Design and performance analysis of global path planning techniques for autonomous mobile robots in grid environments

Châari, Imen; Koubâa, Anis; Bennaceur, Hachémi; Ammar, Adel; Alajlan, Maram; Youssef, Habib

doi:10.1177/1729881416663663

Cited by 51 publications

(22 citation statements)

References 23 publications

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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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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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“…A major issue common in these methods is the trapping of the path at local optima. These methods, therefore, provide non-optimal solutions [19].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Hybrid Control to Enhance Applicability of Mobile Robots in Cluttered Environments

et al. 2021

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Trajectory planning and tracking are the most important aspects of mobile robot research for industrial application. In this research, an intelligent hybrid control is presented to enhance the usability of mobile robots in environments cluttered with static obstacles. The control is hybrid in two ways. On one hand, the algorithm combines obstacles avoidance and trajectory generation. The generated trajectory acts as a reference path to be followed by the mobile robot. On the other hand, the algorithm not only generates trajectory but also tracks the generated trajectory. An optimizationbased intelligent algorithm, simulated annealing, is designed to plan and generate a trajectory for collision-free robot motion in an environment with stationary obstacles. The PID (Proportional-Integral-Derivative) control algorithm is designed to track the generated trajectory with minimum error. Both algorithms are hybridized such that the generated trajectory becomes a reference trajectory for the tracking control algorithm. The simulated annealing generates a realistic trajectory that consists of a series of the best points with obstacles on the path. The best point is selected with the shortest distance from the robot's destination. These points are selected one from each of the grids, generated between the start and destination point of the robot motion. The dimensions of the robot are considered and included in the obstacles dimension to reduce the dimension complexity of the robot. The effectiveness of the proposed hybrid technique is tested in simulation and in real-time experiments for seven types of cluttered environments. The environments vary with size, shape, placement and arrangement of the obstacles. The results show that the simulated annealing generates a collision-free trajectory intelligently without trapping in local minima in these cluttered environments while the PID control tracks the reference trajectory with a maximum absolute error of 0.2 mm. Hence, the proposed method enhances the usability of mobile robots in environments cluttered with static objects.

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“…The key factors affecting underwater gravity matching navigation are not only the gravity matching algorithms [9]- [11], the high-precision and high-resolution global marine gravity anomaly reference map [12], [13], the high-precision gravity measurement systems [14], and the optimized gravity suitability regions [15]- [17], but also the fact that mismatching has a negative impact on the underwater gravity matching. In the actual situation, the mismatching is inevitable in the gravity matching process based on the gross error theory.…”

Section: Introductionmentioning

confidence: 99%

Improving the Reliability of Underwater Gravity Matching Navigation Based on a Priori Recursive Iterative Least Squares Mismatching Correction Method

Zheng

2020

IEEE Access

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This study focuses on the reliability of underwater gravity matching navigation. Firstly, the research started with the post-processing of mismatching. Under the guidance of priori recursive multiple matching and iterative least squares, a new priori recursive iterative least squares mismatching correction (PRILSMC) method was proposed based on statistics and the fitting principle. Secondly, according to factors such as matching algorithm probability, error of INS, and the actual situation, we comprehensively considered the effects of recursive sampling points, priori matching points, etc. The new mismatching judgment and dynamic correction (MJDC) model was constructed based on the PRILSMC method. Finally, under the same conditions, the MJDC model was used to verify a new matching point in three matching regions. The results showed that in the excellent-suitability region, the matching probability increased from about 96% to 100%, i.e., all mismatching points could essentially be eliminated. In the general-suitability region, the matching probability increased from about 64% to 92%, indicating that the probability of mismatching point was greatly reduced, and the reliability of the matching navigation was improved.

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Design and performance analysis of global path planning techniques for autonomous mobile robots in grid environments

Cited by 51 publications

References 23 publications

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

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

An Intelligent Hybrid Control to Enhance Applicability of Mobile Robots in Cluttered Environments

Improving the Reliability of Underwater Gravity Matching Navigation Based on a Priori Recursive Iterative Least Squares Mismatching Correction Method

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