A knee-guided differential evolution algorithm for unmanned aerial vehicle path planning in disaster management

Path planning optimization in unmanned aerial vehicles using meta-heuristic algorithms: a systematic review

Unmanned aerial vehicles (UAVs) have recently been increasingly popular in various areas, fields, and applications. Military, disaster management, rescue operations, public services, agriculture, and various other areas are examples. As a result, UAV path planning is concerned with determining the optimal path from the source to the destination while avoiding collisions with lowering the cost of time, energy, and other resources. This review aims to assort academic studies on the path planning optimization in UAV using meta-heuristic algorithms, summarize the results of each optimization algorithm, and extend the understanding of the current state of the path planning in UAV in the meta-heuristic optimization field. For this purpose, we implemented a broad, automated search using Boolean and snowballing searching methods to find academic works on path planning in UAVs. Studies and papers have been distinguished, and the following information was obtained and aggregated from each article: authors, publication's year, the journal name or the conference name, proposed algorithms, the aim of the study, the outcome, and the quality of each study. According to the findings, the meta-heuristic algorithm is a standard optimization method for tackling single and multi-objective problems. Besides, the findings show that meta-heuristic algorithms have a great compact on the path planning optimization in UAVs, and there is good progress in this field. However, the problem still exists mainly in complex and dynamic environments, on battlefields, in rescue missions, mobile obstacles, and with multiple UAVs.

Development of Fixed-Wing UAV 3D Coverage Paths for Urban Air Quality Profiling

Due to the ever-increasing industrial activity, humans and the environment suffer from deteriorating air quality, making the long-term monitoring of air particle indicators essential. The advances in unmanned aerial vehicles (UAVs) offer the potential to utilize UAVs for various forms of monitoring, of which air quality data acquisition is one. Nevertheless, most current UAV-based air monitoring suffers from a low payload, short endurance, and limited range, as they are primarily dependent on rotary aerial vehicles. In contrast, a fixed-wing UAV may be a better alternative. Additionally, one of the most critical modules for 3D profiling of a UAV system is path planning, as it directly impacts the final results of the spatial coverage and temporal efficiency. Therefore, this work focused on developing 3D coverage path planning based upon current commercial ground control software, where the method mainly depends on the Boustrophedon and Dubins paths. Furthermore, a user interface was also designed for easy accessibility, which provides a generalized tool module that links up the proposed algorithm, the ground control software, and the flight controller. Simulations were conducted to assess the proposed methods. The result showed that the proposed methods outperformed the existing coverage paths generated by ground control software, as it showed a better coverage rate with a sampling density of 50 m.

An Anti-Disturbance Resilience Enhanced Algorithm for UAV 3D Route Planning

Considering that the actual operating environment of UAV is complex and easily disturbed by the space environment of urban buildings, the RoutE Planning Algorithm of Resilience Enhancement (REPARE) for UAV 3D route planning based on the A* algorithm and artificial potential fields algorithm is carried out in a targeted manner. First of all, in order to ensure the safety of the UAV design, we focus on the capabilities of the UAV body and build a risk identification, assessment, and modeling method such that the mission control parameters of the UAV can be determined. Then, the three-dimensional route planning algorithm based on the artificial potential fields algorithm is used to ensure the safe operation of the UAV online and in real time. At the same time, by adjusting the discriminant coefficient of potential risks in real time to deal with time-varying random disturbance encountered by the UAV, the resilience of the UAV 3D flight route planning can be improved. Finally, the effectiveness of the algorithm is verified by the simulation. The simulation results show that the REPARE algorithm can effectively solve the traditional route planning algorithm’s insufficiency in anti-disturbance. It is safer than a traditional A* route planning algorithm, and its running time is shorter than that of the traditional artificial potential field route planning algorithm. It solves the problems of local optimization, enhances the UAV’s ability to tolerate general uncertain disturbances, and eventually improves resilience of the system.

A Method of Planning Disaster Emergency Rescue Paths in Road-Free Environment

The occurrence of natural disasters has caused great loss of lives and properties that affected many people. To improve the efficiency of the emergency rescue efforts, the problem of road planning for emergency workers, vehicles, and other mobile objects should be overcome. Due to the characteristics of the emergency planning for rescue paths, timely, effective, and rapid efforts should be taken into effect based on geographical data in the road-free environment. This paper analyzes trafficability, researches both access ability and traffic efficiency, and uses traffic efficiency concerning the road-free environment as an influential weight and timeliness to improve the A^∗ algorithm, which realizes the rapid planning of the mobile objects' emergency rescue paths in the road-free environment, providing decision-making services for the rescue path planning of disaster emergencies in the environment of no road network. Finally, the experiments were carried out using available data for Shuozhou City, Shanxi Province. The results show that the research can provide a reference for the rescue path planning of disaster emergencies and has contributed to theoretical and applicable aspects of the research.

UAV Path Planning Based on Multi-Stage Constraint Optimization

Evolutionary Algorithms (EAs) based Unmanned Aerial Vehicle (UAV) path planners have been extensively studied for their effectiveness and high concurrency. However, when there are many obstacles, the path can easily violate constraints during the evolutionary process. Even if a single waypoint causes a few constraint violations, the algorithm will discard these solutions. In this paper, path planning is constructed as a multi-objective optimization problem with constraints in a three-dimensional terrain scenario. To solve this problem in an effective way, this paper proposes an evolutionary algorithm based on multi-level constraint processing (ANSGA-III-PPS) to plan the shortest collision-free flight path of a gliding UAV. The proposed algorithm uses an adaptive constraint processing mechanism to improve different path constraints in a three-dimensional environment and uses an improved adaptive non-dominated sorting genetic algorithm (third edition—ANSGA-III) to enhance the algorithm’s path planning ability in a complex environment. The experimental results show that compared with the other four algorithms, ANSGA-III-PPS achieves the best solution performance. This not only validates the effect of the proposed algorithm, but also enriches and improves the research results of UAV path planning.

Path Planning for Autonomous Ships: A Hybrid Approach Based on Improved APF and Modified VO Methods

In this research, a hybrid approach for path planning of autonomous ships that generates both global and local paths, respectively, is proposed. The global path is obtained via an improved artificial potential field (APF) method, which makes up for the shortcoming that the typical APF method easily falls into a local minimum. A modified velocity obstacle (VO) method that incorporates the closest point of approach (CPA) model and the International Regulations for Preventing Collisions at Sea (COLREGS), based on the typical VO method, can be used to get the local path. The contribution of this research is two-fold: (1) improvement of the typical APF and VO methods, making up for previous shortcomings, and integrated COLREGS rules and good seamanship, making the paths obtained more in line with navigation practice; (2) the research included global and local path planning, considering both the safety and maneuverability of the ship in the process of avoiding collision, and studied the whole process of avoiding collision in a relatively entirely way. A case study was then conducted to test the proposed approach in different situations. The results indicate that the proposed approach can find both global and local paths to avoid the target ship.