Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> This paper focuses on the problem of cooperatively searching a given area to detect objects of interest, using a team of heterogenous unmanned air vehicles (UAVs). The paper presents algorithms to divide the whole area taking into account UAV’s relative capabilities and initial locations. Resulting areas are assigned among the UAVs, who could cover them using a zigzag pattern. Each UAV has to compute the sweep direction which minimizes the number of turns needed along a zigzag pattern. Algorithms are developed considering their computational complexity in order to allow near-real time operation. Results demonstrating the feasibility of the cooperative search in a scenario of the COMETS multi-UAV project are presented. <s> BIB001 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> We design and analyze the performance of cooperative search strategies for unmanned aerial vehicles (UAVs) searching for moving, possibly evading, targets in a hazardous environment. Rather than engaging in independent sensing missions, the sensing agents (UAVs with sensors) "work together" by arranging themselves into a flight configuration that optimizes their integrated sensing capability. If a UAV is shot down by enemy fire, the team adapts by reconfiguring its topology to optimally continue the mission with the surviving assets. We presetn a cooperative search methodology that integrates the multiple agents into an advantageous formation that distinctively enhances the sensing and detection operations of the system while minimizing the transmission of excessive control information for adaptation of the team's topology. After analyzing our strategy to determine the performance tradeoff between search time and number of UAVs employed, we present an algorithm that selects the minimum number of UAVs to deploy in order to meet a targeted search time within probabilistic guarantees. <s> BIB002 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> Unmanned aerial vehicles (UAVs) are remote controlled or autonomous air vehicles. An UAV can be equipped with various types of sensors to perform life rescue missions or it can be armed with weapons to carry out stealthy attack missions. With the unmanned nature of UAVs, a mission can be taken in any hostile environment without risking the life of pilots. Among life rescue missions, the common objective is often defined as maximizing the total coverage area of the UAVs with the limited resources. When the number of UAVs increases, coordination among these UAVs becomes very complicated even for experienced pilots. In this paper, a cooperative path planner for UAVs is proposed. The path of each UAV is represented by a B-spline curve with a number of control points. The positions of these control points are optimized using an ant colony optimization algorithm (ACO) such that the total coverage of the UAVs is maximized. <s> BIB003 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> This paper proposes an improved method of exact cellular decomposition to plan the coverage path in polygon area. Firstly, the problem of Coverage Path Planning (CPP) in the convex polygon area is transformed to the width calculation of the convex polygon, and the strategy of flying along the vertical direction of width is presented to reach the least number of turns. Secondly, a convex decomposition algorithm of minimum width sum based on the greedy recursive method is presented to decompose the concave area into some convex subregions. Finally, a subregion connection algorithm based on minimum traversal of undirected graph is proposed to connect the coverage paths of the subregions. The simulation shows that the proposed method is feasible and effective. <s> BIB004 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> This paper presents an algorithm for planning waypoints for the operation of a surveillance mission using cooperative unmanned aerial vehicles (UAVs) in a given map. This algorithm is rather simple and intuitive; therefore, this algorithm is easily applied to actual scenarios as well as easily handled by operators. It is assumed that UAVs do not possess complete information about targets; therefore, kinematics, intelligence, and so forth of the targets are not considered when the algorithm is in operation. This assumption is reasonable since the algorithm is solely focused on a surveillance mission. Various parameters are introduced to make the algorithm flexible and adjustable. They are related to various cost functions, which is the main idea of this algorithm. These cost functions consist of certainty of map, waypoints of co-worker UAVs, their own current positions, and a level of interest. Each cost function is formed by simple and intuitive equations, and features are handled using the aforementioned parameters. <s> BIB005 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> Abstract In this paper, an enhanced exact cellular decomposition method to plan the coverage path of UAVs in a polygon area is proposed. To be more specific, the contributions of the paper are: firstly, the turning motion of UAVs is shown to be less efficient from the viewpoints of route length, duration and energy. Secondly, the problem of coverage Path Planning (CPP) in a convex polygon area is transformed to width calculation of the convex polygon, and a novel algorithm to calculate the widths of convex polygons with time complexity of O ( n ) is developed. The path of the least number of turns for an UAV based on the widths of convex polygons is devised. Thirdly, a convex decomposition algorithm for minimum width sum based on the greedy recursive method which revolves around decomposing the concave area into convex subregions is developed. It is proved that the algorithm is a polynomial time algorithm. To avoid unnecessary back and forth motion, some entirely adjacent subregions are combined. Finally, comparing different weights of two joint-points, a subregion connection algorithm based on minimum traversal of weighted undirected graph is proposed to connect the coverage paths of the subregions. Simulation results show that the proposed method is feasible and effective. <s> BIB006 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> We present the adaptation of an optimal terrain coverage algorithm for the aerial robotics domain. The general strategy involves computing a trajectory through a known environment with obstacles that ensures complete coverage of the terrain while minimizing path repetition. We introduce a system that applies and extends this generic algorithm to achieve automated terrain coverage using an aerial vehicle. Extensive experimental results in simulation validate the presented system, along with data from over 100 kilometers of successful coverage flights using a fixed-wing aircraft. <s> BIB007 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> Aerial images are useful tools for farmers who practise precision agriculture. The difficulty in taking geo-referenced high-resolution aerial images in a narrow time window considering weather restrictions and the high cost of commercial services are the main drawbacks of these techniques. In this paper, a useful tool to obtain aerial images by using low cost unmanned aerial vehicles (UAV) is presented. The proposed system allows farmers to easily define and execute an aerial image coverage mission by using geographic information system tools in order to obtain mosaics made of high-resolution images. The system computes a complete path for the UAV by taking into account the on-board camera features once the image requirements and area to be covered are defined. This work introduces a full four-step procedure: mission definition, automatic path planning, mission execution and mosaic generation. <s> BIB008 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> Multiple UAVs can be used to cover a region effectively. Area coverage involves two stages - area decomposition into cells and path planning inside the cells. The area is decomposed using sweeping technique. For path planning inside the cells, a novel method is developed where optimal number of lanes are generated to minimize the number of UAV turns to accomplish the mission in minimum time. Also to allow operator interaction and precedence in coverage, a novel lawnmower/Zamboni planner is designed. Simulation results are presented for multiple UAVs are presented and flight test results for one UAV performing a persistent mission is presented. <s> BIB009 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> This paper proposes a distributed approach to solve long duration area surveillance missions with a team of aerial robots, taking into account communication constraints. The system, based on “one-to-one” coordination, minimizes the probability that any event happens in the area without being detected and ensures periodic communication between UAVs. A set of simulations are presented to validate the applicability of the approach and its most relevant features: convergence, robustness with dynamic teams, fault-tolerance and finite information sharing time. It is also shown that “one-to-one” coordination for all the pairs of neighbors allows to obtain an efficient coordination scheme for the whole team to accomplish the area surveillance mission without any central unit. <s> BIB010 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> Using aerial robots in area coverage applications is an emerging topic. These applications need a coverage path planning algorithm and a coordinated patrolling plan. This paper proposes a distribut... <s> BIB011 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> A complete procedure that can be used with any commercial quadrotor has been designed.A novel technique denoted as Harmony Search has been applied to path planning.A vineyard parcel has been used as a case study.An improved approach has been obtained in comparison with former techniques. The coverage path planning (CPP) problem belongs to a sub-field of motion planning where the goal is to compute a complete coverage trajectory from initial to final position, within the robot workspace subjected to a set of restrictions. This problem has a complexity NP-complete, and has no general solution. Moreover, there are very few studies addressing this problem applied to aerial vehicles. Previous studies point out that the variable of interest to be optimized is the number of turns. Thus, by minimizing the number of turns, it can be ensured that the mission time is likewise minimized. In this paper, an approach to optimize this cost variable is proposed. This approach uses a quite novel algorithm called Harmony Search (HS). HS is a meta-heuristic algorithm based on jazz musician's improvisation through a pleasant harmony. Finally, the results achieved with this technique are compared with the results obtained with the previous approach found in the literature. <s> BIB012 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> In this article, work in progress on a system for rapid aerial mapping is presented. We believe that a tool able to quickly generate an up-to-date high resolution aerial view, e.g. shortly after a natural disaster or a big incident occurs, can be a highly valuable asset to help first responders in the decision making. The presented work focuses on the path planning capabilities of the system, together with the area partitioning and workload distribution among a team of multi-rotor unmanned aircraft. Sensor footprint and range of the involved aircraft may differ. The presented approach is based on an approximate cellular decomposition of the area of interest. The results of this work will be integrated into an existing system which already provides a mobile ground control station able to supervise and control multiple sensor carriers. <s> BIB013 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> This paper presents a decentralized algorithm for area partition in surveillance missions that ensures information propagation among all the robots in the team. The robots have short communication ranges compared to the size of the area to be covered, so a distributed one-to-one coordination schema has been adopted. The goal of the team is to minimize the elapsed time between two consecutive observations of any point in the area. A grid-shape area partition strategy has been designed to guarantee that the information gathered by any robot is shared among all the members of the team. The whole proposed decentralized strategy has been simulated in an urban scenario to confirm that fulfils all the goals and requirements and has been also compared to other strategies. <s> BIB014 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> Area coverage is one of the compelling applications of UAVs. The existing methods for coverage path planning assume a uniformly interesting target area. However in many real world applications items of interest are not uniformly distributed but form clusters. Here it can be advantageous to only sample regions of interest while skipping uninteresting sections of the environment. In this paper, we present a coverage tree structure that can accommodate non-uniform coverage of regions in the target area. Three strategies are proposed to traverse the coverage tree. Experiments indicate that in some situations our method can cover the interesting regions with about half the travel time / cost of a naive regular ‘lawnmower’ coverage pattern. <s> BIB015 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> This paper represents biologically inspired control strategy for a group of robots. A descripted method was developed to solve exploration and monitoring tasks. The main goal of our work was to develop a simple and powerful algorithm which will accept dynamical changes in the number of robot swarm in line with its mission. This algorithm is suitable for situations where unpredictable conditions may lead to failures in multi-robotics system and the completion of the mission is endangered. In this article we would like to focus on the investigation of the actual characteristics of a simple pheromone based algorithm in a situation where there is a sudden failure in communication. <s> BIB016 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> This paper presents control strategy for a group of multi-rotor unmanned aerial vehicles (UAV). Descripted biologically inspired method was developed to solve exploration or monitoring tasks. The proposed solution was tested in real environment on a quad-rotor UAV platform. Our abstract model is a decentralized adaptive system with a shared memory which represents the environment. <s> BIB017 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> In this paper, we propose strategies for merging occupancy probabilities of target existence in multi-UAV co- operative search. The objective is to determine the impact of cooperation and type of information exchange on search time and detection errors. To this end, we assume that small-scale UAVs (e.g., quadrotors) with communication range limitations move in a given search region following pre-defined paths to locate a single stationary target. Local occupancy grids are used to represent target existence, to update its belief with local observations and to merge information from other UAVs. Our merging strategies perform Bayes updates of the occupancy probabilities while considering realistic limitations in sensing, communication and UAV movement—all of which are important for small-scale UAVs. Our simulation results show that information merging achieves a reduction in mission time from 27% to 70% as the number of UAVs grows from 2 to 5. <s> BIB018 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> We propose a novel method for non-uniform terrain coverage using Unmanned aerial vehicles (UAVs). The existing methods for coverage path planning consider a uniformly interesting target area and hence all the regions are covered with high resolution. However in many real world applications items of interest are not uniformly distributed but form patches of locally high interest. Therefore, with sparse sampling of uninteresting sections of the environment and high resolution sampling of interesting patches, the trajectory of the robot can become shorter. In this paper, we present a novel coverage strategy based on Space-Filling Curves that can accomplish non-uniform coverage of regions in the target area. Simulations and real robot experiments indicate that with the new strategy, travel time / cost of the task can be (almost) always less than a regular ‘lawnmower’ coverage pattern. <s> BIB019 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> Unmanned Aerial Vehicles (UAVs) represent an appealing technology that open new possibilities in civil use. Despite the promising convenience, UAVs face several major setbacks which include limited energy supply and short operation time. With focus on a specific application, localization of mobile devices using wireless communication, this work analyses those aspects of the localization process which cause energy and time sinks. Furthermore, by using the concept of Bezier curves, energy-aware trajectories are developed by generating path smoothing algorithms called Exterior Modification of Trajectory Algorithm (E-MoTA) and Interior Modification of Trajectory Algorithm (I-MoTA) which are applied to the static trajectories. The observation of the impact of these algorithms on the energy and time expenditures at various resolution and on the level of localization accuracy is analyzed. Several state-of-the-art trajectories are compared with E-MoTA and I-MoTA using analytical studies. The results show that applying MoTA aids in reducing energy and time expenditures while maintaining the level of the localization accuracy close to the original one. <s> BIB020 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> Unmanned Aerial Vehicles (UAVs) are starting to be used for photogrammetric sensing of large areas in several application domains, such as agriculture, rescuing, and surveillance. In this context, the problem of finding a path that covers the entire area of interest is known as Coverage Path Planning (CPP). Although this problem has been addressed by several authors from a geometrical point of view, other issues such as energy, speed, acceleration, and image resolution are not often taken into account. To fill this gap, this paper first proposes an energy model derived from real measurements, and then uses this model to implement a coverage path planning algorithm for reducing energy consumption, as well as guaranteeing a desired image resolution. In addition, two safety mechanisms are presented: the first, executed off-line, checks whether the energy stored in the battery is sufficient to perform the planned path; the second, performed online, triggers a safe return-to-launch (RTL) operation when the actual available energy is equal to the energy required by the UAV to go back to the starting point. <s> BIB021 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> Focus is on the coverage path planning problem with UAV for 3D terrain reconstruction.The aim is to obtain a path that reduces the battery consumption, minimizing the turns.Our algorithm deals with both convex and non-convex regionsThe algorithm can perform the coverage when complex regions are considered.Can achieve better solutions than a previous result (using less turns). Three-dimensional terrain reconstruction from 2D aerial images is a problem of utmost importance due its wide level of applications. It is relevant in the context of intelligent systems for disaster managements (for example to analyze a flooded area), soil analysis, earthquake crisis, civil engineering, urban planning, surveillance and defense research.It is a two level problem, being the former the acquisition of the aerial images and the later, the 3D reconstruction. We focus here in the first problem, known as coverage path planning, and we consider the case where the camera is mounted on an unmanned aerial vehicle (UAV).In contrast with the case when ground vehicles are used, coverage path planning for a UAV is a lesser studied problem. As the areas to cover become complex, there is a clear need for algorithms that will provide good enough solutions in affordable times, while taking into account certain specificities of the problem at hand. Our algorithm can deal with both convex and non-convex areas and their main aim is to obtain a path that reduces the battery consumption, through minimizing the number of turns.We comment on line sweep calculation and propose improvements for the path generation and the polygon decomposition problems such as coverage alternatives and the interrupted path concept. Illustrative examples show the potential of our algorithm in two senses: ability to perform the coverage when complex regions are considered, and achievement of better solution than a published result (in terms of the number of turns used). <s> BIB022 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> Several approaches can be identified in the literature for area decomposition: Grid based methods, Cellular decomposition and Boustrophedon (or Morse) decomposition. This paper proposes a novel discretization method for the area based on computational geometry approaches. By using a Constrained Delaunay Triangulation, a complex area is segmented in cells which have the size of the projected field of view of the RPAS (UAS) on-board sensor. In addition, costs are associated to each of these cells for both partitioning the area among several RPAS and full covering each sub-area. Simulation results show that this approach manages to optimally decompose and partition a complex area, and also produces a graph that can be used for different cost-based path planning methods. <s> BIB023 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> In this paper, an offline flight planner that computes an efficient coverage trajectory for a quad-rotors UAV is presented. The planner consists of three steps: mission definition, automatic path planning and trajectory generation. The proposed planner, as a useful tool, allows an UAV operator to easily define and generate a coverage trajectory for any specific task. The resultant trajectory can be dispatched to a quad-rotors with trajectory tracking controller for the missions that require a complete area coverage. <s> BIB024 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> In this paper, we present an algorithm called Learning-based Preferential Surveillance Algorithm (LPSA), which is specific to the problem of persistent surveillance by unmanned aerial vehicles. The novelty of the algorithm lies in providing the capability to a UAV to increase the probability of target detection through learning. The algorithm considers the neighborhood of a localized target as potential additional targets and motivates the UAV to verify if this is indeed true. Once a target is located in any of the neighborhood grid points, that point is visited more often than other points. Moreover, this technique uses the risk calculation method of an existing geometric reinforcement learning algorithm to reduce the frequency of visits to risky regions. Simulation results are used to demonstrate the effectiveness of the algorithm. <s> BIB025 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> Unmanned Aerial Vehicles (UAVs) are steadily replacing conventional manned aerial vehicles in multitude of tasks. Many of those tasks can be performed more efficiently by using a coordination between a team of UAVs rather than a single UAV. In this paper problem of cooperative area coverage using team of UAVs is addressed. Digital pheromones are used to convey information about search area. UAVs communicate indirectly by depositing and sensing digital pheromones which exist in pheromone map. Two approaches for path planning using pheromone maps are proposed. First approach is based on genetic algorithm, whereas second one relies on roulette wheel selection. Both approaches are evaluated in terms of distance required to cover given percentage of the area. <s> BIB026 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> This paper presents an optimized method to employ a multirotor for collecting images to generate a 3D map of an area of interest with attention to man-made structures in the area. A genetic algorithm is combined with space partitioning methods to find an optimized path to ensure complete coverage of a desired area with necessary overlap for generating a 3D map. This path provides for the collection of detailed information of man-made structures within the area and also consider areas of avoidance and adjust for problematic terrain (such as tall vegetation). The result is a flight path for a multirotor that includes waypoint, altitude, heading, and camera angle for all points along the flight path. This allows for the collection of all necessary images to produce a 3D map of the area with high resolution models of the buildings within the area. <s> BIB027 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> Unmanned Aerial Vehicles (UAVs) applications have seen an important increase in the last decade for both military and civilian applications ranging from fire and high seas rescue to military surveillance and target detection. While this technology is now mature for a single UAV, new methods are needed to operate UAVs in swarms, also referred to as fleets. This work focuses on the mobility management of one single autonomous swarm of UAVs which mission is to cover a given area in order to collect information. Several constraints are applied to the swarm to solve this problem due to the military context. First, the UAVs mobility must be as unpredictable as possible to prevent any UAV tracking. However the Ground Control Station (GCS) operator(s) still needs to be able to forecast the UAVs paths. Finally, the UAVs are autonomous in order to guarantee the mission continuity in a hostile environment and the method must be distributed to ensure fault-tolerance of the system. To solve this problem, we introduce the Chaotic Ant Colony Optimization to Coverage (CACOC) algorithm that combines an Ant Colony Optimization approach (ACO) with a chaotic dynamical system. CACOC permits to obtain a deterministic but unpredictable system. Its performance is compared to other state-of-the art models from the literature using several coverage-related metrics, i.e. coverage rate, recent coverage and fairness. Numerical results obtained by simulation underline the performance of our CACOC method: a deterministic method with unpredictable UAV trajectories that still ensures a high area coverage. <s> BIB028 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> The paper presents a novel algorithmic approach that allows to tackle in a common framework the problems of area decomposition, partition and coverage for multiple heterogeneous Unmanned Aircraft Systems (UAS). The approach combines computational geometry techniques and graph search algorithms in a multi-UAS context. Even though literature provides several strategies for area decomposition like grid overlay decomposition or exact cellular methods, some fail to either successfully decompose complex areas, or the associated path generation strategies are not feasible. The proposed approach manages to perform an exact cellular decomposition of non-convex polygonal coastal areas and includes an attributes based schema for area partitioning. In particular, the proposed solution uses a Constrained Delaunay Triangulation (CDT) for computing a configuration space of a complex area containing obstacles. The cell size of each produced triangle is constrained to the maximum projected Field-of-View (FoV) of the sensor on-board each UAS. In addition, the resulting mesh is considered as an undirected graph, where each vertex has several attributes used for area partitioning and coverage in a multi-UAS context. Simulation results show how the algorithms can compute sound solutions in real complex coastal regions. <s> BIB029 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> This paper tackles the problems of exact cell decomposition and partitioning of a coastal region for a team of heterogeneous Unmanned Aerial Vehicles (UAVs) with an approach that takes into account the field of view or sensing radius of the sensors on-board. An initial sensor-based exact cell decomposition of the area aids in the partitioning process, which is performed in two steps. In the first step, a growing regions algorithm performs an isotropic partitioning of the area based on the initial locations of the UAVs and their relative capabilities. Then, two novel algorithms are applied to compute an adjustment of this partitioning process, in order to solve deadlock situations that generate non-allocated regions and sub-areas above or below the relative capabilities of the UAVs. Finally, realistic simulations have been conducted for the evaluation of the proposed solution, and the obtained results show that these algorithms can compute valid and sound solutions in complex coastal region scenarios under different setups for the UAVs. <s> BIB030 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> This paper addresses area coverage in complex non concave coastal regions for an arbitrary number of heterogeneous Unmanned Aircraft Systems (UAS). The space is discretized with the constrained Delaunay triangulation and the Lloyd optimization is applied to the computed mesh. The paper presents an algorithm to compute a waypoint list for each UAS such that each sub-area is covered with its sensor on-board following a pattern that goes from the borders of the sub-area to the inner regions. In addition, the resulting paths lead to a uniform coverage pattern that avoids visiting some regions more times than others. Different sensitivity parameters of the algorithm are compared based on the average angles between the waypoints and the total length of the paths. Results show that these parameters support the optimization of the computed waypoint lists and the proposed algorithm produces feasible coverage paths while increasing their smoothness. <s> BIB031 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> The demands from precision agriculture (PA) for high-quality information at the individual plant level require to re-think the approaches exploited to date for remote sensing as performed by unmanned aerial vehicles (UAVs). A swarm of collaborating UAVs may prove more efficient and economically viable compared to other solutions. To identify the merits and limitations of a swarm intelligence approach to remote sensing, we propose here a decentralised multi-agent system for a field coverage and weed mapping problem, which is efficient, intrinsically robust and scalable to different group sizes. The proposed solution is based on a reinforced random walk with inhibition of return, where the information available from other agents (UAVs) is exploited to bias the individual motion pattern. Experiments are performed to demonstrate the efficiency and scalability of the proposed approach under a variety of experimental conditions, accounting also for limited communication range and different routing protocols. <s> BIB032 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> In this paper, we introduce an informative path planning (IPP) framework for active classification using unmanned aerial vehicles (UAVs). Our algorithm uses a combination of global viewpoint selection and evolutionary optimization to refine the planned trajectory in continuous 3D space while satisfying dynamic constraints. Our approach is evaluated on the application of weed detection for precision agriculture. We model the presence of weeds on farmland using an occupancy grid and generate adaptive plans according to information-theoretic objectives, enabling the UAV to gather data efficiently. We validate our approach in simulation by comparing against existing methods, and study the effects of different planning strategies. Our results show that the proposed algorithm builds maps with over 50% lower entropy compared to traditional “lawnmower” coverage in the same amount of time. We demonstrate the planning scheme on a multirotor platform with different artificial farmland set-ups. <s> BIB033 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> Unmanned aerial vehicles (UAVs) can offer timely and cost-effective delivery of high-quality sensing data. How- ever, deciding when and where to take measurements in complex environments remains an open challenge. To address this issue, we introduce a new multiresolution mapping approach for informative path planning in terrain monitoring using UAVs. Our strategy exploits the spatial correlation encoded in a Gaussian Process model as a prior for Bayesian data fusion with probabilistic sensors. This allows us to incorporate altitude-dependent sensor models for aerial imaging and perform constant-time measurement updates. The resulting maps are used to plan information-rich trajectories in continuous 3-D space through a combination of grid search and evolutionary optimization. We evaluate our framework on the application of agricultural biomass monitoring. Extensive simulations show that our planner performs better than existing methods, with mean error reductions of up to 45% compared to traditional "lawnmower" coverage. We demonstrate proof of concept using a multirotor to map color in different environments. <s> BIB034 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> This paper presents an optimized method to employ a team of multirotors for collecting images to generate a 3D map of an area with emphasis on special features (e.g. man-made structures) and areas of avoidance. When an area is too large to be mapped by one multirotor within its battery life, then either several flights or several multirotors need to be employed to complete the job. This paper introduces a method for equitably dividing the task of collecting images utilizing multiple vehicles to map a large area with man-made structures, which need to be mapped with high resolution. First, the number of vehicles or flights required must be determined and then the special features within the area that require more time must be considered. A parallel flood fill algorithm incorporating game theory is used to determine the best way to divide the tasking. <s> BIB035 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> We propose a multi-objective optimization algorithm to allocate tasks and plan paths for a team of UAVs. The UAVs must find a target in a bounded area and then continuously communicate the target information to the ground personnel. Our genetic algorithm approach aims to minimize the mission completion time, which includes the time to find the target (area coverage) and the time to setup a communication path (network connectivity). We evaluate strategies using a data mule, a relay chain, and a novel hybrid approach to communicate with the ground personnel. The algorithm can be tuned to prioritize coverage or connectivity, depending on the mission demands. Simulation results show reduced overall mission completion times (up to 65%), with more improvement as the UAV density increases. <s> BIB036 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> The recent advances in Unmanned Aerial Vehicles (UAVs) technology permit to develop new usages for them. One of the current challenges is to operate UAVs as an autonomous swarm. In this domain we already proposed a new mobility model using Ant Colony Algorithms combined with chaotic dynamics (CACOC) to enhance the coverage of an area by a swarm of UAVs. In this paper we propose to consider this mobility model as waypoints for real UAVs. A control model of the UAVs is deployed to test the efficiency of the coverage of an area by the swarm. We have tested our approach in a realistic robotics simulator (V-Rep) which is connected with ROS. We compare the performance in terms of coverage using several metrics to ensure that this mobility model is efficient for real UAVs. <s> BIB037 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Discussion <s> Most unmanned aerial vehicles nowadays engage in coverage missions using simple patterns, such as back-and-forth and spiral. However, there is no general agreement about which one is more appropriate. This letter proposes an E-Spiral algorithm for accurate photogrammetry that considers the camera sensor and the flight altitude to apply the overlapping necessary to guarantee the mission success. The algorithm uses an energy model to set different optimal speeds for straight segments of the path, reducing the energy consumption. We also propose an improvement for the energy model to predict the overall energy of the paths. We compare E-Spiral and E-BF algorithms in simulations over more than 3500 polygonal areas with different characteristics, such as vertices, irregularity, and size. Results showed that E-Spiral outperforms E-BF in all the cases, providing an effective energy saving even in the worst scenario with a percentage improvement of <inline-formula><tex-math notation="LaTeX">$10.37\%$</tex-math> </inline-formula> up to the best case with <inline-formula><tex-math notation="LaTeX">$\text{16.1}\%$</tex-math> </inline-formula> of improvement. Real flights performed with a quadrotor state the effectiveness of the E-Spiral over E-BF in two areas, presenting an improvement of <inline-formula><tex-math notation="LaTeX">$\text{9}\%$</tex-math> </inline-formula> in the time and <inline-formula><tex-math notation="LaTeX">$\text{7.7}\%$</tex-math></inline-formula> in the energy. The improved energy model increases the time and the energy estimation precision of <inline-formula> <tex-math notation="LaTeX">$\text{13.24}\%$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX"> $\text{13.41}\%$</tex-math></inline-formula>, respectively. <s> BIB038
The coverage path planning problem with UAVs has been addressed by several authors in the literature, exploring areas of interest with different shapes and complexities. Generally, simple areas of interest, such as rectangular and convex polygons, do not require any discretization method or decomposition technique, being explored by back-and-forth and spiral flight patterns. The back-and-forth pattern usually defines the sweep direction based on the major axis and uses 90 • angles for the turning maneuvers. In polygonal cases, the turning angle may vary both back-and-forth and spiral. In general, these patters require low computational time to find coverage paths and are easy to perform by the aerial platform. One of the main issues involving these platforms is the quite limited flight endurance to perform coverage missions. Several studies seek to minimize distance, flight time or maneuvers in order to decrease energy consumption. During turning maneuvers, the vehicles should decelerate, rotate and accelerate, extending flight time and, consequently, energy consumption. An alternative is smoothing turning maneuvers to keep the velocity of the vehicles constant, as proposed by Artemenko et al. BIB020 . Recent efforts developed state-of-the-art energy-aware back-and-forth BIB021 and spiral BIB038 algorithms mainly concerned about energy consumption considering resolution and energy constraints of the UAVs. These approaches adopt an energy model explored for analyzing the energetic behavior in distinct circumstances. Thus, optimizing velocity in straight parts of the path leads to energy consumption minimization. Table 1 summarizes the CPP approaches revised in this paper considering areas of interest with no cellular decomposition technique. The table presents the CPP approach, corresponding reference, shape of the area of interest, adopted performance metrics to evaluate coverage pattern, indication of single or multiple UAVs used in the coverage, and the type of UAV: rotary-wing (RW), fixed-wing (FW), or both. In larger and more complex areas of interest, an exact cellular decomposition may be applied to split the scenario into subregions. The resulting sub-regions can be covered by different sweep directions to obtain an optimal coverage BIB004 BIB006 . In such areas, there is the possibility to explore four back-and-forth alternatives, varying the direction and the orientation, aiming at minimizing the distance among the subareas, as presented by Torres et al. BIB022 . Cooperative strategies are also being explored using area decomposition according to the relative capabilities of the vehicles BIB001 . Obstacles and forbidden flight zones may also be considered in the scenarios BIB007  and, depending on the mission, it might be necessary to revisit previously explored areas BIB002 BIB009 . A hybrid decomposition technique mixing exact and approximate cellular decomposition is proposed to discretize the area of interest into triangle cells matching almost the exact shape of the area BIB023 BIB029 BIB030 BIB031 . The authors employ a spiral-like pattern to perform the coverage in a complex area. A decentralized approach is employed for partitioning and area coverage in regular and irregular-shaped scenarios using heterogeneous aerial vehicles BIB010 BIB011 BIB014  . Table 2 summarizes the CPP approaches revised in this paper considering areas of interest discretized through the exact cellular decomposition technique. The online/offline column in the table refers to how the coverage path is obtained. In offline cases, the whole path is computed before being performed, while in online cases the path can be computed or modified during coverage. Depending on the complexity of the area of interest, simple flight patterns may generate inefficient trajectories, requiring post-processing phases to adjust the waypoint positions and the angle between them to overcome this issue. A considerable computational time may be spent evaluating all different rotations to find the optimal sweep direction. Regarding spiral motions, shorter paths can be generated in polygonal shapes with larger internal angles, but in more complex forms, the algorithm may be stuck or may not complete the coverage task. Thus, more sophisticated approaches based on approximate cellular decomposition have been proposed for coverage with UAVs. In Section 4, we decided to separate the existing approaches according to the available information to compute and perform the coverage. When the UAV has full knowledge about the workspace, including area and NFZ, the path can be computed in an offline phase before its execution. However, in some cases, the aerial vehicle does not have complete information about the map due to the presence of moving targets, obstacles, or other vehicles. In this case, the UAV should gather the necessary information using its sensors to perform the mission, usually interleaving between planning and execution. Considering full information, grid-based solutions exploring the Wavefront algorithm for single UAV are presented by Valente et al. BIB008 and further optimized using cubic interpolation by Nam et al. BIB024 . Cooperative strategies using multiple vehicles are introduced by Barrientos et al.  , including area subdivision and vehicle allocation. A meta-heuristic algorithm named Harmony Search (HS) is proposed by Valente et al. BIB012 to minimize the number of turning maneuvers in irregular areas and it is compared with the approach proposed by Barrientos et al.  . Parts of the area of interest may be covered with distinct resolutions through flights at different altitudes, according to the importance of each subregion BIB015 BIB019 or depending on the different footprint sensors onboard the vehicles BIB013 . Cooperative bio-inspired techniques have been dealing with environments containing only partial information. Real-time search methods are considered in simulations and a Node Counting algorithm is applied in real flights by Nattero et al.  . A reinforced random walk strategy is introduced by Albani et al. BIB032 for field coverage and weed mapping, where the UAVs prefer to explore the areas ahead of its current position aligned with the momentum vector. A cellular automata approach is proposed by Zelenka and Kasanicky BIB016 and Zelenka and Kasanicky BIB017 , but presents problems regarding the pheromone degradation and lack of evaporation. Degradation may generate communication failures between the vehicles, while the excess of pheromones may block certain locations of the area for the vehicles. Some approaches consider the local uncertainty during the coverage missions BIB005 , while other ones explore distributed information merging, where the UAVs directly exchange information to speed up the search and enhance the performance BIB018 . Path replanning in a continuous UAV trajectory is also explored, where the authors fuse visual information received into a single probabilistic map BIB033 BIB034 . Another continuous coverage approach using a learning algorithm is addressed by Ramasamy and Ghose BIB025 , considering the preferential surveillance problem. In this problem, the aerial vehicle should increase the visitation frequency in regions of interest and reduce the visitation frequency in a risky region. GA combined with pheromone strategies are also explored BIB026 BIB027 BIB035 BIB036 , while ACO using pheromone maps are introduced for coverage missions with UAVs BIB003 BIB028 BIB037 . Table 3 summarizes the CPP approaches revised in this paper considering areas of interest discretized through the approximate cellular decomposition technique considering full and partial information. While the CPP approaches with no decomposition or combined with exact cellular decomposition can be executed by rotary-wing, fixed-wing, or both types of vehicles, the CPP approaches using approximate cellular decomposition almost exclusively adopt rotary-wing UAVs. This is because the rotary-wings present maneuverability advantages when making turns in scenarios discretized into a grid. The fixed-wing has maneuverability restrictions, demanding a large space to make turns. The algorithms proposed to solve the CPP problem are usually concerned with the planning phase to obtain a coverage path according to a performance metric. When dealing with fixed-wing UAVs, the approaches must also consider motion constraints of such vehicles in order to plan a feasible trajectory. However, these approaches do not consider important control tasks such as path following or trajectory tracking, trusting exclusively the internal controller of the UAV to perform the planned path in real flights.