Temporally and Spatially Deconflicted Path Planning for Multiple Autonomous Marine Vehicles

Author(s):  
Häusler, Andreas
Author(s):  
Andreas J. Hausler ◽  
Reza Ghabcheloo ◽  
Isaac Kaminer ◽  
Antonio M. Pascoal ◽  
A. Pedro Aguiar

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2515 ◽  
Author(s):  
Chengke Xiong ◽  
Hexiong Zhou ◽  
Di Lu ◽  
Zheng Zeng ◽  
Lian Lian ◽  
...  

This research presents a novel sample-based path planning algorithm for adaptive sampling. The goal is to find a near-optimal path for unmanned marine vehicles (UMVs) that maximizes information gathering over a scientific interest area, while satisfying constraints on collision avoidance and pre-specified mission time. The proposed rapidly-exploring adaptive sampling tree star (RAST*) algorithm combines inspirations from rapidly-exploring random tree star (RRT*) with a tournament selection method and informative heuristics to achieve efficient searching of informative data in continuous space. Results of numerical experiments and proof-of-concept field experiments demonstrate the effectiveness and superiority of the proposed RAST* over rapidly-exploring random sampling tree star (RRST*), rapidly-exploring adaptive sampling tree (RAST), and particle swarm optimization (PSO).


2009 ◽  
Vol 42 (18) ◽  
pp. 376-381 ◽  
Author(s):  
Andreas J. Häusler ◽  
Reza Ghabcheloo ◽  
António M. Pascoal ◽  
A. Pedro Aguiar ◽  
Isaac I. Kaminer ◽  
...  

2018 ◽  
Vol 51 (29) ◽  
pp. 305-310 ◽  
Author(s):  
Vahid Hassani ◽  
Simen V. Lande

2020 ◽  
Vol 215 ◽  
pp. 107901
Author(s):  
Jialei Zhang ◽  
Xianbo Xiang ◽  
Weijia Li ◽  
Shaolong Yang ◽  
Qin Zhang

2018 ◽  
Vol 43 (3) ◽  
pp. 640-664 ◽  
Author(s):  
Zheng Zeng ◽  
Karl Sammut ◽  
Lian Lian ◽  
Andrew Lammas ◽  
Fangpo He ◽  
...  

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