Paper Info
Title
Integrating neuroscience-based models towards an autonomous biomimetic Synthetic Forager
Abstract
Foraging can be described as goal-oriented exploration for resources. It exemplifies how animals coordinate complex sensory and effector systems under varying environmental conditions. To emulate the foraging capabilities of natural systems is a major goal for robotics. Therefore, foraging is an excellent paradigm to benchmark novel autonomous control strategies. Here we describe the biomimetic control architecture of the Synthetic Forager (SF), an effort to integrate multiple biologically constrained models of specific perceptual and cognitive processes pertaining to foraging into one general autonomous robot controller. This proposal is built upon the well-established Distributed Adaptive Control (DAC) framework and brings together neuroscience-based models of decision-making, multi-modal sensory processing, localization and mapping and allostatic behavioral control. To show the potential of the SF model we used it to control a high-mobility wheeled robotic platform in three behavioral tasks similar to experimental protocols applied to rodents. We show that the robot can reliably perform cue detection, rule learning and goal-oriented navigation in open environments. We propose that this approach to robotics allows both the study of embodied neuroscience models and the transfer of brain based principles to robotic systems.
Year
DOI
Venue
2011
10.1109/ROBIO.2011.6181287
Robotics and Biomimetics
Keywords
Field
DocType
biomimetics,mobile robots,allostatic behavioral control,animals coordinate complex sensory,autonomous biomimetic synthetic forager,autonomous control strategies,behavioral tasks,biologically constrained models,biomimetic control architecture,decision making,distributed adaptive control,goal oriented exploration,goal oriented navigation,high mobility wheeled robotic,multi modal sensory processing,natural systems,neuroscience based models integration,neuroscience models,robot controller,robotic systems,rule learning
Control theory,Neuroscience,Control engineering,Artificial intelligence,Engineering,Adaptive control,Robot,Autonomous robot,Mobile robot,Robotics,Foraging,Sensory processing
Conference
ISBN
Citations 
PageRank 
978-1-4577-2136-6
1
0.36
References 
Authors
11
7
Name
Order
Citations
PageRank
César Rennó-Costa1143.47
Andre Luvizotto2222.70
Encarni Marcos372.57
Armin Duff4498.22
Martí Sánchez-Fibla54411.13
Paul F. M. J. Verschure6677116.64
Renno-Costa, C.710.36