Active slam with informative path planning for heterogeneous robot teams

Abstract

Recently, heterogeneous teams consisting of unmanned ground vehicles and unmanned aerial vehicles are being used for different types of missions such as surveillance, tracking, and exploration, etc. Exploration missions with heterogeneous robot teams should acquire a common map for understanding the surroundings better. The unique approach presented in this dissertation with cooperative use of agents provides a well-detailed observation over the environment where challenging details and complex structures are involved. Also, the presented method is suitable for real-time applications and autonomous path planning for exploration. Lidar Odometry and Mapping with various similarity metrics such as Shannon Entropy, Kullback-Liebler Divergence, Jeffrey Divergence, K Divergence, Topsoe Divergence, Jensen-Shannon Divergence and Jensen Divergence are used to construct a common height map of the environment. Furthermore, the given layering method that provides more accuracy and a better understanding of the common map. All of the given similarity metrics are compared, and the advantage of utilizing the layering method is shown. The best similarity metric for constructing a heterogeneous robot team common map of the experimental area was obtained by using the Jensen Divergence similarity metric and layering method. Moreover, Extended Kalman Filter localization and OctoMap techniques are utilized to create an adaptive simultaneous localization and mapping infrastructure for informative path planning. Optimal parameter tuning for the specified simulation environment provides adjustable memory allocation and exploration performance, such as; duration, collected information and effort. The information seeking controller obtained with the use of relative entropy ensures exploration of the given area to minimize the uncertainty between observed states and environmental states. Robots move to the volumetric spaces' center under given rules and collect measurements by proprioceptive and exteroceptive sensors. With the use of heterogeneous robot teams, the measurements collected by the Lidar provide an advantage in perceiving complex details that can not be done by homogeneous robot teams. Constructing common map part of the theoretical approaches in this thesis are experimentally validated. In addition, the complete demonstration of this dissertation is done with six different cases by simulation studies. The theoretical background of active simultaneous localization and mapping with informative path planning for heterogeneous robot teams are validated, and the advantages of this study are remarked.