State Space Partitioning and Clustering with Sensor Alignment for Autonomous Robots

Abstract

The goal of this study is to develop an intelligent wheelchair (IWC) acquiring autonomous, cooperative, and collaborative behavior. An important part of achieving the goal is that the agent controlling the IWC can realize suitable partitioning state of new environment for developing a behavior policy without human intervention. In particular, domestic robots like the IWCs which have low-cost, discrete simple range sensors have difficulty of efficient learning of environment because the deviation of sensor configuration in each robot causes serious problem in which each learning policy can not be shared among them. To overcome this problem, a new method of alignment and clustering of sensor space is conducted. The alignment process reconfigures sensor configurations on concentric circle from spot-turn behavior of IWC robots in order to compensate the deviation of the configuration. The clustering process realizes self-organized state space with simple vector partitioning method. The simulation experiment results have shown the method gives the IWC agent an ability of autonomous partitioning of environment and efficient constructing state space.