Closed-Loop Control in an Autonomous Bio-hybrid Robot System Based on Binocular Neuronal Input

In this paper, we describe the implementation of a closed-loop control architecture on a bio-hybrid robotic system. The control loop uses the spiking activity from two motion-sensitive H1-cells recorded in both halves of the blowfly's brain as visual feedback signals that are sent to an ARM processor, programmed to establish a brain machine interface. The resulting output controls the movements of the robot which, in turn, generates optic flow that modifies the activity of the H1-cells. Instead of being inhibited by front-to-back optic flow would the robot move forward in a straight line, the closed-loop system autonomously produces an oscillatory trajectory, alternatingly stimulating both H1-cells with back-to-front optic flow. The spike rate information of each cell is then used to control the speed of each robot wheel, on average driving the robot in the forward direction. Our extracellular recordings from the two cells show similar spike rate oscillation frequencies and amplitude, but opposite phases. From our experiments we derive parameters relevant for the future implementation of collision avoidance capabilities. Finally, we discuss a control algorithm that combines positive and negative feedback to drive the robot.