A homeostatic-adaptive approch for controlling robotic systems

Abstract

In this work, we present a hybrid mechanism for controlling a mobile robotic system which combines concepts from homeostatic control and adaptive behavior. The homeostatic control is inspired by an emotional approach consisting of a set of artificial hormones [2] computed from pre-categorical sensory data and also from high-level application results. The adaptive behavior is implemented by a fuzzy controller whose rules dynamically modify several system parameters, using the same structure to control both low-level hormonal loops and high-level application tasks. The objective of this proposal is twofold: guarantee an acceptable image quality keeping the perceptual data into a homeostatic regime, and use the adaptive behavior to obtain a better resource management and dynamic response. Fig. 1. Homeostatic regulation mechanism Fig. 2. Set of camera parameters In Figure 1 the outline of a homeostatic regulation mechanism is shown. The state of the system is considered to fall into one of these categories: homeostatic, overwhelmed and understimulated. The objective is to keep the system in the homeostatic regime modifying its behavior toward this goal. In computer vision applications, for example, where the environment changes from a controlled conditions E to E ′ (Fig. 2) the performance of the system will be maximum for another set of camera parameters δ ′. So if the system does not own a mechanism to detect the new environment, its performance will drop since it will continue using the initial parameter set δ, and we must rely on an external agent to readjust the parameter set to δ ′. In our proposal, the homeostatic regulation is based on different hormones: some directly obtained from the images (h luminance, h whitebalance) and others from the ⋆ This work has been partially supported by the Spanish Min. of Educ. and FEDER funds