Multi-objective evolutionary algorithm for neural oscillator based robot locomotion

In this paper we present synaptic weight optimization for dynamic locomotion in four-legged robot (cat, dog) based on neural oscillator. We investigate the muscular structure to design the relationship for both extensor neuron and flexor neuron. The robot has 3 joints in each leg and each joint is represented by 2 neurons, extensor and flexor neuron. The robot has 4 main circular neurons as the server neuron and the other neurons are the client neurons. The server neurons generate the oscillator signal to the client neurons. The signal can be dynamically adjusted according to the environmental condition. Not only the synaptic network between the neurons, but the synaptic network between neurons and sensors was also designed to realize dynamical locomotion. Pressure sensor and inclination sensor were installed in the robot. The signal is influenced by ground reaction sensor and body inclination feedback. While the foot touches the ground, the sensory neuron sends the signal to the joint neuron. Negative signal will be sent to flexor neuron and positive signal will be sent to extensor neuron. To optimize the strength of weights in the synaptic neurons we apply the Nondominated Sorting Genetic Algorithm II (NSGA-II). The stability of torso body, the velocity, and the movement direction are the three objectives in the multi-objective NSGA-II. In the experiments, a computer simulation framework, the Open Dynamic Engine (ODE) is applied. The solution is evaluated based mainly on the moving distance of the robot. Experiments were conducted to confirm the proposed technique.