Biologically Inspired Spiking Neural Network for Autonomous Locomotion Control of Snake-Like Robots

Snake-like robots have been widely studied due to their special 3D locomotion ability and the adaptability in diverse complex environments. Autonomous locomotion, that is, acquiring environment information, making locomotion decisions independently and then performing locomotion, is of the essence for snake-like robots to complete self-governed tasks in various complex terrain. The traditional model-based control methods, including numerical, kinematic and geometric, however, cannot completely meet the challenges posed by dynamic and changing conditions, which has higher requirements on stability and adaptability. Moreover, the high degree of freedom (DOF) of snakelike robots also makes the process of analyzing the state of the robot in real-time difficult. The autonomous locomotion problem is becoming an area in which biology and robotics should closely interact [1].