Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Introduction <s> This paper surveys recent results in coverage path planning, a new path planning approach that determines a path for a robot to pass over all points in its free space. Unlike conventional point-to-point path planning, coverage path planning enables applications such as robotic de-mining, snow removal, lawn mowing, car-body painting, machine milling, etc. This paper will focus on coverage path planning algorithms for mobile robots constrained to operate in the plane. These algorithms can be classified as either heuristic or complete. It is our conjecture that most complete algorithms use an exact cellular decomposition, either explicitly or implicitly, to achieve coverage. Therefore, this paper organizes the coverage algorithms into four categories: heuristic, approximate, partial-approximate and exact cellular decompositions. The final section describes some provably complete multi-robot coverage algorithms. <s> BIB001 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Introduction <s> The combination of photogrammetric aerial and terrestrial recording methods can provide new opportunities for photogrammetric applications. A UAV (Unmanned Aerial Vehicle), in our case a helicopter system, can cover both the aerial and quasi-terrestrial image acquisition methods. A UAV can be equipped with an on-board high resolution camera and a priori knowledge of the operating area where to perform photogrammetric tasks. In this general scenario our paper proposes vision-based techniques for localizing a UAV. Only natural landmarks provided by a feature tracking algorithm will be considered, without the help of visual beacons or landmarks with known positions. The novel idea is to perform global localization, position tracking and localization failure recovery (kidnapping) based only on visual matching between current view and available georeferenced satellite images. The matching is based on SIFT features and the system estimates the position of the UAV and its altitude on the base of the reference image. The vision system replaces the GPS signal combining position information from visual odometry and georeferenced imagery. Georeferenced satellite or aerial images must be available on-board beforehand or downloaded during the flight. The growing availability of high resolution satellite images (e.g., provided by Google Earth or other local information sources) makes this topic very interesting and timely. Experiments with both synthetic (i.e., taken from satellites or datasets and pre elaborated) and real world images have been performed to test the accuracy and the robustness of our method. Results show sufficient performance if compared with common GPS systems and give a good performance also in the altitude estimation, even if in this last case there are only preliminary results. <s> BIB002 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Introduction <s> In this paper, we consider the structure inspection problem using a miniature unmanned aerial vehicle (UAV). The influence of the wind on the UAV behavior and onboard energy limitations are important parameters that must be taken into account in the structure inspection problem. To tackle these problems, we derive three methods to inspect a structure. First, we develop a Zermelo-Traveling Salesman Problem (TSP) method to compute the optimal route to inspect a simple virtual structure. Second, we derive a method that combines meshing techniques with the Zermelo-TSP method. In this approach, the inspection coordinates for the interest points are obtained automatically by means of a meshing algorithm, then, the Zermelo-TSP method is used to compute the time-optimal route to inspect all the interest points in minimal time. Finally, we derive a method for structure inspection based on the Zermelo-Vehicle Routing Problem (VRP). These methods have been validated in a simulated environment. <s> BIB003 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Introduction <s> We consider the problem of providing surveillance to a grid area using multiple heterogeneous UAVs, named sentinels and searchers, with complementary sensing and actuation capabilities. We consider probabilistic attacks and we analyze the expected performance with respect to the team deployment. We then introduce the problem of finding minmax deployments that result in the most desirable worst case performance caused by an attack. We present an algorithm to compute deployments while trading off solution's quality and computational effort and we qualitatively and quantitatively analyze it. <s> BIB004 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Introduction <s> We study the problem of monitoring the evolution of atmospheric variables within low-altitude cumulus clouds with a fleet of Unmanned Aerial Vehicles (UAVs). To tackle this challenge, two main problems can be identified: i) creating on-line maps of the relevant variables, based on sparse local measurements; ii) designing a planning algorithm which exploits the obtained map to generate trajectories that optimize the adaptive data sampling process, minimizing the uncertainty in the map, while steering the vehicles within the air flows to generate energetic-efficient flights. Our approach is based on Gaussian Processes (GP) for the mapping, combined with a stochastic optimization scheme for the trajectories generation. The system is tested in simulations carried out using a realistic three-dimensional current field. Results for a single UAV as well as for a fleet of multiple UAVs, sharing information to cooperatively achieve the mission, are provided. <s> BIB005 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Introduction <s> Unmanned aerial vehicles (UAVs) and other robots in smart farming applications offer the potential to monitor farm land on a per-plant basis, which in turn can reduce the amount of herbicides and pesticides that must be applied. A central information for the farmer as well as for autonomous agriculture robots is the knowledge about the type and distribution of the weeds in the field. In this regard, UAVs offer excellent survey capabilities at low cost. In this paper, we address the problem of detecting value crops such as sugar beets as well as typical weeds using a camera installed on a light-weight UAV. We propose a system that performs vegetation detection, plant-tailored feature extraction, and classification to obtain an estimate of the distribution of crops and weeds in the field. We implemented and evaluated our system using UAVs on two farms, one in Germany and one in Switzerland and demonstrate that our approach allows for analyzing the field and classifying individual plants. <s> BIB006 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Introduction <s> In this paper, we design and build a power line inspection robot capable of hybrid operation modes. Specifically, the developed robot is able to land on the overhead ground wire (OGW) and to move as the climbing robot. When to negotiate obstacles, it can vertically take off the wire and fly over the obstacles as the unmanned aerial vehicle (UAV). A customized trumpet-shaped undercarriage is used to guarantee that the robot can land and move safely. With the aid of a swingable 2D Laser Range Finder (LRF), the robot can not only determine whether there are obstacles but also detect the position and orientation of the OGW, making it suitable for automatic inspection of power lines. The outdoor experimental results1 demonstrate the effectiveness of the robot in landing and obstacle negotiation. In addition, the average power consumption of the robot is much lower than that of traditional flying robots for power line inspection. <s> BIB007
Unmanned Aerial Vehicles (UAVs) have increasingly been used in a wide range of applications, such as surveillance BIB004 , smart farming BIB006 , photogrammetry BIB002 , disaster management, civil security  , wildfire tracking  , cloud monitoring BIB005 , structure supervision BIB003 , and power line inspection BIB007 . The UAVs consist of aerial platforms with no pilots on-board the vehicle. Such platforms are remotely and manually operated by a human, but they also perform automated pre-programmed flights. Autonomous flights can be executed using intelligent systems integrated with on-board sensors. The Coverage Path Planning (CPP) problem is classified as a motion planning subtopic in robotics, where it is necessary to build a path for a robot to explore every location in a given scenario BIB001 . Despite the technological progress in this type of aerial platform regarding autonomous flight, it is important to emphasize that the phases concerning take-off, mission execution, and landing are usually assisted by two people for each UAV due to safety measures. The pilot supervises the mission and may change the flight mode to manual in case of a failure or an emergency during the flight, while the base operator monitors the navigation data during the mission execution, such as altitude variation and battery discharge  .