Abstract
Nowadays, humanoids are increasingly expected acting in the real world to complete some high-level tasks humanly and intelligently. However, this is a hard issue due to that the real world is always extremely complicated and full of miscellaneous variations. As a consequence, for a real-world-acting robot, precisely perceiving the environmental changes might be an essential premise. Unlike human being, humanoid robot usually turns out to be with much less sensors to get enough information from the real world, which further leads the environmental perception problem to be more challenging. Although it can be tackled by establishing direct sensory mappings or adopting probabilistic filtering methods, the nonlinearity and uncertainty caused by both the complexity of the environment and the high degree of freedom of the robots will result in tough modeling difficulties. In our study, with the Gaussian process regression framework, an alternative learning approach to address such a modeling problem is proposed and discussed. Meanwhile, to debase the influence derived from limited sensors, the idea of fusing multiple sensory information is also involved. To evaluate the effectiveness, with two representative environment changing tasks, that is, suffering unknown external pushing and suddenly encountering sloped terrains, the proposed approach is applied to a humanoid, which is only equipped with a three-axis gyroscope and a three-axis accelerometer. Experimental results reveal that the proposed Gaussian process regression-based approach is effective in coping with the nonlinearity and uncertainty of the humanoid environmental perception problem. Further, a humanoid balancing controller is developed, which takes the output of the Gaussian process regression-based environmental perception as the seed to activate the corresponding balancing strategy. Both simulated and hardware experiments consistently show that our approach is valuable and leads to a good base for achieving a successful balancing controller for humanoid.
Highlights
Environmental perception is one of the most elementary functionalities of intelligent robots
We focus on how Gaussian process (GP) regression is used for settling the above modeling problems for humanoid environmental perception with the integration of inertial sensory informations
With simulation platform Webots 6.0 developed by Cyberbotics Ltd., experiments to evaluate the GP regressionbased environmental perception approach are performed, along with the experiments on the performance of the presented humanoid robot balancing controller
Summary
Environmental perception is one of the most elementary functionalities of intelligent robots. As discussed at the end part of section ‘‘Perception tasks under two typical environment changing cases,’’ the mapping function to be estimated is right such a case that depends on sensory inputs and on the phase variable t ph as indicated by equation (5). To deal with this problem, a simple strategy is introduced that segmenting the whole training data into bins according to t ph.
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