Paper Info
Title
An island-model framework for evolving neuro-controllers for planetary rover control
Abstract
Autonomous navigation and robust obstacle avoidance are prerequisites for the successful operation of a planetary rover. Typical approaches to tackling this problem rely on complex and computationally expensive navigation strategies based upon the creation of 3D maps of the environment. In contrast, this research proposes a simple artificial neural network relying on infrared sensory input as the control structure. This paper presents a unified framework for designing such control structures for a simulated rover, taking advantage of code parallelisation and the latest advances in global optimisation research. In particular, it details a 3D physics-based simulation of a planetary rover and a tool set for performing the optimisation of ANN parameters within the island model. This paper also presents preliminary results showing that the aforementioned framework can parallelise the controller design process without any loss in performance over traditional methods, and will outline research directions, which aim to take full advantage of this technique's potential.
Year
DOI
Venue
2010
10.1109/IJCNN.2010.5596942
Neural Networks
Keywords
Field
DocType
collision avoidance,control system synthesis,navigation,neurocontrollers,optimisation,planetary rovers,3D maps,3D physics-based simulation,artificial neural network,autonomous navigation,control structure design,controller design,global optimisation research,infrared sensory input,island-model framework,neuro-controllers,obstacle avoidance,planetary rover control
Obstacle avoidance,Controller design,Computer science,Simulation,Robot kinematics,Island model,Control engineering,Artificial intelligence,Artificial neural network,Planetary rover,Machine learning
Conference
ISSN
ISBN
Citations 
1098-7576
978-1-4244-6916-1
0
PageRank 
References 
Authors
0.34
13
4
Name
Order
Citations
PageRank
Peniak, M.100.34
Bentley, B.200.34
Marocco, D.300.34
Cangelosi, A.4794.58