柔性机器人多层启发式动态规划平衡认知研究

Research on Balance Cognition Based on Multi-level Heuristic Dynamic Programming of Flexible Robot

针对柔性自平衡机器人的稳定自平衡认知问题,提出一种基于多层启发式动态规划认知(Multi-level heuristic dynamic programming,Ml HDP)模型的平衡认知方法,将这种认知方法用于柔性自平衡机器人自平衡学习中。通过引入取向奖赏模块,把原有离散形式的奖赏机制转化为连续形式,以转化后的连续奖赏信号作为评价的主要依据。该方案使得机器人在自主认知的过程中能够记录更多信息量,提高其认知能力。通过机器人的自平衡认知实验可以看出,在机器人具有柔性关节的条件下仍然具备良好的认知能力,学习效果优于传统方法,鲁棒性强。

Aiming at the stable self-balancing cognition problems of flexible self-balancing robot, a balance cognition method based on multi-level heuristic dynamic programming is proposed and applied on the self-balance learning of flexible self-balancing robot in this paper In the proposed cognition method, the original reward mechanism with discrete form is transformed into a continuous form by introducing the orientational reward module, and the converted continuous reward signal is used as the major basis for evaluation. The scheme enables the robot to record more information in the autonomic cognition process and improve its cognitive ability. Through the robot self-balancing cognitive experiment, it can be seen that the robot can still be able to achieve good cognitive ability even the robot contains flexible joints. Its learning effect and robustness are better than traditional method.