Title :
GENCEM: a genetic algorithms approach to coordinated exploration and mapping with multiple autonomous robots
Author :
Sotzing, Chris C. ; Htay, Win Mar ; Congdon, Clare Bates
Author_Institution :
Ocean Syst. Lab, Heriot-Watt Univ., Edinburgh, UK
Abstract :
GENCEM is a genetic algorithms approach to coordinated exploration and mapping with multiple autonomous robots. Building on previous work in coordinated mapping, the work reported here compares static to evolutionary approaches for the same coordination tasks. In GENCEM, parameters affecting the coordination behaviors are evolved, leading to a decided improvement over hand-coded parameter settings across a variety of environments and using different numbers of robots. The success of this preliminary study demonstrates the viability of this approach for learning to coordinate, representing the first stage of implementation of a larger system for more complex coordination tasks and strategies.
Keywords :
genetic algorithms; learning (artificial intelligence); mobile robots; GENCEM; coordinated exploration; coordinated mapping; coordination behaviors; evolutionary approach; genetic algorithms; multiple autonomous robots; static approach; Centralized control; Computer science; Control systems; Drives; Educational institutions; Genetic algorithms; Humans; Oceans; Robot kinematics; Robot sensing systems;
Conference_Titel :
Evolutionary Computation, 2005. The 2005 IEEE Congress on
Print_ISBN :
0-7803-9363-5
DOI :
10.1109/CEC.2005.1554983