A Novel Hierarchical Adaptive Controller with Dynamic Memory for an Autonomous Robot

This work concerns practical issues surrounding the application of learning and memory in a robot toward optimal navigation in dynamic environments. A novel hierarchical adaptive controller that contains two-level units was developed and trained in a physical mobile robot. In the low-level unit, the robot holds N numbers of biologically inspired Aplysia-like spiking neural networks that have the property of spike timedependent plasticity. Each of these networks is trained to become an expert in a particular local environment(s). All the trained networks are stored in a tree-type memory structure in the highlevel unit. These stored networks are used as experiences for the robot to enhance its navigation ability in the upcoming environments. Forgetting and dynamic clustering techniques are also developed to control the memory size. Experimental results show that the proposed model can produce a robot with learning and memorizing capabilities that enable it to survive in highly dynamic environments.