Performance optimisation of mobile robots in dynamic environments

2012 ◽  
Author(s):  
S. H. Choi ◽  
W. K. Zhu
Keyword(s):  
Mobile Robots ◽  
Dynamic Environments

Improving Dense Mapping for Mobile Robots in Dynamic Environments Based on Semantic Information

2020 ◽  
pp. 1-1
Author(s):  
Jiyu Cheng ◽  
Chaoqun Wang ◽  
Xiaochun Mai ◽  
Zhe Min ◽  
Max Q.-H. Meng
Keyword(s):  
Mobile Robots ◽  
Semantic Information ◽  
Dynamic Environments

scholarly journals Learning Innovative Routes for Mobile Robots in Dynamic Partially Unknown Environments

10.5772/5787 ◽  
2005 ◽  
Vol 2 (3) ◽  
pp. 21
Author(s):  
Kristo Heero ◽  
Alvo Aabloo ◽  
Maarja Kruusmaa
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Shortest Path ◽  
Dynamic Environments ◽  
Distance Transform ◽  
Test Results ◽  
Approach To Learning ◽  
Planning Strategy ◽  
Unknown Environments ◽  
Planning Strategies

This paper examines path planning strategies in partially unknown dynamic environemnts and introduces an approach to learning innovative routes. The approach is verified against shortest path planning with a distance transform algorithm, local and global replanning and suboptimal route following in unknown, partially unknown, static and dynamic environments. We show that the learned routes are more reliable and when traversed repeatedly the robot's behaviour becomes more predictable. The test results also suggest that the robot's behaviour depends on knowledge about the environemnt but not about the path planning strategy used.

Motion Planning of Mobile Robots for Occluded Obstacles

2018 ◽  
Vol 30 (3) ◽  
pp. 485-492
Author(s):  
Satoshi Hoshino ◽  
◽  
Tomoki Yoshikawa
Keyword(s):  
Motion Planning ◽  
Mobile Robots ◽  
Dynamic Environments ◽  
Simulation Experiments ◽  
Moving Obstacles ◽  
Moving Direction ◽  
Velocity Obstacle

Motion planning of mobile robots for occluded obstacles is a challenge in dynamic environments. The occlusion problem states that if an obstacle suddenly appears from the occluded area, the robot might collide with the obstacle. To overcome this, we propose a novel motion planner, the Velocity Obstacle for occlusion (VOO). The VOO is based on a previous motion planner, the Velocity Obstacle (VO), which is effective for moving obstacles. In the proposed motion planner, information uncertainties about occluded obstacles, such as position, velocity, and moving direction, are quantitatively addressed. Thus, the robot based on the VOO is able to move not only among observed obstacles, but also among the occluded ones. Through simulation experiments, the effectiveness of the VOO for the occlusion problem is demonstrated by comparison with the VO.

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