Probabilistic Navigation in Partially Observable Environments

Autonomous mobile robots need very reliable navigation capabilities in order to operate unattended for long periods of time. We have developed an approach that uses partially observable Markov models to robustly track a robot’s location and integrates it with a planning and execution monitoring approach that uses this information to control the robot’s actions. The approach explicitly maintains a probability distributionover the possible locations of the robot, taking into account various sources of uncertainty, including approximate knowledge of the environment, actuator uncertainty, and sensor noise. A novel feature of our approach is its integration of topological map information with approximate metric information. We demonstrate the reliability of this approach, especially its ability to smoothly recover from errors in sensing.