Multi-AUV task assignment and path planning with ocean current based on biological inspired self-organizing map and velocity synthesis algorithm

2015 ◽  
Vol 23 (1) ◽  
pp. 31-39 ◽  
Author(s):  
Xiang Cao ◽  
Daqi Zhu
Keyword(s):  
Path Planning ◽  
Task Assignment ◽  
Ocean Current ◽  
Self Organizing Map ◽  
Synthesis Algorithm ◽  
Self Organizing

scholarly journals Current Patterns on the West Florida Shelf from Joint Self-Organizing Map Analyses of HF Radar and ADCP Data

2007 ◽  
Vol 24 (4) ◽  
pp. 702-712 ◽  
Author(s):  
Yonggang Liu ◽  
Robert H. Weisberg ◽  
Lynn K. Shay
Keyword(s):  
Hf Radar ◽  
Ocean Current ◽  
Self Organizing Map ◽  
West Florida Shelf ◽  
The West ◽  
Florida Shelf ◽  
Buoyancy Forcing ◽  
Temporal Structures ◽  
Self Organizing

Abstract To assess the spatial structures and temporal evolutions of distinct physical processes on the West Florida Shelf, patterns of ocean current variability are extracted from a joint HF radar and ADCP dataset acquired from August to September 2003 using Self-Organizing Map (SOM) analyses. Three separate ocean–atmosphere frequency bands are considered: semidiurnal, diurnal, and subtidal. The currents in the semidiurnal band are relatively homogeneous in space, barotropic, clockwise polarized, and have a neap-spring modulation consistent with semidiurnal tides. The currents in the diurnal band are less homogeneous, more baroclinic, and clockwise polarized, consistent with a combination of diurnal tides and near-inertial oscillations. The currents in the subtidal frequency band are stronger and with more complex patterns consistent with wind and buoyancy forcing. The SOM is shown to be a useful technique for extracting ocean current patterns with dynamically distinctive spatial and temporal structures sampled by HF radar and supporting in situ measurements.

scholarly journals An Application of Self-Organizing Map for Multirobot Multigoal Path Planning with Minmax Objective

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Jan Faigl
Keyword(s):  
Path Planning ◽  
Unsupervised Learning ◽  
Shortest Paths ◽  
Polygonal Domain ◽  
Self Organizing Map ◽  
Multiple Traveling Salesman Problem ◽  
Robotic Planning ◽  
Inspection Task ◽  
Self Organizing

In this paper, Self-Organizing Map (SOM) for the Multiple Traveling Salesman Problem (MTSP) with minmax objective is applied to the robotic problem of multigoal path planning in the polygonal domain. The main difficulty of such SOM deployment is determination of collision-free paths among obstacles that is required to evaluate the neuron-city distances in the winner selection phase of unsupervised learning. Moreover, a collision-free path is also needed in the adaptation phase, where neurons are adapted towards the presented input signal (city) to the network. Simple approximations of the shortest path are utilized to address this issue and solve the robotic MTSP by SOM. Suitability of the proposed approximations is verified in the context of cooperative inspection, where cities represent sensing locations that guarantee to “see” the whole robots’ workspace. The inspection task formulated as the MTSP-Minmax is solved by the proposed SOM approach and compared with the combinatorial heuristic GENIUS. The results indicate that the proposed approach provides competitive results to GENIUS and support applicability of SOM for robotic multigoal path planning with a group of cooperating mobile robots. The proposed combination of approximate shortest paths with unsupervised learning opens further applications of SOM in the field of robotic planning.

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