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Although the fall of a humanoid robot is rare in controlled environments, it cannot be avoided in the real world where the robot may physically interact with the environment. Our earlier work introduced the strategy of direction changing fall, in which the robot attempts to reduce the chance of human injury by changing its default fall direction in realtime and falling in a safer direction. The current paper reports further theoretical developments culminating in a successful hardware implementation of this fall strategy conducted on the Aldebaran NAO robot. This includes new algorithms for humanoid kinematics and Jacobians involving coupled joints and a complete estimation of the body frame attitude using an additional inertial measurement unit. Simulations and experiments are smoothly handled by our platform independent humanoid control software called Locomote. We report experiment scenarios where we demonstrate the effectiveness of the proposed strategies in changing the fall direction.
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