Research of the Path Planning Complexity for Autonomous Mobile Robot under Dynamic Environments

2006 ◽  
Author(s):  
Hongyan Shi ◽  
Xiaoming Sun ◽  
Changzhi Sun ◽  
Dongyang Chen ◽  
Yuejun An
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Dynamic Environments ◽  
Autonomous Mobile Robot

Autonomous mobile robot path planning in unknown dynamic environments using neural dynamics

2020 ◽  
Vol 24 (18) ◽  
pp. 13979-13995 ◽  
Author(s):  
Yanjie Chen ◽  
Jiacheng Liang ◽  
Yaonan Wang ◽  
Qi Pan ◽  
Jianhao Tan ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Dynamic Environments ◽  
Neural Dynamics ◽  
Autonomous Mobile Robot ◽  
Robot Path Planning ◽  
Robot Path

scholarly journals Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

2020 ◽  
Vol 89 ◽  
pp. 106076 ◽  
Author(s):  
Fatin H. Ajeil ◽  
Ibraheem Kasim Ibraheem ◽  
Mouayad A. Sahib ◽  
Amjad J. Humaidi
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimization Algorithm ◽  
Autonomous Mobile Robot ◽  
Multi Objective

Human-centered X–Y–T space path planning for mobile robot in dynamic environments

2015 ◽  
Vol 66 ◽  
pp. 18-26 ◽  
Author(s):  
Ippei Nishitani ◽  
Tetsuya Matsumura ◽  
Mayumi Ozawa ◽  
Ayanori Yorozu ◽  
Masaki Takahashi
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Dynamic Environments

scholarly journals Minimum control effort–based path planning and nonlinear guidance for autonomous mobile robots

2018 ◽  
Vol 15 (6) ◽  
pp. 172988141881263 ◽  
Author(s):  
Paul Quillen ◽  
Kamesh Subbarao
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Predictive Control ◽  
Autonomous Mobile Robots ◽  
Autonomous Mobile Robot ◽  
Control Effort ◽  
Control Approach ◽  
Navigation Function ◽  
Guidance Law ◽  
High Level

This article puts forth a framework using model-based techniques for path planning and guidance for an autonomous mobile robot in a constrained environment. The path plan is synthesized using a numerical navigation function algorithm that will form its potential contour levels based on the “minimum control effort.” Then, an improved nonlinear model predictive control approach is employed to generate high-level guidance commands for the mobile robot to track a trajectory fitted along the planned path leading to the goal. A backstepping-like nonlinear guidance law is also implemented for comparison with the NMPC formulation. Finally, the performance of the resulting framework using both nonlinear guidance techniques is verified in simulation where the environment is constrained by the presence of static obstacles.

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