Unmanned Aerial Vehicle Path Research Articles

Cooperative Path Planning for Multi-UAVs with Time-Varying Communication and Energy Consumption Constraints

In the field of Unmanned Aerial Vehicle (UAV) path planning, designing efficient, safe, and feasible trajectories in complex, dynamic environments poses substantial challenges. Traditional optimization methods often struggle to address the multidimensional nature of these problems, particularly when considering constraints like obstacle avoidance, energy efficiency, and real-time responsiveness. In this paper, we propose a novel algorithm, Dimensional Learning Strategy and Spherical Motion-based Particle Swarm Optimization (DLS-SMPSO), specifically designed to handle the unique constraints and requirements of cooperative path planning for Multiple UAVs (Multi-UAVs). By encoding particle positions as motion paths in spherical coordinates, the algorithm offers a natural and effective approach to navigating multidimensional search spaces. The incorporation of a Dimensional Learning Strategy (DLS) enhances performance by minimizing particle oscillations and allowing each particle to learn valuable information from the global best solution on a dimension-by-dimension basis. Extensive simulations validate the effectiveness of the DLS-SMPSO algorithm, demonstrating its capability to consistently generate optimal paths. The proposed algorithm outperforms other metaheuristic optimization algorithms, achieving a feasibility ratio as high as 97%. The proposed solution is scalable, adaptable, and suitable for real-time implementation, making it an excellent choice for a broad range of cooperative multi-UAV applications.

UAV-Enabled Diverse Data Collection via Integrated Sensing and Communication Functions Based on Deep Reinforcement Learning

Unmanned aerial vehicles (UAVs) and drones are considered to represent a flexible mobile aerial platform to collect data in various applications. However, the existing data collection methods mainly consider uplink communication. The burgeoning development of integrated sensing and communication (ISAC) provides a new paradigm for data collection. A diverse data collection framework is established where the uplink communication and sensing functions are both considered, which can also be referred to as the uplink ISAC system. An optimization is formulated to minimize the data freshness indicator for communication and the detection freshness indicator for sensing by optimizing the UAV paths, the transmitted power of IoT devices and UAVs, and the transmission allocation indicators. Three state-of-the-art deep reinforcement learning (DRL) algorithms are utilized to solve this optimization. Experiments are conducted in both single-UAV and multi-UAV scenarios, and the results demonstrate the effectiveness of the proposed algorithms. In addition, the proposed algorithms outperform the benchmark in terms of accuracy and efficiency. Moreover, the effectiveness of the data collection mode with only communication or sensing functions is also verified. Also, the numerical Pareto front between communication and sensing performance is obtained by adjusting the importance parameter.

A Hybrid ARO Algorithm and Key Point Retention Strategy Trajectory Optimization for UAV Path Planning

Path planning is a fundamental research issue for enabling autonomous flight in unmanned aerial vehicles (UAVs). An effective path planning algorithm can greatly improve the operational efficiency of UAVs in complex environments like urban and mountainous areas, thus offering more extensive coverage for various tasks. However, existing path planning algorithms often encounter problems such as high computational costs and a tendency to become trapped in local optima in complex 3D environments with multiple constraints. To tackle these problems, this paper introduces a hybrid multi-strategy artificial rabbits optimization (HARO) for efficient and stable UAV path planning in complex environments. To realistically simulate complex scenarios, we introduce spherical and cylindrical obstacle models. The HARO algorithm balances exploration and exploitation phases using a dual exploration switching strategy and a population migration memory mechanism, enhancing search performance and avoiding local optima. Additionally, a key point retention trajectory optimization strategy is proposed to reduce redundant path points, thus lowering flight costs. Experimental results confirm the HARO algorithm’s superior search performance, planning more efficient and stable paths in complex environments. The key point retention strategy effectively reduces flight costs during trajectory optimization, thereby enhancing adaptability.

Dual-path differential perturbation sand cat swarm optimization algorithm integrated with escape mechanism

In this paper, based on the sand cat swarm optimization (SCSO) algorithm, a dual-path differential perturbation sand cat swarm optimization algorithm integrated with escape mechanism (EDSCSO) is proposed. EDSCSO aims to solve the problems of the original SCSO, such as the limited diversity of the population, low efficiency of solving complex functions, and ease of falling into a local optimal solution. First, an escape mechanism was proposed to balance the exploration and exploitation of the algorithm. Second, a random elite cooperative guidance strategy was used to utilize the elite population to guide the general population to improve the convergence speed of the algorithm. Finally, the dual-path differential perturbation strategy is used to continuously perturb the population using two differential variational operators to enrich population diversity. EDSCSO obtained the best average fitness for 27 of 39 test functions in the IEEE CEC2017 and IEEE CEC2019 test suites, indicating that the algorithm is an efficient and feasible solution for complex optimization problems. In addition, EDSCSO is applied to optimize the three-dimensional wireless sensor network coverage as well as the unmanned aerial vehicle path planning problem, and it provides optimal solutions for both problems. The applicability of EDSCSO in real-world optimization scenarios was verified.

Deep Reinforcement Learning for UAV-Based SDWSN Data Collection

Recent advancements in Unmanned Aerial Vehicle (UAV) technology have made them effective platforms for data capture in applications like environmental monitoring. UAVs, acting as mobile data ferries, can significantly improve ground network performance by involving ground network representatives in data collection. These representatives communicate opportunistically with accessible UAVs. Emerging technologies such as Software Defined Wireless Sensor Networks (SDWSN), wherein the role/function of sensor nodes is defined via software, can offer a flexible operation for UAV data-gathering approaches. In this paper, we introduce the “UAV Fuzzy Travel Path”, a novel approach that utilizes Deep Reinforcement Learning (DRL) algorithms, which is a subfield of machine learning, for optimal UAV trajectory planning. The approach also involves the integration between UAV and SDWSN wherein nodes acting as gateways (GWs) receive data from the flexibly formulated group members via software definition. A UAV is then dispatched to capture data from GWs along a planned trajectory within a fuzzy span. Our dual objectives are to minimize the total energy consumption of the UAV system during each data collection round and to enhance the communication bit rate on the UAV-Ground connectivity. We formulate this problem as a constrained combinatorial optimization problem, jointly planning the UAV path with improved communication performance. To tackle the NP-hard nature of this problem, we propose a novel DRL technique based on Deep Q-Learning. By learning from UAV path policy experiences, our approach efficiently reduces energy consumption while maximizing packet delivery.

Improved A-STAR Algorithm for Power Line Inspection UAV Path Planning

The operational areas for unmanned aerial vehicles (UAVs) used in power line inspection are highly complex; thus, the best path planning under known obstacles is of significant research value for UAVs. This paper establishes a three-dimensional spatial environment based on the gridding and filling of two-dimensional maps, simulates a variety of obstacles, and proposes a new optimization algorithm based on the A-STAR algorithm, considering the unique dynamics and control characteristics of quadcopter UAVs. By utilizing a novel heuristic evaluation function and uniformly applied quadratic B-spline curve smoothing, the planned path is optimized to better suit UAV inspection scenarios. Compared to the traditional A-STAR algorithm, this method offers improved real-time performance and global optimal solution-solving capabilities and is capable of planning safer and more realistic flight paths based on the operational characteristics of quadcopter UAVs in mountainous environments for power line inspection.

Optimal trajectories for UAV three-dimensional path planning using a hybrid ABC-RRT* algorithm

Path planning is critical to the effective operation of unmanned aerial vehicles (UAVs) in complex environments. It is crucial to quickly determine the best path for UAV navigation. In this paper, a novel approach for unmanned aerial vehicle (UAV) path planning is presented by combining the robust artificial bee colony (ABC) algorithm with the flexible rapidly exploring random tree star (RRT*) algorithm. The main objective of this approach is to ensure effective obstacle avoidance. The proposed hybrid algorithm, ABC-RRT*, is compared with several current approaches, namely Improved ABC (IABC), ABC, Particle Swarm Optimization (PSO), ABC-PSO and RRT. ABC-RRT* combines the exploration capabilities of ABC with the route optimization capabilities of RRT* to efficiently determine the best trajectories for UAVs by balancing exploration and exploitation. ABC integration increases the effectiveness of exploration, while RRT* excels at identifying shorter paths and adapting to different conditions. ABC-RRT* has been shown to outperform other methods in terms of path length and flexibility when traversing complicated terrain with many obstacles. This was proven by extensive simulations in various situations. The results demonstrate the effectiveness, fast convergence and robustness of ABC-RRT* and make it a viable solution for obstacle avoidance of UAVs in practice. The fusion of ABC and RRT* for obstacle avoidance shows great potential for UAV route planning and offers remarkable advantages compared to conventional approaches.

Extremum Seeking-Based Radio Signal Strength Optimization Algorithm for Hoverable UAV Path Planning

For the safe autonomous operations of unmanned aerial vehicles (UAVs) and ground control stations (GCS), including autonomous battery replacement, wireless power transfer, and more, the precise landing of UAVs on GCS is essential. Accurate landing is only possible when the link capacity strength exceeds a certain threshold, but this is often disturbed due to complex terrain. To address this, we developed an extremum seeking (ES)-based radio signal strength optimization (RSSO) algorithm, ES-RSSO, designed to find the optimal positions of the UAV using radio communication signals. This ensures energy-efficient path planning while guaranteeing the minimum received signal strength indication (RSSI) capacity. This algorithm is particularly useful in obstacle-rich environments, where UAVs are limited in power resources. Simulation results demonstrate a 2.37% decrease in the mean, a 62.08% improvement in variance, and a 3.72% decrease in the integration strength of the link capacity when ES-RSSO is applied. These results confirm that the RADIO.rssi maintenance ability remains above a critical boundary level, supporting robust communication links and energy-efficient path planning. Throughout the study, we showed how, in many cases, simply moving the UAV a few meters can significantly improve the communication link.

Learning to construct a solution for UAV path planning problem with positioning error correction

Unmanned aerial vehicles (UAVs) are advanced flight systems. However, their positioning systems cause distance-dependent errors during flight. This study seeks to solve the UAV path planning problem with positioning error correction (UPEC) with an end-to-end method. Traditional methods struggle to balance solution quality and computational overload, and often have limited utilisation of scenario information. To overcome these issues, we propose a path planning model (PPM) based on deep reinforcement learning to solve the UPEC. The model has a complete structure that includes a mathematical model, feature engineering, solution process, neural policy network, scenario generation, training process, and test solution mechanism. Specifically, we first establish a Markov decision process (MDP) for UPEC and apply feature engineering with effective features to support decision-making. We then introduce a path planning neural network (PPNN) to represent the MDP policy. Based on the dataset generated from the multi-rule combination validation, we train the PPNN using the proposed RL algorithm with storage pool. Furthermore, we propose a backtracking mechanism to guarantee solution feasibility during the construction process. Extensive experiments demonstrate that the proposed PPM outperforms existing state-of-the-art algorithms in terms of solution quality and timeliness, and the backtracking mechanism effectively improves the scenario completion rate. The model study indicates the efficacy of our training algorithm and the generalisation of the PPNN. Additionally, our construction process is problem-tailored and more suitable for addressing UPEC than iterative search algorithms, because it effectively mitigates the impact of invalid nodes.

A multi-objective cat swarm optimization algorithm based on two-archive mechanism for UAV 3-D path planning problem

Achieving a balance between convergence and diversity is crucial in addressing multi-objective optimization problems (MOPs). In this paper, a multi-objective cat swarm optimization based on a new two-archive mechanism (MOCSO_TA) is proposed for the above challenge. In this approach, solutions that can promote convergence are stored by the convergence archive (CA). While solutions that can enhance diversity in the population are saved in the diversity archive (DA). Path planning is a critical process for unmanned aerial vehicles (UAVs), involving the identification of a route that is short and secure. Multi-objective algorithms have become a crucial approach for addressing UAV path planning problem, thus motivating the use of the proposed MOCSO_TA in path planning problem. The proposed algorithm is compared with two sets of representative multi-objective algorithms on the DTLZ, WFG, and ZDT benchmark problems. The experimental results demonstrate the outstanding performance of MOCSO_TA. The effectiveness of the MOCSO_TA is demonstrated by designing two terrains and comparing it with various multi-objective algorithms. The results confirmed the superiority of MOCSO_TA.

Small Fixed-Wing Unmanned Aerial Vehicle Path Following Under Low Altitude Wind Shear Disturbance

Small Fixed-Wing Unmanned Aerial Vehicle Path Following Under Low Altitude Wind Shear Disturbance

Somersault Foraging and Elite Opposition-Based Learning Dung Beetle Optimization Algorithm

To tackle the shortcomings of the Dung Beetle Optimization (DBO) Algorithm, which include slow convergence speed, an imbalance between exploration and exploitation, and susceptibility to local optima, a Somersault Foraging and Elite Opposition-Based Learning Dung Beetle Optimization (SFEDBO) Algorithm is proposed. This algorithm utilizes an elite opposition-based learning strategy as the method for generating the initial population, resulting in a more diverse initial population. To address the imbalance between exploration and exploitation in the algorithm, an adaptive strategy is employed to dynamically adjust the number of dung beetles and eggs with each iteration of the population. Inspired by the Manta Ray Foraging Optimization (MRFO) algorithm, we utilize its somersault foraging strategy to perturb the position of the optimal individual, thereby enhancing the algorithm’s ability to escape from local optima. To verify the effectiveness of the proposed improvements, the SFEDBO algorithm is utilized to optimize 23 benchmark test functions. The results show that the SFEDBO algorithm achieves better solution accuracy and stability, outperforming the DBO algorithm in terms of optimization results on the test functions. Finally, the SFEDBO algorithm was applied to the practical application problems of pressure vessel design, tension/extension spring design, and 3D unmanned aerial vehicle (UAV) path planning, and better optimization results were obtained. The research shows that the SFEDBO algorithm proposed in this paper is applicable to actual optimization problems and has better performance.

Multi-UAV Path Planning for Inspection of Target Points with Stable Monitoring Frequencies

In this study, we focus on the path-planning problem of unmanned aerial vehicles (UAVs) deployed for inspection missions at target points. The goal is to visit each target point, provide revisits to important target points, and ultimately meet the monitoring requirements with regular and stable monitoring frequencies. Herein, we present MTSP-R, a novel variant of the multiple traveling salesmen problem (MTSP), in which revisits to important target points are allowed. We address the path-planning problem of multi-UAV in two stages. First, we propose a nearest insertion algorithm with revisits (NIA-R) to determine the number of required UAVs and initial inspection paths. We then propose an improved genetic algorithm (IGA) with two-part chromosome encoding to further optimize the inspection paths of the UAVs. The simulation results demonstrate that the IGA can effectively overcome the shortcomings of the original genetic algorithm, providing shorter paths for multiple UAVs and more stable monitoring frequencies for the target points.

Advancements in UAV Path Planning: A Deep Reinforcement Learning Approach with Soft Actor-Critic for Enhanced Navigation

This paper tackles the intricate challenge of autonomous navigation for Unmanned Aerial Vehicles (UAVs) through dynamically changing environments. We focus on a sophisticated Deep Reinforcement Learning (DRL) approach using the Soft Actor-Critic (SAC) algorithm, optimized for UAV path planning within a continuous action space. This methodology leverages environmental image data to enhance the precision of flight maneuvers and effective obstacle avoidance. Our approach, validated through extensive simulations in Gazebo and field tests, demonstrates the algorithm’s efficacy in enabling UAVs to adeptly navigate obstacles using depth maps. The study further explores the robustness of the SAC algorithm by comparing it with traditional DRL methods, emphasizing its superior performance in real-world applications. This research contributes significantly to advancing UAV technology, particularly in autonomous motion planning, by integrating cutting-edge machine learning techniques. The video link is: https://www.youtube.com/watch?v=Nd_aMzejNXY .

MISAO: A Multi-Strategy Improved Snow Ablation Optimizer for Unmanned Aerial Vehicle Path Planning

The snow ablation optimizer (SAO) is a meta-heuristic technique used to seek the best solution for sophisticated problems. In response to the defects in the SAO algorithm, which has poor search efficiency and is prone to getting trapped in local optima, this article suggests a multi-strategy improved (MISAO) snow ablation optimizer. It is employed in the unmanned aerial vehicle (UAV) path planning issue. To begin with, the tent chaos and elite reverse learning initialization strategies are merged to extend the diversity of the population; secondly, a greedy selection method is deployed to retain superior alternative solutions for the upcoming iteration; then, the Harris hawk (HHO) strategy is introduced to enhance the exploitation capability, which prevents trapping in partial ideals; finally, the red-tailed hawk (RTH) is adopted to perform the global exploration, which, enhances global optimization capability. To comprehensively evaluate MISAO’s optimization capability, a battery of digital optimization investigations is executed using 23 test functions, and the results of the comparative analysis show that the suggested algorithm has high solving accuracy and convergence velocity. Finally, the effectiveness and feasibility of the optimization path of the MISAO algorithm are demonstrated in the UAV path planning project.

CGull: A Non-Flapping Bioinspired Composite Morphing Drone.

Despite the tremendous advances in aircraft design that led to successful powered flights of aircraft as heavy as the Antonov An-225 Mriya, which weighs 640 tons, or as fast as the NASA-X-43A, which reached a record of Mach 9.6, many characteristics of bird flight have yet to be utilized in aircraft designs. These characteristics enable various species of birds to fly efficiently in gusty environments and rapidly change their momentum in flight without having modern thrust vector control (TVC) systems. Vultures and seagulls, as examples of expert gliding birds, can fly for hours, covering more than 100 miles, without a single flap of their wings. Inspired by the Great Black-Backed Gull (GBBG), this paper presents "CGull", a non-flapping unmanned aerial vehicle (UAV) with wing and tail morphing capabilities. A coupled two degree-of-freedom (DOF) morphing mechanism is used in CGull's wings to sweep the middle wing forward and the outer feathered wing backward, replicating the GBBG's wing deformation. A modular two DOF mechanism enables CGull to pitch and tilt its tail. A computational model was first developed in MachUpX to study the effects of wing and tail morphing on the generated forces and moments. Following the biological construction of birds' feathers and bones, CGull's structure is mainly constructed from carbon-fiber composite shells. The successful flight test of the proof-of-concept physical model proved the effectiveness of the proposed morphing mechanisms in controlling the UAV's path.

An Adaptive Spiral Strategy Dung Beetle Optimization Algorithm: Research and Applications.

The Dung Beetle Optimization (DBO) algorithm, a well-established swarm intelligence technique, has shown considerable promise in solving complex engineering design challenges. However, it is hampered by limitations such as suboptimal population initialization, sluggish search speeds, and restricted global exploration capabilities. To overcome these shortcomings, we propose an enhanced version termed Adaptive Spiral Strategy Dung Beetle Optimization (ADBO). Key enhancements include the application of the Gaussian Chaos strategy for a more effective population initialization, the integration of the Whale Spiral Search Strategy inspired by the Whale Optimization Algorithm, and the introduction of an adaptive weight factor to improve search efficiency and enhance global exploration capabilities. These improvements collectively elevate the performance of the DBO algorithm, significantly enhancing its ability to address intricate real-world problems. We evaluate the ADBO algorithm against a suite of benchmark algorithms using the CEC2017 test functions, demonstrating its superiority. Furthermore, we validate its effectiveness through applications in diverse engineering domains such as robot manipulator design, triangular linkage problems, and unmanned aerial vehicle (UAV) path planning, highlighting its impact on improving UAV safety and energy efficiency.

Research, Analysis, and Improvement of Unmanned Aerial Vehicle Path Planning Algorithms in Urban Ultra-Low Altitude Airspace

Urban ultra-low altitude airspace (ULAA) presents unique challenges for unmanned aerial vehicle (UAV) path planning due to high building density and regulatory constraints. This study analyzes and improves classical path planning algorithms for UAVs in ULAA. Experiments were conducted using A*, RRT, RRT*, and artificial potential field (APF) methods in a simulated environment based on building data from Chengdu City, China. Results show that traditional algorithms struggle in dense obstacle environments, particularly APF due to local minima issues. Enhancements were proposed: a density-aware heuristic for A*, random perturbation for APF, and a hybrid optimization strategy for RRT*. These modifications improved computation time, path length, and obstacle avoidance. The study provides insights into the limitations of classical algorithms and suggests enhancements for more effective UAV path planning in urban environments.

Evolutionary computation for unmanned aerial vehicle path planning: a survey

Unmanned aerial vehicle (UAV) path planning aims to find the optimal flight path from the start point to the destination point for each aerial vehicle. With the rapid development of UAV technology, UAVs are required to tackle missions in increasingly complex environments. Consequently, UAV path planning encounters more challenges, causing traditional deterministic algorithms to struggle to find the optimal path within a certain time. Evolutionary computation (EC) is a series of nature-inspired methodologies and algorithms, which have shown effectiveness and efficiency in solving many complex optimization problems in real-world applications. Recently, EC algorithms have been effectively applied in UAV path planning and have shown encouraging performance in obtaining high-quality solutions. Therefore, it is crucial to review the related research experience and literature in the field of using EC for UAV path planning. This paper presents a comprehensive survey to showcase the existing studies on EC in UAV path planning, especially in complex environments. The paper first proposes a novel taxonomy to categorize the relevant studies into three different categories according to the complex environmental properties of the application scenarios. These environmental properties include complex search space, complex time control, and complex optimization objectives. Then, the EC algorithms for UAV path planning in these complex environments are further systematically surveyed as constrained search space and large-scale search space in complex search space, dynamic UAV path planning and multi-UAV concurrent path planning in complex time control, and expensive objective and multiple objectives in complex optimization objectives. Finally, some potential future research directions for applying EC algorithms to UAV path planning are presented and discussed.

Joint resource scheduling and flight path planning of UAV-assisted IoTs in response to emergencies

In unmanned aerial vehicles (UAV)-assisted Internet of Things (IoT), emergencies can lead to changes in the status of ground sensor networks. This necessitates UAV-assisted IoT systems to possess the capability to dynamically respond to changes in the status of the ground sensor network. Therefore, this paper proposes a UAV scheduling scheme based on regional coordination (USRC). In this scheme, we divide the ground sensor network into task sub-regions according to the deployment of base stations. Then, we introduce a scheduling relationship pairing algorithm to determine the scheduling relationships between task sub-regions and UAV resources that need to be scheduled for each sub-region. Based on this, a dynamic path planning algorithm is designed to synchronize the planning of cross-region flight paths and intra-region flight paths for UAVs. Experimental results have demonstrated that the proposed scheme can efficiently respond to changes in the status of the ground sensor network by scheduling UAVs from sub-regions with abundant resources to those with scarce resources. Compared to other schemes, our scheme exhibits superior performance in reducing the age of information (AoI) and packet loss rate.