Bio-inspired neural networks for decision-making mechanisms and neuromodulation for motor control in a differential robot

Abstract

The aim of this work is to propose bio-inspired neural networks for decision-making mechanisms and modulation of motor control of an automaton. In this work, we have adapted and applied cortical synaptic circuits, such as short-term memory circuits, winner-take-all (WTA) class competitive neural networks, modulation neural networks, and nonlinear oscillation circuits, in order to make the automaton able to avoid obstacles and explore simulated and real environments. The performance achieved by using biologically inspired neural networks to solve the task at hand is similar to that of several works mentioned in the specialized literature. Furthermore, this work contributed to bridging the fields of computational neuroscience and robotics.

Keywords: adaptation stage; automaton; bio-inspired neural network; differential robot; exploration behavior; neuromodulation network; signal processing.

Figure 1

Architecture of the bio-inspired network for the exploration behavior.

Figure 2

Short-term memory circuit. Recurrent excitation. Two circuits similar to that shown above, process the signals A_r and A_l, respectively. The four processing units shown correspond to the units named in Equations (3)–(6) y (7)–(10).

Figure 3

Memory linear chain. Two circuits similar to that shown above, process the propagation of the short-term memory circuit (Figure 2). The five processing units shown correspond to the units named in Equations (11), (12) y (14), (15).

Figure 4

Architecture of the bio-inspired network for the exploration behavior. (A) Architecture of the bio-inspired network for the exploration behavior. (B) comparison circuit.

Figure 5

Competitive neural network. Class winner-take-all (WTA).

Figure 6

Memory linear chain for the adaptation stage. Two circuits similar to that shown above, process the propagation of O₁ and O₂ in the meta-decision circuit (Figure 5). The five processing units shown correspond to the units named in Equations (22)–(24).

Figure 7

Meta-control circuit.

Figure 8

Non-linear oscillation generator circuit. (A) Non-linear oscillation circuit for turning left. (B) Non-linear oscillation generator circuit for turning right. (C) Non-linear oscillation generator circuit for forward motion.

Figure 9

Turtlebot3 Burger model dimensions. Taken from Robotis (2022).

Figure 10

Signal processing. Take l for left and r for right. Take i for inside and o for outside.

Figure 11

Simulation environment results. Figures on the left side show the Gazebo simulation environment without the meta-control circuit. The right side images show the trajectory made by the robot in the exploration behavior with the meta-control circuit. (A, B) Loop. (C, D) Zigzag. (E, F) Cross. (G, H) Traditional maze. In (B, D, F, H), one can observe how we obtain a better performance using the meta-control network and allowing to achieve a greater trajectory in (F, H).

Figure 12

Implementation environment results. The path made by the automaton was drawn with red lines in each type of environment. Green circles are initial positions and blue circles are final positions. (A) Loop. (B) Cross. (C, D) Traditional maze part 1 and part 2, respectively. (E) Zigzag. (F) Simple maze. There one can be observed how the automaton completed the (A, E, F) environments successfully. In the (B) environment the automaton's performance started in the middle of the cross-environment and finished doing circles around the environment. In the (C, D) environments, there can be observed how the automaton's trajectory finishes at its starting point.

Figure 13

Implementation of the zigzag environment. (A) Real signals obtained from the environment and its processing in time. The top left image exhibits the LiDAR's points processing inside a corridor of the zigzag environment. Blue dots correspond to the points inside the safe area and red dots are the points outside the safe area. S₁, A_r, and A_l curves are the inputs signals for the bio-inspired network (Section 2.5), these were sampled within an interval of 360ms. The automaton's trajectory seen in Figure 12E is a result of processing A_r and A_l signals. The biggest values of A_r and A_l appear when the robot executes turns. (B) Meta-control circuit projection G. (C) Motor control signals of the mobile automaton. This signal was sampled within an interval of 1.0ms. Blue and orange signals correspond to the left wheel and the right wheel, respectively.