Jia Yuanyuan, Ma Shugen
Ritsumeikan University, Kyoto, Japan.
Bioinspir Biomim. 2023 Oct 24;18(6). doi: 10.1088/1748-3190/ad0350.
This paper presents a method which avoids the common practice of using a complex coupled snake robot model and performing kinematic analysis for control in cluttered environments. Instead, we introduce a completely decoupled dynamical Bayesian formulation with respect to interacted snake robot links and environmental objects, which requires much lower complexity for efficient and robust control. When a snake robot does not interact with obstacles, it runs by a simple serpenoid controller. However, when it exhibits interaction with environments, defined as close proximity or collision with targets and/or obstacles, we extend the conventional Bayesian framework by modeling such interactions in terms of stimuli. The proposed '' represents the cumulative effect of both external environmental influences and internal constraints of the snake robot. It implicitly handles the '' problem and thus solve the difficult data association and shape adjustment problems for snake robot control in an innovative way. Preliminary experimental results have demonstrated promising performance of the proposed method comparing with the state-of-the-art.
本文提出了一种方法，该方法避免了在杂乱环境中使用复杂的耦合蛇形机器人模型并进行运动学分析以进行控制的常见做法。相反，我们针对相互作用的蛇形机器人链路和环境物体引入了一种完全解耦的动态贝叶斯公式，这对于高效且鲁棒的控制而言所需的复杂度要低得多。当蛇形机器人不与障碍物相互作用时，它通过简单的类蛇形控制器运行。然而，当它与环境表现出相互作用时（定义为与目标和/或障碍物接近或碰撞），我们通过根据刺激对这种相互作用进行建模来扩展传统的贝叶斯框架。所提出的“”代表了外部环境影响和蛇形机器人内部约束的累积效应。它隐式地处理了“”问题，从而以创新的方式解决了蛇形机器人控制中困难的数据关联和形状调整问题。初步实验结果表明，与现有技术相比，该方法具有良好的性能。
