In this paper, a compound biped locomotion algorithm for a humanoid robot under development is presented. This paper is organized in two main parts. In the first part, it mainly focuses on the structural design for the humanoid. In the second part, the compound biped locomotion algorithm is presented based on the reference motion and reference Zero Moment Point (ZMP). This novel algorithm includes calculation of the upper body motion and trajectory of the Center of Gravity (CoG) of the robot. First, disturbances from the environment are eliminated by the compensational movement of the upper body; then based on the error between a reference ZMP and the real ZMP as well as the relation between ZMP and CoG, the CoG error is calculated, thus leading to the CoG trajectory. Then, the motion of the robot converges to its reference motion, generating stable biped walking. Because the calculation of upper body motion and tra-jectory of CoG both depend on the reference motion, they can work in parallel, thus providing double insurances against the robot's collapse. Finally, the algorithm is validated by different kinds of simulation experiments.
A humanoid robot is always fiooded by sensed information when sensing the environment, and it usually needs significant time to compute and process the sensed information. In this paper, a selective attention-based contextual perception approach was proposed for humanoid robots to sense the environment with high efficiency. First, the connotation of attention window (AW) is extended to make a more general and abstract definition of AW, and its four kinds of operations and state transformations are also discussed. Second, the attention control policies are described, which integrate intension- guided perceptual objects selection and distractor inhibition, and can deal with emergent issues. Distractor inhibition is used to filter unrelated information. Last, attention policies are viewed as the robot’s perceptual modes, which can control and adjust the perception efficiency. The experimental results show that the presented approach can promote the perceptual efficiency significantly, and the perceptual cost can be effectively controlled through adopting different attention policies.
