Cooperative Aerial Vehicles for Non-Uniform Mapping of 3D Unknown Environments – AVENUE

The objective of AVENUE is to propose new efficient solutions to generate online trajectories for a team of cooperating aerial vehicles to achieve a full exploration and non-uniform mapping of an unknown 3D environment. Although the autonomous exploration of an unknown environment is a widely studied problem, most of the existing solutions are limited to 2D environments, while coping with complex 3D structures explored with aerial vehicles is still an open challenge from both the theoretical and the experimental point of view. First of all, the complexity of the environment requires new solutions to efficiently represent and clusterize the gathered data. Then, to solve the path planning problem, a multi-criteria optimization problem is formulated, defining new metrics for the prediction of the information gain obtained from future viewpoints. Ideally, this optimization problem will be solved in a distributed way to have more robust solutions with respect to data loss and robots failures. The results of the optimization process is the final generation of efficient trajectories leading to a complete exploration and the reconstruction of a 3D map. Furthermore, an additional goal is to go beyond the classic mapping problem by introducing different levels of required resolution in the sensed environments depending on the relevance of a particular zone. In such a context, there could be an interest to find and map these particular areas faster than the rest of the environment and the problem can be formulated as a simultaneous search and exploration, leading to new planning problems studied only marginally in the literature. Thus, the overall goal of AVENUE is to overcome the limitations and simplified assumptions of the current literature, to deal with the challenges that real environments and real platforms present, and finally try to bridge the gap between theory and field experiments in the exploration and mapping field.