Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> UAV Classification <s> To date, a large number of algorithms to solve the problem of autonomous exploration and mapping has been presented. However, few efforts have been made to compare these techniques. In this paper, an extensive study of the most important methods for autonomous exploration and mapping of unknown environments is presented. Furthermore, a representative subset of these techniques has been chosen to be analysed. This subset contains methods that differ in the level of multi-robot coordination and in the grade of integration with the simultaneous localization and mapping (SLAM) algorithm. These exploration techniques were tested in simulation and compared using different criteria as exploration time or map quality. The results of this analysis are shown in this paper. The weaknesses and strengths of each strategy have been stated and the most appropriate algorithm for each application has been determined. <s> BIB001 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> UAV Classification <s> Coverage Path Planning (CPP) is the task of determining a path that passes over all points of an area or volume of interest while avoiding obstacles. This task is integral to many robotic applications, such as vacuum cleaning robots, painter robots, autonomous underwater vehicles creating image mosaics, demining robots, lawn mowers, automated harvesters, window cleaners and inspection of complex structures, just to name a few. A considerable body of research has addressed the CPP problem. However, no updated surveys on CPP reflecting recent advances in the field have been presented in the past ten years. In this paper, we present a review of the most successful CPP methods, focusing on the achievements made in the past decade. Furthermore, we discuss reported field applications of the described CPP methods. This work aims to become a starting point for researchers who are initiating their endeavors in CPP. Likewise, this work aims to present a comprehensive review of the recent breakthroughs in the field, providing links to the most interesting and successful works. <s> BIB002 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> UAV Classification <s> Unmanned Aircraft Systems(UAS) have seen unprecedented levels of growth during the last two decades. Although many challenges still exist, one of the main UAS focus research areas is in navigation and control. This paper provides a comprehensive overview of helicopter navigation and control, focusing specifically on small-scale traditional main/tail rotor configuration helicopters. Unique to this paper, is the emphasis placed on navigation/control methods, modeling techniques, loop architectures and structures, and implementations. A ‘reference template’ is presented and used to provide a basis for comparative studies and determine the capabilities and limitations of algorithms for unmanned/autonomous flight, as well as for navigation, and control. A detailed listing of related research is provided, which includes model structure, helicopter platform, control method and loop architecture, flight maneuvers and results for each. The results of this study was driven by and has led to the development of a ‘one-fits-all’ comprehensive and modular navigation controller and timing architecture applicable to any rotorcraft platform. <s> BIB003
The UAVs can be classified into two main top-level configurations, fixed-wing and rotary-wing. Both types present specific advantages and challenges considering the control and guidance system BIB003 . using multiple robots and reducing the number of turning maneuvers. Finally, the author highlights the available environment information. Several approaches admit previous knowledge of the robot regarding the search area (offline), while sensor-based approaches acquire such information in real-time during the coverage (online). The most recent survey regarding CPP presents several approaches and techniques to perform mostly missions with land vehicles BIB002 . Considering exploration under uncertainty, Juliá et al. BIB001 present a study about unknown environment mapping strategies. The robots are supposed to autonomously explore the environment to collect data and build a navigation map. With the absence of global positioning information, it is necessary to constantly correct the robot positioning and orientation estimation using simultaneous localization and mapping techniques. Multiple robots can be employed to either reduce exploration time or improve map quality, but require coordination strategies. In such strategies, the robots may share perceptions and construct a common map of the workspace. This global map may be built either as a centralized or distributed way.