Artificial Endocrine Controller for Power Management in Robotic Systems

Abstract

The robots that operate autonomously for extended periods in remote environments are often limited to gather only small amounts of power through photovoltaic solar panels. Such limited power budgets make power management critical to the success of the robot's mission. Artificial endocrine controllers, inspired by the mammalian endocrine system, have shown potential as a method for managing competing demands, gradually switching between behaviors, synchronizing behavior with external events, and maintaining a stable internal state of the robot. This paper reports the results obtained using these methods to manage power in an autonomous sailing robot. Artificial neural networks are used for sail and rudder control, while an artificial endocrine controller modulates the magnitude of actuator movements in response to battery or sunlight levels. Experiments are performed both in simulation and using a real robot. In simulation a 13-fold reduction in median power consumption is achieved; in the robot this is reduced to a twofold reduction because of the limitations of the simulation model. Additional simulations of a long term mission demonstrate the controller's ability to make gradual behavioral transitions and to synchronize behaviors with diurnal and seasonal changes in sunlight levels.

Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis

“…As an important method in computer science, a rough set has been applied in the field of AI [65,66]. The latest research in AI methods is the artificial endocrine system (AES), which is introduced into the field of AI [6,[67][68][69]. It is foreseeable that AES will become an important method of AI in computer science and related domains.…”

Modeling of Dynamic Systems Using Orthogonal Endocrine Adaptive Neuro-Fuzzy Inference Systems

“…Idea is to mimic the process from the nature where the behavior of the neurons is regulated by hormones secreted by glands of endocrine system. In contrast to these papers [6][7][8] where hormones are usually used to modify weights of neurons in artificial neural networks, based on external conditions, idea of this paper is to introduce these stimuli directly to the neurons of fourth layer, via parameter d in Eq. (11), as reaction to the variations of nominal components of dynamical system under effect of changed working conditions, determined by sensors.…”

“…These networks mimic the biological process of regulating the behavior of the system through hormone secretion from the gland cell associated to the artificial neural network. This principle of adding hormones can be applied to all kinds of regular neural networks [7,8] as well as ANFIS, as in this paper. The advantage of the method presented in this paper is that the system after completion of the training has the ability to adapt to ever changing conditions in which the system operates, based on the information obtained from the external sensors.…”

Endocrine Composite Skyhook-Groundhook Control of Electromagnetic Linear Hybrid Active Suspension

“…When the vehicle system runs on an uneven road, it is inevitable to suffer from uncertainties such as sprung mass and tire stiffness, so the suspension controller should be adaptive to these complex and uncertain environment. In recent years, the endocrine control has been widely studied in various fields because of its excellent adaptive performance and selflearning ability [20][21][22]. In [23], an endocrine single-neuron PID compound sliding control system was designed; the output gain of whole order neuron PID was controlled by using a fuzzy controller.…”