Active balancing in autonomous humanoid robots is a challenging task due to the complexity of combining a walking gait with dynamic balancing, vision and high-level behaviors. Humans not only walk successfully over even and uneven terrain, but can recover from the interaction of external forces such as impacts with obstacles and active pushes. While push recovery has been demonstrated successfully in, expensive robots, it is more challenging with robots that are inexpensive, with limited power in actuators and less accurate sensing. This work describes a closed-loop feedback control method that uses an accelerometer and gyroscope to allow an inexpensive humanoid robot to actively balance while walking and recover from pushes. Three common balancing strategies: center of pressure, centroidal moment pivot, and step-out, for biped robots are studied. An experiment is performed to test three hand-tuned closed-loop feedback control configurations; using only the gyroscope, only the accelerometer, and a combination of both sensors to recover from pushes. Each of the sensors is discretized into four discrete domains in order to categorize pushes with different strengths. Experimental results show that the combination of gyroscope and accelerometer outperforms the other methods with 100% recovery from a light push and 70% recovery from a strong push. The proposed closed-loop feedback control is examined in both simulation and real-world.
ASJC Scopus subject areas