Collective Gradient Perception in a Flocking Robot Swarm

2020 ◽  
pp. 290-297
Author(s):  
Tugay Alperen Karagüzel ◽  
Ali Emre Turgut ◽  
Eliseo Ferrante
Keyword(s):  
Robot Swarm

Urban Search and Rescue with Anti-pheromone Robot Swarm architecture

2021 ◽  
Author(s):  
Ruben Martin Garcia ◽  
Daniel Hernandez de la Iglesia ◽  
Juan F. de Paz ◽  
Valderi R. Q. Leithardt ◽  
Gabriel Villarrubia
Keyword(s):  
Search And Rescue ◽  
Urban Search And Rescue ◽  
Robot Swarm

Balancing robot swarm cost and interference effects by varying robot quantity and size

2018 ◽  
Vol 13 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Adam Schroeder ◽  
Brian Trease ◽  
Alessandro Arsie
Keyword(s):  
Interference Effects ◽  
Robot Swarm ◽  
Balancing Robot

Decentral control of a robot-swarm

2005 ◽  
Author(s):  
I. Dahm ◽  
M. Hebbel ◽  
M. Hulsbusch ◽  
J. Kerdels ◽  
W. Nistico ◽  
...  
Keyword(s):  
Robot Swarm

Distributed Shortest-Path Finding by a Micro-robot Swarm

2006 ◽  
pp. 404-411 ◽  
Author(s):  
Marc Szymanski ◽  
Tobias Breitling ◽  
Jörg Seyfried ◽  
Heinz Wörn
Keyword(s):  
Shortest Path ◽  
Path Finding ◽  
Robot Swarm ◽  
Micro Robot

Path formation in a robot swarm

2007 ◽  
Vol 2 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Shervin Nouyan ◽  
Alexandre Campo ◽  
Marco Dorigo
Keyword(s):  
Robot Swarm

scholarly journals Failure-Robot Path Complementation for Robot Swarm Mission Planning

2019 ◽  
Vol 9 (18) ◽  
pp. 3756 ◽  
Author(s):  
Meng-Tse Lee ◽  
Bo-Yu Chen ◽  
Wen-Chi Lu
Keyword(s):  
Search Algorithm ◽  
Optimal Solution ◽  
Unmanned Vehicles ◽  
Target Point ◽  
Diversification Strategy ◽  
Robot Swarm ◽  
Limited Power ◽  
Time Distance ◽  
Global Optimal ◽  
Robot Path

Currently, unmanned vehicles are widely used in different fields of exploration. Due to limited capacities, such as limited power supply, it is almost impossible for one unmanned vehicle to visit multiple wide areas. Multiple unmanned vehicles with well-planned routes are required to minimize an unnecessary consumption of time, distance, and energy waste. The aim of the present study was to develop a multiple-vehicle system that can automatically compile a set of optimum vehicle paths, complement failed assignments, and avoid passing through no-travel zones. A heuristic algorithm was used to obtain an approximate solution within a reasonable timeline. The A* Search algorithm was adopted to determine an alternative path that does not cross the no-travel zone when the distance array was set, and an improved two-phased Tabu search was applied to converge any initial solutions into a feasible solution. A diversification strategy helped identify a global optimal solution rather than a regional one. The final experiments successfully demonstrated a group of three robot cars that were simultaneously dispatched to each of their planned routes; when any car failed during the test, its path was immediately reprogrammed by the monitoring station and passed to the other cars to continue the task until each target point had been visited.

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