Vector quantization for state-action map compression
Vector quantization for state-action map compression
复制标题
用于状态动作图压缩的矢量量化
DOI：
10.1109/robot.2003.1241945
复制
发表时间：
2003
期刊：
影响因子：
--
通讯作者：
T. Arai
中科院分区：
文献类型：
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作者：
R. Ueda;Takeshi Fukase;Yuichi Kobayashi;T. Arai
通过预计算算法实现移动机器人的智能听起来很聪明，因为它可以减少安装在机器人上的小计算机的计算。然而，预计算结果的数量通常太大，无法存储。提出了一种动态规划预计算数据的压缩方法。通过对信息量评价方法的研究，提出了一种矢量量化方法。采用动态规划的方法对RoboCup中的机器人运动进行规划。最优状态-动作图上的状态一旦被绑定到相邻的组中，然后被压缩成极少量的状态-动作。失真，即压缩的不良副作用，被评估并最小化。该方法在仿真和机器人实验上都得到了验证。
It sounds clever to achieve intelligence of a mobile robot by means of pre-computed algorithm, because it can cut down computation on a small computer installed on the robot. However, the amount of pre-computed results is usually too large to store. This paper proposes a compression method for pre-computed data of dynamic programming. A vector quantization method is proposed with the studies on entropy evaluation. Robot motions in RoboCup are planned by means of dynamic programming. States on the optimal state-action map are once bounded into a neighboring group and then compressed into a tiny number of state-action. The distortion, the bad side effect of compression, is evaluated and minimized. The proposed method is verified on both simulations and experiments of robots.