Unmanned Aerial Vehicles Search Research Articles

Online Unmanned Aerial Vehicles Search Planning in an Unknown Search Environment

Unmanned Aerial Vehicles (UAVs) have been widely used in localized data collection and information search. However, there are still many practical challenges in real-world operations of UAV search, such as unknown search environments. Specifically, the payoff and cost at each search point are unknown for the planner in advance, which poses a great challenge to decision making. That is, UAV search decisions should be made sequentially in an online manner thereby adapting to the unknown search environment. To this end, this paper initiates the problem of online decision making in UAV search planning, where the drone has limited energy supply as a constraint and has to make an irrevocable decision to search this area or route to the next in an online manner. To overcome the challenge of unknown search environment, a joint-planning approach is proposed, where both route selection and search decision are made in an integrated online manner. The integrated online decision is made through an online linear programming which is proved to be near-optimal, resulting in high information search revenue. Furthermore, this joint-planning approach can be favorably applied to multi-round online UAV search planning scenarios, showing a great superiority in first-mover dominance of gathering information. The effectiveness of the proposed approach is validated in a widely applied dataset, and experimental results show the superior performance of online search decision making.

Three-Dimensional Path Planning for Post-Disaster Rescue UAV by Integrating Improved Grey Wolf Optimizer and Artificial Potential Field Method

The path planning of unmanned aerial vehicles (UAVs) is crucial in UAV search and rescue operations to ensure efficient and safe search activities. However, most existing path planning algorithms are not suitable for post-disaster mountain rescue mission scenarios. Therefore, this paper proposes the IGWO-IAPF algorithm based on the fusion of the improved grey wolf optimizer (GWO) and the improved artificial potential field (APF) algorithm. This algorithm builds upon the grey wolf optimizer and introduces several improvements. Firstly, a nonlinear adjustment strategy for control parameters is proposed to balance the global and local search capabilities of the algorithm. Secondly, an optimized individual position update strategy is employed to coordinate the algorithm’s search ability and reduce the probability of falling into local optima. Additionally, a waypoint attraction force is incorporated into the traditional artificial potential field algorithm based on the force field to fulfill the requirements of three-dimensional path planning and further reduce the probability of falling into local optima. The IGWO is used to generate an initial path, where each point is assigned an attraction force, and then the IAPF is utilized for subsequent path planning. The simulation results demonstrate that the improved IGWO exhibits approximately a 60% improvement in convergence compared to the conventional GWO. Furthermore, the integrated IGWO-IAPF algorithm shows an approximately 10% improvement in path planning effectiveness compared to other traditional algorithms. It possesses characteristics such as shorter flight distance and higher safety, making it suitable for meeting the requirements of post-disaster rescue missions.

UAV search coverage under priority of important targets based on multi-location domain decomposition

<abstract> <p>In recent years, the coverage path planning (CPP) of unmanned aerial vehicles (UAVs) has attracted attention in reconnaissance, patrol, and search and rescue efforts, aiming to plan the paths for UAVs to cover a specified area as efficiently as possible. This paper proposes a UAV path fast coverage model to prioritize important targets with domain composition based on the starting point and location of the targets, combined with the domain decomposition strategy of important targets. Considering the constraints of the number of UAVs, the number of operators, and the flight time, the parallel search strategy can plan the coverage scheme with the shortest search time for the search range, and further obtain the coordinate points and path coordinates of the UAV turning. Finally, through multiple simulation experiments in four maps of various islands, the proposed method is verified to have an improved performance compare to the two track path coverage algorithms methods in terms of the coverage efficiency and the time complexity, thus providing a more scientific basis for the path coverage research of multi-target searches.</p> </abstract>

Cooperative search for moving targets with the ability to perceive and evade using multiple UAVs

This paper focuses on the problem of regional cooperative search using multiple unmanned aerial vehicles (UAVs) for targets that have the ability to perceive and evade. When UAVs search for moving targets in a mission area, the targets can perceive the positions and flight direction of UAVs within certain limits and take corresponding evasive actions, which makes the search more challenging than traditional search problems. To address this problem, we first define a detailed motion model for such targets and design various search information maps and their update methods to describe the environmental information based on the prediction of moving targets and the search results of UAVs. We then establish a multi-UAV search path planning optimization model based on the model predictive control, which includes various newly designed objective functions of search benefits and costs. We propose a priority-encoded improved genetic algorithm with a fine-adjustment mechanism to solve this model. The simulation results show that the proposed method can effectively improve the cooperative search efficiency, and more targets can be found at a much faster rate compared to traditional search methods.

Unmanned Aerial Vehicles for Search and Rescue: A Survey

In recent years, unmanned aerial vehicles (UAVs) have gained popularity due to their flexibility, mobility, and accessibility in various fields, including search and rescue (SAR) operations. The use of UAVs in SAR can greatly enhance the task success rates in reaching inaccessible or dangerous areas, performing challenging operations, and providing real-time monitoring and modeling of the situation. This article aims to help readers understand the latest progress and trends in this field by synthesizing and organizing papers related to UAV search and rescue. An introduction to the various types and components of UAVs and their importance in SAR operations is settled first. Additionally, we present a comprehensive review of sensor integrations in UAVs for SAR operations, highlighting their roles in target perception, localization, and identification. Furthermore, we elaborate on the various applications of UAVs in SAR, including on-site monitoring and modeling, perception and localization of targets, and SAR operations such as task assignment, path planning, and collision avoidance. We compare different approaches and methodologies used in different studies, assess the strengths and weaknesses of various approaches, and provide insights on addressing the research questions relating to specific UAV operations in SAR. Overall, this article presents a comprehensive overview of the significant role of UAVs in SAR operations. It emphasizes the vital contributions of drones in enhancing mission success rates, augmenting situational awareness, and facilitating efficient and effective SAR activities. Additionally, the article discusses potential avenues for enhancing the performance of UAVs in SAR.

Edge Computing Enabled Energy-Efficient Multi-UAV Cooperative Target Search

Multiple unmanned aerial vehicles (UAVs) cooperative search have been widely adopted for surveillance and search-related applications. For a certain search area, UAVs may need to search it repeatedly to obtain a high-confidence result about the target distribution in the search area. However, the short battery life and moderate computational capability restrict UAVs to repeatedly execute the computation-intensive and energy-consuming search tasks. To address the issue, in this paper, we utilize edge computing to develop a continual and cooperative UAV search mechanism. Specifically, we first establish an edge computing enabled multi-UAV cooperative search framework, in which the mobility model of UAV, search task computing and offloading models are presented. An uncertainty minimization problem is then formulated, aiming to obtain a high-efficiency and high-confidence search result at the unpredictable uncertainty in search area. Considering that round-trip energy consumption, offloading decision-making, and trajectory planning may contribute to the reduction in uncertainty, we propose an uncertainty minimization-based cooperative target search (UMCTS) strategy. Finally, extensive simulation results validate that UMCTS can outperform the existing strategies and achieve at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$89\%$</tex-math></inline-formula> performance gain on average uncertainty. Based on the results, we also present a comprehensive analysis and discussion on how different parameters affect the search performance.

An Unmanned Aerial Vehicle (UAV) System for Disaster and Crisis Management in Smart Cities

Over the course of the last decade, the unmanned aerial vehicle (UAV) research community has received a significant amount of attention. Emergency response operations, such as those that follow a natural disaster, are one of the civil applications that could benefit from the use of UAVs in disaster and crisis management. In the event of a catastrophic event, it would be extremely beneficial for both victims and first responders to have access to a UAV network that is capable of deploying independently and offering communication services. However, when working with complicated situations, one of the most difficult things is coming up with exploratory paths for the networks involved. A crisis and disaster management system using a swarm optimization algorithm (SOA) is proposed to assist in disaster and crisis management. In this system, the UAV search and rescue team follows the strategy called the delay tolerant network, which has the ability to explore. The proposed approach is able to find the global maximum in the search space without ever settling for a suboptimal solution. This work has two primary objectives: the first is to investigate a potential disaster zone, and the second is to direct the UAV to a number of victim groups that were found during the investigation phase. For the purpose of performing a characterization, performance metrics such as delay, throughput, performance rate, and path loss have been analyzed. The results show the superiority of the performance over the existing work.

Visual servoing of quadrotor UAVs for slant targets with autonomous object search

In this paper, an enhanced visual servoing method is designed for a quadrotor unmanned aerial vehicle (UAV) based on virtual plane image moments, under underactuation and tight coupling constraints of UAV kinematics. Moreover, in order to make the UAV search visual targets autonomously in target vicinity during flight, a flexible flight system is developed with stages of take-off, target searching, and image-based visual servoing (IBVS). With dual-camera sensor configuration, the UAV system searches targets from given directions while making localization. A virtual image plane is constructed and image moments are adopted to decouple UAV lateral movement. For a non-horizonal target, homography is utilized to construct the target plane and transform it into a horizonal plane. Backstepping techniques are used to derive the nonlinear controller to realize the IBVS strategy. Stability analysis proves global asymptotic performance of the closed-loop system. Experimental verification shows feasibility of the overall flight system and effectiveness of the visual servoing controller.

Cooperative UAV search strategy based on DMPC-AACO algorithm in restricted communication scenarios

Improvement of integrated battlefield situational awareness in complex environments involving dynamic factors such as restricted communications and electromagnetic interference (EMI) has become a contentious research problem. In certain mission environments, due to the impact of many interference sources on real-time communication or mission requirements such as the need to implement communication regulations, the mission stages are represented as a dynamic combination of several communication-available and communication-unavailable stages. Furthermore, the data interaction between unmanned aerial vehicles (UAVs) can only be performed in specific communication-available stages. Traditional cooperative search algorithms cannot handle such situations well. To solve this problem, this study constructed a distributed model predictive control (DMPC) architecture for a collaborative control of UAVs and used the Voronoi diagram generation method to re-plan the search areas of all UAVs in real time to avoid repetition of search areas and UAV collisions while improving the search efficiency and safety factor. An attention mechanism ant-colony optimization (AACO) algorithm is proposed for UAV search-control decision planning. The search strategy is adaptively updated by introducing an attention mechanism for regular instruction information, a priori information, and emergent information of the mission to satisfy different search expectations to the maximum extent. Simulation results show that the proposed algorithm achieves better search performance than traditional algorithms in restricted communication constraint scenarios.

A Two-Stage Strategy for UAV-enabled Wireless Power Transfer in Unknown Environments

Due to the outstanding merits such as mobility, high maneuverability, and flexibility, Unmanned Aerial Vehicles (UAVs) are viable mobile power transmitters that can be rapidly deployed in geographically constrained regions. They are good candidates for supplying power to energy-limited Sensor Nodes (SNs) with Wireless Power Transfer (WPT) technology. In this paper, we investigate a UAV-enabled WPT system that transmits power to a set of SNs at unknown positions. A key challenge is how to efficiently gather the locations of SNs and design a power transfer scheme. We formulate a multi-objective optimization problem to jointly optimize these objectives: maximization of UAV's search efficiency, maximization of total harvested energy, minimization of UAV's flight energy consumption and maximization of UAV's energy utilization efficiency. To tackle these issues, we present a two-stage strategy that includes a UAV Motion Control (UMC) algorithm for obtaining the coordinates of SNs and a Dynamic Genetic Clustering (DGC) algorithm for power transfer via grouping SNs into clusters. First, the UMC algorithm enables the UAV to autonomously control its own motion and conduct target search missions. The objective is to make the energy-restricted UAV find as many SNs as feasible without any apriori knowledge of their information. Second, the DGC algorithm is used to optimize the energy consumption of the UAV by combining a genetic clustering algorithm with a dynamic clustering strategy to maximize the amount of energy harvested by SNs and the energy utilization efficiency of the UAV. Finally, experimental results show that the proposed algorithms outperform their counterparts.

A UAV-Assisted Search and Localization Strategy in Non-Line-of-Sight Scenarios

Recently, unmanned aerial vehicle (UAV)-assisted ground targets localization is widely used in search and rescue (SAR) scenes. In this article, we propose a UAV search and localization strategy, which can search and locate unknown number of victims. The proposed strategy uses the UAV as a mobile anchor to measure time of arrival (TOA) and can effectively mitigate localization errors caused by non-line-of-sight (NLOS) propagation. Generally speaking, the strategy is divided into two parts: 1) trajectory planning and 2) localization. By selecting waypoints and planning a suitable search trajectory, UAV can obtain all the measurement information in deployment area and ensure that each target has at least three anchors for localization even in the NLOS propagation environment. In the localization stage, a new estimator is proposed based on maximum-likelihood estimation (MLE), which estimates average NLOS bias together with the coordinates of target and can be well solved by the particle swarm algorithm. The simulation results show that the proposed strategy outperforms for mitigating NLOS error compared with other methods, and can effectively ensure high localization accuracy in the NLOS propagation environment.

Helicopter–UAVs search and rescue task allocation considering UAVs operating environment and performance

The task allocation of unmanned aerial vehicles (UAVs) is important in UAV search and rescue to guarantee an effective and orderly search and rescue activity. However, few studies have investigated the influences of the UAV operating environment and performance on search and rescue task allocation. Considering the influences of low-altitude wind and terrain on UAV energy consumption and performance, UAV release position (from helicopter) selection and task allocation models are proposed in this study. Given that the detection zone of UAVs is affected by topographic factors, the principal component analysis method was used to determine the search and rescue level at each search and rescue point and the UAV hovering endurance, which was determined using clustering analysis. Considering the influence factors of UAV battery energy consumption, such as UAV performance and low-altitude wind, an UAV release position selection model was constructed and solved via the improved binary bat algorithm which exhibited enhanced calculation accuracy comparing with three other algorithms. According to the release position planning result, a multi-objective (minimization of the total search and rescue cost and number of UAVs and multi-UAV task balance) optimization model was established and solved using the non-dominated sorting genetic algorithm-II. Finally, the experimental results indicated that the solution through multiple objectives had no cost advantage comparing with those under the single objective, but exhibited an evident advantage in task completion time through sensitivity analysis of UAVs task allocation problem.

An Information-Motivated Exploration Agent to Locate Stationary Persons with Wireless Transmitters in Unknown Environments.

Unmanned Aerial Vehicles (UAVs) show promise in a variety of applications and recently were explored in the area of Search and Rescue (SAR) for finding victims. In this paper we consider the problem of finding multiple unknown stationary transmitters in a discrete simulated unknown environment, where the goal is to locate all transmitters in as short a time as possible. Existing solutions in the UAV search space typically search for a single target, assume a simple environment, assume target properties are known or have other unrealistic assumptions. We simulate large, complex environments with limited a priori information about the environment and transmitter properties. We propose a Bayesian search algorithm, Information Exploration Behaviour (IEB), that maximizes predicted information gain at each search step, incorporating information from multiple sensors whilst making minimal assumptions about the scenario. This search method is inspired by the information theory concept of empowerment. Our algorithm shows significant speed-up compared to baseline algorithms, being orders of magnitude faster than a random agent and 10 times faster than a lawnmower strategy, even in complex scenarios. The IEB agent is able to make use of received transmitter signals from unknown sources and incorporate both an exploration and search strategy.

UAV cooperative search in dynamic environment based on hybrid-layered APF

Unmanned aerial vehicle (UAV) detection has the advantages of flexible deployment and no casualties. It has become a force that cannot be ignored in the battlefield. Scientific and efficient mission planning can help improving the survival rate and mission completion rate of the UAV search in dynamic environments. Towards the mission planning problem of UAV collaborative search for multi-types of time-sensitive moving targets, a search algorithm based on hybrid layered artificial potential fields algorithm (HL-APF) was proposed. This method consists of two parts, a distributed artificial field algorithm and a centralized layered algorithm. In the improved artificial potential field (IAPF), this paper utilized a new target attraction field function which was segmented by the search distance to quickly search for dynamic targets. Moreover, in order to solve the problem of repeated search by the UAV in a short time interval, a search repulsion field generated by the UAV search path was proposed. Besides, in order to solve the unknown target search and improve the area coverage, a centralized layered scheduling algorithm controlled by the cloud server (CS) was added. CS divides the mission area into several sub-areas, and allocates UAV according to the priority function based on the search map. The CS activation mechanism can make full use of prior information, and the UAV assignment cool-down mechanism can avoid the repeated assignment of the same UAV. The simulation results show that compared with the hybrid artificial potential field and ant colony optimization and IAPF, HL-APF can significantly improve the number of targets and mission area coverage. Moreover, comparative experiment results of CS mechanism proved the necessity of setting CS activation and cool-down for improving the search performance. Finally, it also verified the robustness of the method under the failure of some UAVs.

Moving peak drone search problem: An online multi-swarm intelligence approach for UAV search operations

Many practical, real-world applications have dynamic features. This paper introduces a novel dynamic optimization problem applied to Unmanned Aerial Vehicle (UAV) search and rescue scenarios, named the Moving Peak Drone Search Problem (MPDSP). It utilizes the dynamic environment generator of the well-known Moving Peak Benchmark (MPB) and it is formulated as a maximization problem, with additional constraints imposed by the use of UAVs. For solving the MPDSP, a multi-swarm framework is proposed and seven optimization algorithms are tested. Five well known swarm intelligence algorithms and two algorithms effectively used in continuous and dynamic optimization problems. The implemented methods are evaluated and compared on 105 scenarios with 4 different UAV fleet configurations. Among the tested swarm intelligence variants, the Particle Swarm Optimization implementations proved to be the most effective for solving the MPDSP.

Evolutionary Collaborative Human-UAV Search for Escaped Criminals

The use of unmanned aerial vehicles (UAVs) for target searching in complex environments has increased considerably in recent years. The numerous studies on UAV search methods have been reported, but few have been conducted on collaborative human-UAV search which is common in many applications. In this paper, we present a problem of collaborative human-UAV search for escaped criminals, the aim of which is to minimize the expected time of capture rather than detection. We show that our problem is much more complex than the problem of pure UAV search. The difficulty of our problem is further increased by the fact that criminals will attempt to avoid detection and capture. To solve the problem, we propose a hybrid evolutionary algorithm (EA) that uses three evolutionary operators, namely, comprehensive learning, variable mutation, and local search, to efficiently explore the solution space. The experimental results demonstrate that the proposed method outperforms some well-known EAs and other popular UAV search methods on test instances. An application of our method to a real-world operation took 311 min to capture a criminal who had escaped for over three days, validating its practicability and performance advantage. This paper provides a good basis for promoting the application of EAs to a wider class of man–machine collaboration scheduling problems.

Gaussian mixture model and receding horizon control for multiple UAV search in complex environment

In this paper, we present a three-layer distributed control structure with certain centralization mechanism to generate the optimal trajectories of multiple unmanned aerial vehicles (UAVs) for searching target in complex environment, based on the method of Gaussian mixture model (GMM) and receding horizon control (RHC). The goal of cooperative searching problem is to obtain the maximum probability of finding the target during given flight time under various constraints, e.g., obstacle/collision avoidance and simultaneous arrival at the given destination. Hence it is taken as a complicated discrete optimization problem in this paper. First, GMM is utilized to approximate the prior known target probability distribution map, and the searching region is hence decomposed where several subregions representing a cluster of target probability can be extracted. Second, these subregions are prioritized hierarchically by evaluating their Gaussian components obtained from GMM, and then allocated to UAVs aiming to maximize the predicted mission payoff. Third, each UAV visits its allocated subregions sequentially, and the corresponding trajectory is obtained by RHC-based concurrent method. Finally, the proposed method is demonstrated and compared with other methods in the simulated scenario. The simulation results show its high efficiency to solve the cooperative searching problem.

Robust Satisficing Decision Making for Unmanned Aerial Vehicle Complex Missions under Severe Uncertainty.

This paper presents a robust satisficing decision-making method for Unmanned Aerial Vehicles (UAVs) executing complex missions in an uncertain environment. Motivated by the info-gap decision theory, we formulate this problem as a novel robust satisficing optimization problem, of which the objective is to maximize the robustness while satisfying some desired mission requirements. Specifically, a new info-gap based Markov Decision Process (IMDP) is constructed to abstract the uncertain UAV system and specify the complex mission requirements with the Linear Temporal Logic (LTL). A robust satisficing policy is obtained to maximize the robustness to the uncertain IMDP while ensuring a desired probability of satisfying the LTL specifications. To this end, we propose a two-stage robust satisficing solution strategy which consists of the construction of a product IMDP and the generation of a robust satisficing policy. In the first stage, a product IMDP is constructed by combining the IMDP with an automaton representing the LTL specifications. In the second, an algorithm based on robust dynamic programming is proposed to generate a robust satisficing policy, while an associated robustness evaluation algorithm is presented to evaluate the robustness. Finally, through Monte Carlo simulation, the effectiveness of our algorithms is demonstrated on an UAV search mission under severe uncertainty so that the resulting policy can maximize the robustness while reaching the desired performance level. Furthermore, by comparing the proposed method with other robust decision-making methods, it can be concluded that our policy can tolerate higher uncertainty so that the desired performance level can be guaranteed, which indicates that the proposed method is much more effective in real applications.

Response Threshold Model Based UAV Search Planning and Task Allocation

This paper addresses a search planning and task allocation problem for a Unmanned Aerial Vehicle (UAV) team that performs a search and destroy mission in an environment where targets with different values move around. The UAVs are heterogeneous having different sensing and attack capabilities, and carry limited amount of munitions. The objective of the mission is to find targets and eliminate them as quickly as possible considering the values of the targets. In this context, there are two distinct issues that need to be addressed simultaneously: search planning and task allocation. The search plan generates an efficient search path for each UAV to facilitate a fast target detection. The task allocation assigns UAVs attack tasks over detected targets such that each UAV's attack capability is respected. We model these two issues in one framework and propose a distributed approach that utilizes a probabilistic decision making mechanism based on response threshold model. The proposed approach accounts for natural uncertainties in the environment, and provides flexibility, resulting in efficient exploration in the environment and effective allocation of attack tasks. The approach is evaluated in simulation experiments in comparison with other methods, of which results show that our approach outperforms the other methods.

Human Body Detection in Search and Rescue Operation Conducted by Unmanned Aerial Vehicles

Human body detection is a fundamental research problem where computer vision could be applied. This research area has wide applications including surveillance, search and rescue missions. Building an effective human body detector framework under wide ranges of weather, lighting and environmental conditions is a challenge task. A variety of sensors and optimisation algorithms are proposed. This paper aims to review the human body detection methodology conducted by the Unmanned Aerial Vehicles (UAV), and the future research for the UAV search and rescue applications.