Autonomous Exploration of Unknown 3D Environments Using a Frontier-Based Collector Strategy

Autonomous exploration using unmanned aerial vehicles (UAVs) is essential for various tasks such as building inspections, rescue operations, deliveries, and warehousing. However, there are two main limitations to previous approaches: they may not be able to provide a complete map of the environment and assume that the map built during exploration is accurate enough for safe navigation, which is usually not the case. To address these limitations, a novel exploration method is proposed that combines frontier-based exploration with a collector strategy that achieves global exploration and complete map creation. In each iteration, the collector strategy stores and validates frontiers detected during exploration and selects the next best frontier to navigate to. The collector strategy ensures global exploration by balancing the exploitation of a known map with the exploration of unknown areas. In addition, the online path replanning ensures safe navigation through the map created during motion. The performance of the proposed method is verified by exploring 3D simulation environments in comparison with the state-of-the-art methods. Finally, the proposed approach is validated in a real-world experiment.

A Search Strategy and Vessel Detection in Maritime Environment Using Fixed-Wing UAVs

In this paper, we address the problem of autonomous search and vessel detection in an unknown GNSS-denied maritime environment with fixed-wing UAVs. The main challenge in such environments with limited localization, communication range, and the total number of UAVs and sensors is to implement an appropriate search strategy so that a target vessel can be detected as soon as possible. Thus we present informed and non-informed methods used to search the environment. The informed method relies on an obtained probabilistic map, while the non-informed method navigates the UAVs along predefined paths computed with respect to the environment. The vessel detection method is trained on synthetic data collected in the simulator with data annotation tools. Comparative experiments in simulation have shown that our combination of sensors, search methods and a vessel detection algorithm leads to a successful search for the target vessel in such challenging environments.