Artificial Hormone Network for Adaptable Robots

With current robotic technologies, it generally remains unreliable to use fully autonomous robots in high-risk robotic applications such as search and rescue, surveillance or exploration in disaster scenarios. One of the main issues comes from the fact that unstructured real-world environments are dynamic and full of interventions. Therefore, for autonomous robots to operate in such environments, the ability to adapt to both internal and external environmental changes is crucial. Being unable to deal with such changes not only could downgrade the performance of the robots but also potentially cause devastating consequences in risky environments. Looking towards nature, it can be observed that biological organisms can cope well with the dynamic unpredictability of real-world environments. One of the key properties which assist biological organisms is the ability to adapt to changing environments by the utilization of hormones in response to environmental cues. This biological feature provides an inspiration for this research which investigates a novel Artificial Hormone Network architecture in providing adaptability for autonomous robots to deal with both internal and external environmental changes in simulations of unstructured real-world environments. The Artificial Hormone Network architecture proposes a new method which allows constructions and interactions of several hormones in order to provide adaptability for autonomous robots in different application scenarios. Two Artificial Hormone Networks (AHN1 and AHN2) are proposed and investigated in this research. Results from experiments correspondingly report better performance in dealing with considered internal and external environmental changes on a robot implemented with the Artificial Hormone Networks than a robot implemented without them. Another important aspect of the Artificial Hormone Network architecture is the ability to be constructed automatically to provide particular adaptability using Cartesian Genetic Programming. Experiment results show that the construction of Artificial Hormone Networks can be evolved and that this evolved system not only performed to a level of adaptability that was acceptable but actually performed better than the “hand-coded” system.