Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Partial Information <s> In MANET research the mobility of the nodes is often described using standard synthetic models. Given a particular application, e.g. networks of unmanned aerial vehicles (UAVs) performing a cooperative task, the use of a standard synthetic mobility model can result in incorrect conclusions, as the movement pattern can impact the networking performance of the system. In this paper we present the criteria that characterize desirable mobility properties for the movement of UAVs in a reconnaissance scenario, and provide two mobility models for the scenario. In the first mobility model the UAVs move independently and randomly, and in the second model pheromones guide their movement. The random model is very simple but it achieves mediocre results. The pheromone model has very good scanning properties, but it has problems with respect to network connectivity. The study shows that there in an inherent conflict between the two goals of maximum coverage by frequent rescanning and adequate communication connectivity. <s> BIB001 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Partial Information <s> Robotics researchers increasingly agree that ideas from biology and self-organization can strongly benefit the design of autonomous robots. Biological organisms have evolved to perform and survive in a world characterized by rapid changes, high uncertainty, indefinite richness, and limited availability of information. Industrial robots, in contrast, operate in highly controlled environments with no or very little uncertainty. Although many challenges remain, concepts from biologically inspired (bio-inspired) robotics will eventually enable researchers to engineer machines for the real world that possess at least some of the desirable properties of biological organisms, such as adaptivity, robustness, versatility, and agility. <s> BIB002 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Partial Information <s> New approaches to artificial intelligence spring from the idea that intelligence emerges as much from cells, bodies, and societies as it does from evolution, development, and learning. Traditionally, artificial intelligence has been concerned with reproducing the abilities of human brains; newer approaches take inspiration from a wider range of biological structures that that are capable of autonomous self-organization. Examples of these new approaches include evolutionary computation and evolutionary electronics, artificial neural networks, immune systems, biorobotics, and swarm intelligenceto mention only a few. This book offers a comprehensive introduction to the emerging field of biologically inspired artificial intelligence that can be used as an upper-level text or as a reference for researchers. Each chapter presents computational approaches inspired by a different biological system; each begins with background information about the biological system and then proceeds to develop computational models that make use of biological concepts. The chapters cover evolutionary computation and electronics; cellular systems; neural systems, including neuromorphic engineering; developmental systems; immune systems; behavioral systemsincluding several approaches to robotics, including behavior-based, bio-mimetic, epigenetic, and evolutionary robots; and collective systems, including swarm robotics as well as cooperative and competitive co-evolving systems. Chapters end with a concluding overview and suggested reading. <s> BIB003 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Partial Information <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> BIB004 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Partial Information <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> Partial Information <s> Insect colony inspires scientists for years to create similar behavior in the robotic application. The main goal of our work was to develop simple and powerful algorithm which will accept dynamically changes in the size of a robot swarm due the mission. This algorithm is suitable for situations where unpredictable conditions may lead to robot fault in multi-robotics system and mission completion is endangered. In this article we would like to investigate properties of a simple pheromone based algorithm. The algorithm operates as cellular automata and partially uses an insect pheromone strategy for the robots coordination. Our abstract model is a decentralized adaptive system with a shared memory which represents the environment. <s> BIB006 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Partial Information <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> BIB007 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Partial Information <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> BIB008 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Partial Information <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> BIB009 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Partial Information <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> BIB010 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Partial Information <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> BIB011 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Partial Information <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> BIB012 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Partial Information <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> BIB013 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Partial Information <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> BIB014 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Partial Information <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> BIB015 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Partial Information <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> BIB016 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Partial Information <s> A heuristic learning algorithm is presented in this paper to solve the problem of persistent preferential surveillance by an Unmanned Aerial Vehicle (UAV). The algorithm helps a UAV perform surveillance as per quantitative priority specifications over a known area. It allows the specification of regional priorities either as percentages of visitation to be made by a UAV to each region or as percentages of surveillance time to be spent within each. Additionally, the algorithm increases the likelihood of target detection in an unknown area. The neighborhood of a detected target is suspected to be a region of a high likelihood of target detection, and the UAV plans its path accordingly to verify this suspicion. Similar to using the target information, the algorithm uses the risk information to reduce the frequency of visits to risky regions. The technique of using risk map to avoid risky regions is adapted from the existing geometric reinforcement learning technique. The effectiveness of this algorithm is demonstrated using simulation results. <s> BIB017
Biological systems are able to adapt, evolve and survive in natural environments characterized by quick modifications, elevated uncertainty, and restricted information BIB002 . Since such natural systems are robust and sophisticated, they have been replicated as computational systems over the last decades in order to solve complex optimization problems and application topics BIB003 . Thus biologically-inspired approaches consisting of algorithms based on fundamental aspects of natural intelligence have emerged, such as behavioral autonomy and social interaction, evolution and learning BIB003 . Considering the CPP problem with aerial vehicles, several authors have explored different approaches in the literature, including real-time search methods  , random walk BIB014 , cellular systems BIB006 BIB007 BIB008 , evolutionary computation BIB010 BIB011 , and swarm intelligence BIB004 BIB001 BIB012 . Coverage with uncertainty considering information points is also addressed BIB009 BIB005 BIB015 BIB016 BIB013 BIB017 . Most of the approaches are pheromone-based and explore the natural behavior of ants to guide the vehicles through a grid-discretized scenario.