Unlike animals and humans who are very adept at push recovery, humanoid robots push recovery is difficult for its high dimensional, non-linear, and hybrid features. Existed research results such like Capture points, provide several methods to recover from the push. However when a high magnitude push applies to the humanoid, existed methods are not enough to recover the robot. Towards this problem, Continuous Steps method is proposed to solve this problem in this paper.We present simulation of a simple humanoid that can recover from a high magnitude push by using continuous steps. Future work involves extending the modeling to arbitrary direction pushes and applying the method to the real humanoid robots.
In this paper, the local minima free search algorithm using chaos is proposed for an unstructured search space. The problem is that given the quality function, find the value of a configuration that minimizes the quality function. The proposed algorithm started basically from the gradient search technique but at the prescribed points, that is, local minimum points, which are to be automatically detected the chaotic jump is introduced by the dynamics of a chaotic neuron. Chaotic motions are mainly because of the Gaussian function having a hysteresis as a refractoriness. In order to enhance the probability of finding the global minimum, a parallel search strategy is also given. The validity of the proposed method wil be verified in simulation examples of the function minimization problem and the motion planning problem of a mobile robot.
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