NaviGates: A Benchmark for Indoor Navigation

Reliable indoor navigation has been a goal of mobile robotics research in recent years. Probabilistic belief update, occupancy grid methods, and multi-tiered hybrid architectures have all been implemented in the pursuit of autonomous navigation systems that can function in the face of danger and incomplete information. The conventional approach applies AI techniques to the localization problem in order to attain reasonable navigation reliability. In this paper, we describe a simple navigation system that is neither multi-tiered nor AI-based. The system has been implemented on a Nomad 150 mobile robot that has demonstrated statistically significant navigation reliability during working hours in several buildings in the San Francisco Bay Area. In addition to providing navigation benchmarks based on long-term empirical tests, NaviGates demonstrates implementations of robot skills that are essential to robot autonomy: robust dynamic obstacle avoidance, path replanning in case of obstruction, automatic avoidance of dangerous regions such as staircases, and automatic human-guided mapping for a new building, allowing the robot to be introduced to a new office building without the need for time consuming manual mapping. Simplicity itself is an important thesis of this research, as a robot with an extremely simple control algoritm is shown to perform admirably in a variety of environments.