Long-Term Exploration in Unknown Dynamic Environments

Abstract: In order for autonomous robots to perform tasks and safely navigate environments they need to have a reliable and detailed map. These maps are generally created by the robot itself since maps with the required level of detail rarely exist beforehand. In order to create that map the robot has to explore an unknown environment. Such activity is referred to as autonomous exploration within the field of robotics. Most research done in autonomous exploration assumes a static environment. Since most environments in the real world often changes over time an exploration algorithm that is able to re-explore areas where changes may occur is of interest for autonomous long term missions. This thesis presents a method to predict where changes may occur in the environment using Markov chains and an occupancy grid map. An exploration algorithm is also developed with the aim of keeping an updated map of a changing environment. The exploration algorithm is based on a static exploration algorithm that uses RRT? to sample poses and evaluates these poses based on the length of the path to get there and the information gain at and on the path to the sampled pose. An evaluation of both the mapping and exploration is made respectively. The mapping is evaluated on its ability of suppressing noisy measurements whilst being able to accurately model the dynamics of the map. The exploration algorithm is evaluated in three different environments of increasing complexity. Its ability to seek out areas susceptible of change whilst providing data for the mapping is evaluated in each environment. The results show both a mapping and exploration algorithm who works well but are noise sensitive.