Neural based RSPN multi-agent strategy for biped motion control

Abstract

In this paper fhe problem of motion control of a biped is considered. We develop a new method based on multi-agent associated Neural AIGLS (On-line Augmented Integration of Gradient and Last Sguare method) – RSPN (Recursive Stochastic Petri Nets) strategy. This method deals with organization and coordination aspects in an intelligent modeling of human motion. We propose a cooperative multi-agent model. Based on this model, we develop a control kernel named IMCOK (Intelligent Motion COntrol Kernel) which consists of a controller, a coordinator and an executor of different cycles of the motion of the biped. When walking, IMCOK receives messages and sends offers. A Decision Making of Actions (DMA) is developed at the supervisor level. The articulator agents partially planify the motion of the associated non-articulator agents. The system is hybrid and distributed functionally. The learning of the biped is performed using an On-line Augmented Integration of Gradient and Last Sguare Neural Networks based algorithm. In the conflictual situations of sending or receiving messages by the managers of MABS we apply a new strategy: Recursive Stochastic Petri Nets (RSPN). This module is fundamental in the On-line information processing between agents. It allows particularly the Recursive strategy concept. Cognitive agents communicate with reactive (non-articulator) agents in order to generate the motion.