Biologically-inspired adaptive obstacle negotiation behavior of hexapod robots.

Dennis Goldschmidt,Poramate Manoonpong,Florentin Wörgötter

doi:10.3389/fnbot.2014.00003

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.

Highlights

Insects are truly remarkable creatures with fascinating capabilities
AMOS II has mounted 21 sensors in total: two ultrasonic (US) sensors attached to the front body part, six foot contact (FC) sensors for its legs, six reflex infrared (IR) sensors located at the front of its legs (Figure 1B), a built-in current sensor (BICS) and an inclinometer sensor (IM) inside the trunk, three light dependent resistors (LDR), a USB camera (CM), and a laser scanner (LS) at the front body part (Figure 1C)
As described in the previous section, the plastic synapse ρ1 of the learner neuron increases due to the correlation of the predictive and the reflex signal provided by the US sensors

Summary

Introduction

Insects are truly remarkable creatures with fascinating capabilities. their brains are much simpler structures than the human brain, this does not limit their capacity of generating a vast variety of complex behaviors (Chittka and Niven, 2009). The adaptability of motor behavior in insects enables an effective response to unknown and novel situations. These observations have encouraged many roboticists to develop biologically-inspired robots and control schemes. Recent works on obstacle negotiation have been done on hybrid leg-wheeled robots (Chen et al, 2011; Chou et al, 2011). These robots have fewer degrees of freedom and can be more controlled than legged robots. In contrast to legged robots, these robots have problems performing versatile locomotion such as omnidirectional walking and negotiating very tall or narrow steps due to their reduced mobility

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