Vision-Servoed Localization and Behavior-Based Planning for an Autonomous Quadruped Legged Robot

Planning actions for real robots in dynamic ,and mlcertain environments is a challenging problem. It is not viable to use a complete model of the world: it is most appropriate to achieve goals mid handle uncertainty by integrating deliberation and behavior-based reactive planning. We successfully developed a system integrating perception and action for the RoboCup99 Sony legged robot league. The quadruped legged robots are fully autonomous and thus must. have onboard vision, localization a~td action selection. We briefly present our perception algorithm that automatically classifies arid tracks colored blobs in real time. We then briefly introduce our Sensor Resetting I,ocalization (SRL) algorithm which is an extension of Monte Carlo Localization. Vision and localization provide the state input for action selection. Our robust and sensible behavior scheme handles dynamic changes in information accuracy. We developed a utility-based system for using mid acquiring location information. Finally, we have devised several special built-in plans to deal with times when urgent action is needed and the robot cannot afford to colh:ct accurate location information. We present results using the real robots, which demonst.rate the success of our approach. Our team of Sony quadruped legged robots, CMTrio-99, won all but one of its games in RoboCup99, and was awarded third place in the competition.