Reinforcement learning in a behaviour-based control architecture for marine archaeology

2015 ◽  
Author(s):  
Gordon Frost ◽  
Francesco Maurelli ◽  
David M Lane
Keyword(s):  
Reinforcement Learning ◽  
Control Architecture ◽  
Marine Archaeology

scholarly journals A Multi-Agent Control Architecture for a Robotic Wheelchair

2006 ◽  
Vol 3 (3) ◽  
pp. 179-189 ◽  
Author(s):  
C. Galindo ◽  
A. Cruz-Martin ◽  
J. L. Blanco ◽  
J. A. Fernńndez-Madrigal ◽  
J. Gonzalez
Keyword(s):  
Reinforcement Learning ◽  
Control Architecture ◽  
Daily Lives ◽  
Multi Agent ◽  
Human Skills ◽  
Overall Performance ◽  
Agent Control ◽  
Robotic Wheelchairs ◽  
Assistant Robots ◽  
Robotic Wheelchair

Assistant robots like robotic wheelchairs can perform an effective and valuable work in our daily lives. However, they eventually may need external help from humans in the robot environment (particularly, the driver in the case of a wheelchair) to accomplish safely and efficiently some tricky tasks for the current technology, i.e. opening a locked door, traversing a crowded area, etc. This article proposes a control architecture for assistant robots designed under a multi-agent perspective that facilitates the participation of humans into the robotic system and improves the overall performance of the robot as well as its dependability. Within our design, agents have their own intentions and beliefs, have different abilities (that include algorithmic behaviours and human skills) and also learn autonomously the most convenient method to carry out their actions through reinforcement learning. The proposed architecture is illustrated with a real assistant robot: a robotic wheelchair that provides mobility to impaired or elderly people.

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