, Antonio

doi:10.1109/iccar.2017.7942653

Cited by 21 publications

(13 citation statements)

References 6 publications

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“…The issue of planning the route for mobile robots in complex environments was analyzed and tuned for disparate working areas with obstacles in different numbers, sizes, and shapes [12]. Some of the suggested methodologies focus on searching for the smallest route from the start to the target.…”

Section:  Complex Environmentsmentioning

confidence: 99%

A Review on Path Planning Algorithms for Mobile Robots

Abed¹,

Lutfy²,

Al-Doori³

2021

ETJ

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Mobile robots use is rising every day. Path planning algorithms are needed to make a traveler of robots with the least cost and without collisions. Many techniques have been developed in path planning for mobile robot worldwide, however, the most commonly used techniques are presented here for further study. This essay aims to review various path planning strategies for mobile robots using different optimization methods taken recent publisher’s paper in last five year.

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Section:  Complex Environmentsmentioning

confidence: 99%

A Review on Path Planning Algorithms for Mobile Robots

Abed¹,

Lutfy²,

Al-Doori³

2021

ETJ

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“…The size of the environment and its complexity surely affect the performance of path planning algorithms as increasing them leads to increasing the time cost [64,65]. The complexity can be expressed as the number of obstacles between the start and target points.…”

Section: A the Size And Complexity Of The Environmentmentioning

confidence: 99%

A Supervisory-Based Collaborative Obstacle-Guided Path Refinement Algorithm for Path Planning in Wide Terrains

2020

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Robotic exploration of wide terrains, such as agricultural fields, could be challenging while considering the limited robot's capabilities in terms of sensing and power. Thus, in this paper, we proposed OGPR*, an Obstacle Guided Path Refinement algorithm for quickly planning collision-free paths utilizing the obstacles existing in the environment. To tackle the issue of exploring wide terrains, a supervisorybased collaboration between quadcopter and mobile robots is proposed. The quadcopter is responsible for streaming subsequently live two-dimensional images for the environment under discussion while planning safe paths for the ground the mobile robot is planning safe paths to manoeuvre. Numerical simulations proved the significant performance of the proposed OGBR* algorithm when compared to the state of the art algorithms exist in the literature. INDEX TERMS collaborative, mobile robot, OGPR, path planning, tracking I. INTRODUCTION

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“…In order to show the performance of the combined algorithm in the complex environment, the path planning on the grid map (80 × 80) with unknown regions is simulated, which presents the simulation map of Qixing Lake, part of Dongting Lake. For comparison, the parameters of the classical ACO algorithm [35] are set as follows: the information elicitation factor α = 1, the expected heuristic factor β = 7, the evaporation factor ρ = 0.3, the pheromone intensity Q = 1. To ensure the ACO algorithm can get better planning results, the number of ants m = 200, the iterations k = 100, and the detection range of each ant is set as 5 × 5 grid map size.…”

Section: Simulation In Complex Environmentmentioning

confidence: 99%

A Hybrid Path Planning Method for an Unmanned Cruise Ship in Water Quality Sampling

Deng

Zhao

et al. 2019

IEEE Access

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Cruise ships are widely used in water quality monitoring but suffer from path planning problems in a surface water environment. Solutions to path planning problems have higher requirements of path planning distance and path planning time and the ability of real-time obstacles avoidance. This paper introduces a hybrid path planning method to solve the path planning problem when using unmanned cruise ships. First, a model of the surface water environment with unknown districts is established by the grid method. Then, the global path is planned by the A * algorithm based on such a model, and in unknown areas of the model, the artificial potential filed (APF) is employed for local path planning. The problem of obtaining unreachable targets and falling into a local minimum introduced by the APF is improved by using the optimized repulsion potential field function and adding the directional random escape strategy. The A * algorithm with self-learning ability is proposed for secondary planning and cases where a local path cannot be generated. Finally, the optimal path combined with the global path and the local path is smoothed. The simulation results show that the proposed algorithm has better performance than other algorithms from the aspects of distance cost and time cost. INDEX TERMS Artificial potential field method, A * algorithm, hybrid path planning, obstacle avoidance, water quality sampling cruise ship.

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Mobile robot path planning in complex environments using ant colony optimization algorithm

Cited by 21 publications

References 6 publications

A Review on Path Planning Algorithms for Mobile Robots

A Review on Path Planning Algorithms for Mobile Robots

A Supervisory-Based Collaborative Obstacle-Guided Path Refinement Algorithm for Path Planning in Wide Terrains

A Hybrid Path Planning Method for an Unmanned Cruise Ship in Water Quality Sampling

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