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Front Neurorobot. 2014 Jan 29;8:3. doi: 10.3389/fnbot.2014.00003. eCollection 2014.

Biologically-inspired adaptive obstacle negotiation behavior of hexapod robots.

Author information

1
Bernstein Center for Computational Neuroscience, Third Institute of Physics, Georg-August-Universität Göttingen Göttingen, Germany ; Institute of Neuroinformatics, University of Zurich and ETH Zurich Zurich, Switzerland.
2
Bernstein Center for Computational Neuroscience, Third Institute of Physics, Georg-August-Universität Göttingen Göttingen, Germany.
3
Bernstein Center for Computational Neuroscience, Third Institute of Physics, Georg-August-Universität Göttingen Göttingen, Germany ; Mærsk Mc-Kinney Møller Institute, University of Southern Denmark Odense, Denmark.

Abstract

Neurobiological studies have shown that insects are able to adapt leg movements and posture for obstacle negotiation in changing environments. Moreover, the distance to an obstacle where an insect begins to climb is found to be a major parameter for successful obstacle negotiation. Inspired by these findings, we present an adaptive neural control mechanism for obstacle negotiation behavior in hexapod robots. It combines locomotion control, backbone joint control, local leg reflexes, and neural learning. While the first three components generate locomotion including walking and climbing, the neural learning mechanism allows the robot to adapt its behavior for obstacle negotiation with respect to changing conditions, e.g., variable obstacle heights and different walking gaits. By successfully learning the association of an early, predictive signal (conditioned stimulus, CS) and a late, reflex signal (unconditioned stimulus, UCS), both provided by ultrasonic sensors at the front of the robot, the robot can autonomously find an appropriate distance from an obstacle to initiate climbing. The adaptive neural control was developed and tested first on a physical robot simulation, and was then successfully transferred to a real hexapod robot, called AMOS II. The results show that the robot can efficiently negotiate obstacles with a height up to 85% of the robot's leg length in simulation and 75% in a real environment.

KEYWORDS:

adaptive behavior; associative learning; autonomous robots; backbone joint control; neural control; obstacle negotiation

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