Real-time decision making under uncertainty of self-localization results

Abstract

In this paper, we present a real-time decision making method for a quadruped robot whose sensor and locomotion have large errors. We make a State-Action Map by off-line planning considering the uncertainty of the robot's location with Dynamic Programming (DP). Using this map, the robot can immediately decide optimal action that minimizes the time to reach a target state at any state. The number of observation is also minimized. We compress this map for implementation with Vector Quantization (VQ). Using the differences of the values between the optimal action and others as distortion measure of VQ minimizes the total loss of optimality. © Springer-Verlag Berlin Heidelberg 2003.