Mobile Robot Local Navigation with a Polar Neural Map
The application of (artificial) neural maps on mobile robot navigation is investigated. Neural maps are dynamic representations with computational capabilities, inspired by the cortical maps of the brain. The methodology is presented in detail, based on previous theoretical works, and simulation results demonstrate the applicability of the method on basic global (map-based) navigation problems. However, the real power of the method is revealed in local (sensory-based) navigation for a class of mobile robots equipped with range-finding sensors on their periphery. A complete local navigation scheme for a real Nomad 200 robot has been proposed, implemented and tested in real-world indoor environments. It is based on a topographic neural map and uses ultrasonic sensor (sonar) information. The map is polar and egocentric attached to the center of the robot and represents the robot’s environment in a local (spatial and temporal) sense. Paths are computed incrementally and a separate motion controller determines the control input (velocity level) to drive the robot, respecting its kinematic and dynamic constraints. The system is enhanced with a sensor short-term memory and temporal adaptation methods that compensate for potential uncertainties. The system was tested thoroughly and detailed analytical and experimental results are presented. The potential applications and usability of the system reveal how it can be used further for global navigation, exploration and cognitive mapping.