Biologically Inspired Robot Arm Control Using Neural Oscillators

Humans or animals exhibit natural adaptive motions against unexpected disturbances or environment changes. This is because that, in general, the neural oscillator based circuits on the spinal cord known as Central Pattern Generators (CPGs) might contribute to efficient motor movement and novel stability properties in biological motions of animal and human. Based on the CPGs, most animals locomote stably using inherent rhythmic movements adapted to the natural frequency of their body dynamics in spite of differences in their sensors and actuators. For such reasons, studies on human-like movement of robot arms have been paid increasing attention. In particular, human rhythmic movements such as turning a steering wheel, rotating a crank, etc. are self-organized through the interaction of the musculoskeletal system and neural oscillators. In the musculoskeletal system, limb segments connected to each other with tendons are activated like a mechanical spring by neural signals. Thus neural oscillators may offer a reliable and cost efficient solution for rhythmic movement of robot arms. Incorporating a network of neural oscillators, we expect to realize human nervous and musculoskeletal systems in various types of robots. The mathematical description of a neural oscillator was presented in Matsuoka’s works (Matsuoka, 1985). He proved that neurons generate the rhythmic patterned output and analyzed the conditions necessary for the steady state oscillations. He also investigated the mutual inhibition networks to control the frequency and pattern (Matsuoka, 1987), but did not include the effect of the feedback on the neural oscillator performance. Employing Matsuoka’s neural oscillator model, Taga et al. investigated the sensory signal from the joint angles of a biped robot as feedback signals (Taga et al., 1991), showing that neural oscillators made the robot robust to the perturbation through entrainment (Taga, 1995). This approach was applied later to various locomotion systems (Miyakoshi et al., 1998), (Fukuoka et al., 2003), (Endo et al., 2005), (Yang et al., 2008). Besides the examples of locomotion, various efforts have been made to strengthen the capability of robots from biological inspiration. Williamson created a humanoid arm motion based on postural primitives. The spring-like joint actuators allowed the arm to safely deal with unexpected collisions sustaining cyclic motions (Williamson, 1996). And the neuromechanical system coupled with the neural oscillator for controlling rhythmic arm motions 8