Unmanned Aerial Vehicle Path Research Articles

FC-RRT*: An Improved Path Planning Algorithm for UAV in 3D Complex Environment

In complex environments, path planning is the key for unmanned aerial vehicles (UAVs) to perform military missions autonomously. This paper proposes a novel algorithm called flight cost-based Rapidly-exploring Random Tree star (FC-RRT*) extending the standard Rapidly-exploring Random Tree star (RRT*) to deal with the safety requirements and flight constraints of UAVs in a complex 3D environment. First, a flight cost function that includes threat strength and path length was designed to comprehensively evaluate the connection between two path nodes. Second, in order to solve the UAV path planning problem from the front-end, the flight cost function and flight constraints were used to inspire the expansion of new nodes. Third, the designed cost function was used to guide the update of the parent node to allow the algorithm to consider both the threat and the length of the path when generating the path. The simulation and comparison results show that FC-RRT* effectively overcomes the shortcomings of standard RRT*. FC-RRT* is able to plan an optimal path that significantly improves path safety as well as maintains has the shortest distance while satisfying flight constraints in the complex environment. This paper has application value in UAV 3D global path planning.

Joint deployment and trajectory optimization in UAV-assisted vehicular edge computing networks

As the general mobile edge computing (MEC) scheme cannot adequately handle the emergency communication requirements in vehicular networks, unmanned aerial vehicle (UAV)-assisted vehicular edge computing networks (VECNs) are envisioned as the reliable and cost-efficient paradigm for the mobility and flexibility of UAVs. UAVs can perform as the temporary base stations to provide edge services for road vehicles with heavy traffic. However, it takes a long time and huge energy consumption for the UAV to fly from the stay charging station to the mission areas disorderly. In this paper, we design a pre-dispatch UAV-assisted VECNs system to cope with the demand of vehicles in multiple traffic jams. We propose an optimal UAV flight trajectory algorithm based on the traffic situation awareness. The cloud computing center (CCC) server predicts the real-time traffic conditions, and assigns UAVs to different mission areas periodically. Then, a flight trajectory optimization problem is formulated to minimize the cost of UAVs, while both the UAV flying and turning energy costs are mainly considered. In addition, we propose a deep reinforcement learning(DRL)-based energy efficiency autonomous deployment strategy, to obtain the optimal hovering position of UAV at each assigned mission area. Simulation results demonstrate that our proposed method can obtain an optimal flight path and deployment of UAV with lower energy consumption.

A Novel UAV Path Planning Algorithm Based on Double-Dynamic Biogeography-Based Learning Particle Swarm Optimization

Particle swarm optimization (PSO), one of the classical path planning algorithms, has been considered for unmanned aerial vehicle (UAV) path planning more frequently in recent years. A large amount of studies on UAV path planning based on modified PSO have been reported. However, most UAV path planning algorithms still optimize only one kind terrain problem which is mountain terrain. At the same time, many modified PSO algorithms also have some problems, such as insufficient convergence and unsatisfactory efficiency. In this paper, six kinds of terrain functions of UAV path planning are proposed to simulate real-world application. The terrain functions contain city, village without houses, village with houses, mountainous area without houses, mountainous area with houses, and mountainous area with a huge building. Inspired by CLPSO and BLPSO, we proposed a new double-dynamic biogeography-based learning particle swarm optimization (DDBLPSO) algorithm to solve these problems. The double-dynamic biogeography-based learning strategy replacing the traditional learning mechanism from the personal and global best particles is used to select the learning particles. In this strategy, each particle will learn from the better one of two selected particles which are not worse than itself. However, one random component of particle will replaced by corresponding component of other particle if all components of the particle only learn from itself. In this way, particles sufficiently learn from better objects and maintain the ability of jumping out of local optimality. The superiority of our algorithm is verified with four relevant algorithms, a PSO variant, and a BBO variant on the benchmark suite of CEC2015. Real-world application demonstrates that the algorithm we proposed outperforms four relevant algorithms, a PSO variant, and a BBO variant both in small-scale problems and large-scale problems. This paper shows a good application of our novel algorithm.

A decomposition-based constrained multi-objective evolutionary algorithm with a local infeasibility utilization mechanism for UAV path planning

Unmanned Aerial Vehicle (UAV) path planning problems can be treated as constrained multi-objective optimization problems, which often have complicated constraints in real-world scenarios. Algorithms for solving them require a powerful constraint-handling technique to utilize infeasible information. However, this has seldom been explored in this field. To remedy this issue, this paper proposes a decomposition-based constrained multi-objective evolutionary algorithm (M2M-DW) with a local infeasibility utilization mechanism for UAV path planning. Therein, M2M-DW is adopted as a solution optimizer since it can utilize infeasible individuals. However, this may result in poor performance due to the arbitrary use of infeasible individuals. To solve this issue, a local infeasibility utilization mechanism is proposed to effectively utilize the infeasible information. Besides, an improved mutation scheme is designed to further explore the promising regions. Experimental studies are conducted on three sets of UAV path planning problems with different difficulties, and the results highlight the effectiveness of the proposed algorithm in terms of reliability and stability in finding a set of feasible optimal solutions.

Path Planning for Multi-Vehicle-Assisted Multi-UAVs in Mobile Crowdsensing

Due to the capability of fast deployment and controllable mobility, unmanned aerial vehicles (UAVs) play an important role in mobile crowdsensing (MCS). However, constrained by limited battery capacity, UAVs cannot serve a wide area. In response to this problem, the ground vehicle is introduced and used to transport, release, and recycle UAVs. However, existing works only consider a special scenario: one ground vehicle with multiple UAVs. In this paper, we consider a more general scenario: multiple ground vehicles with multiple UAVs. We formalize the multi-vehicle-assisted multi-UAV path planning problem, which is a joint route planning and task assignment problem (RPTSP). To solve RPTSP, an efficient multi-vehicle-assisted multi-UAV path planning algorithm (MVP) is proposed. In MVP, we first allocate the detecting points to proper parking spots and then propose an efficient heuristic allocation algorithm EHA to plan the paths of ground vehicles. Besides, a genetic algorithm and reinforcement learning are utilized to plan the paths of UAVs. MVP maximizes the profits of an MCS carrier with a response time constraint and minimizes the number of employed vehicles. Finally, performance evaluation demonstrates that MVP outperforms the baseline algorithm.

Path planning in unmanned aerial vehicles: An optimistic overview

SummaryWith the development in the technology, a rapid increase in the use of unmanned aerial vehicle (UAV) is observed. UAVs are executing tasks that were previously performed by humans or manned aircraft, thus reducing the man workload and saving time. Path planning of UAVs is one of the most researched topics these days. Researchers are investigating more about UAVs and path planning to make it more feasible and economical. The major issue faced while planning a feasible path is to detect and avoid the obstacles encountered during a mission. This paper presents a detailed analysis of path planning in UAVs. Important aspects of path planning, that is, environment, dimensions, obstacles, and type and number of UAVs used in path planning, are also being discussed. Obstacle scenarios which UAV may come across during a mission and constraints which UAV has to follow for successful mission are briefly mentioned. Optimization techniques used to optimize the UAV path are also presented. In addition, future challenges and issues are also discussed.

UAV path design with connectivity constraint based on deep reinforcement learning

Cellular networks are expected to communicate effectively with unmanned aerial vehicles (UAVs) and support various applications. However, existing cellular networks are primarily designed to cover users on the ground; thus, coverage holes in the sky will exist. In this paper, we investigate the problem of path design for cellular-connected UAVs, taking into account the interruption performance throughout the UAV mission to minimize the completion time. Two types of connectivity constraints requirements are assumed to be available. The first is defined as the maximum continuous time interval that the UAV loses connection with base stations (BSs) below a predefined threshold. For the second, we consider the sum outage of UAV is limited during the entire UAV mission. The UAV is tasked with flying from a starting location to a final destination while minimization the mission time, satisfying the two constraints, separately. The formulated path design problem which involves continues variables and a dynamic radio environment, is not convex and thus is extremely difficult to solve directly. To tackle this challenge, a deep reinforcement learning (DRL) based trajectory design algorithm is proposed, where the Dueling Double Deep Q Network(Dueling DDQN) with multi-steps learning method is applied. Simulation results demonstrate the effectiveness of the proposed DRL algorithm and achieve a trade-off between the trajectory length of the UAV and connection quality.

UAV path planning and collision avoidance in 3D environments based on POMPD and improved grey wolf optimizer

Due to the complexity and uncertain factors of the environment, a 3D path planning algorithm is urgently needed. This paper presents a 3D optimal feasible flight path generation and collision avoidance algorithms based on partially observable Markov decision process (POMDP) and improved grey wolf optimizer (GWO) for an unmanned aerial vehicle (UAV). Firstly, a novel algorithm based on the GWO is proposed to deal with constrained optimization problem (COP) and utilized to plan a flyable path. The designed variant is called improved GWO with level comparison (GWOLC), which combines the communication mechanism and the ε-level comparison method at the same time. Secondly, aircraft collision avoidance is modeled as a Partially Observable Markov Decision Process (POMDP) and the Monte-Carlo tree search (MCTS) algorithm is used to solve it. We introduce a novel algorithm, Information Particle Filter Tree (IPFT), to solve the problem of belief update in continuous domain. Thirdly, simulation experiments are conducted in 3D environment, and numerical results showed the proposed algorithm offers good performance as measured by effectiveness, robustness, convergence, and constraint handling capabilities.

A Two-Stage Approach of Joint Route Planning and Resource Allocation for Multiple UAVs in Unmanned Logistics Distribution

Unmanned aerial vehicles (UAVs) can serve as means of delivery to enhance the effectiveness and accuracy of logistics distribution in various scenarios, such as emergency items delivery for disaster areas and logistics services for remote areas. This paper focuses on the problem of route planning for UAVs, which is the basis for UAVs to complete distribution tasks. Besides, to enhance the collaboration of UAVs during the delivery mission, this work also studies wireless resource (spectrum, power) allocation problem for multiple UAVs enabled communication networks. Due to the non-convex and combinatorial characteristics, it is challenging to obtain an optimal strategy for joint route planning and resource allocation issues within a finite time. To this end, the paths of UAVs are planned by the simulated annealing (SA) method in the first stage. Based on the preplanned paths, a double deep Q network (DDQN) based resource allocation method is proposed to maximize the average sum capacity of UAV-to-UAV (U2U) links and reduce transmission delay while minimizing the interference to UAV-to-infrastructure (U2I) links. Multi-UAV communication networks are reconnected according to the positions of UAVs changing in each time slot. Then each U2U link acting as an agent learns to improve spectrum and power allocation policy with imperfect knowledge of the environment. Simulation results demonstrate that the proposed DDQN-based resource management scheme can achieve higher system communication capacity than a random scheme. Moreover, the successful transmission probability of U2U links obtained by the DDQN-based method is much higher than a Particle Swarm Optimization (PSO) based method.

Improved Artificial Bee Colony Algorithm Based on Multi-Strategy Synthesis for UAV Path Planning

Path planning is the key for unmanned aerial vehicle (UAV) to perform tasks efficiently, which needs to quickly obtain the optimal path in the complex environment. To solve the path planning problem in the complex urban environment, an improved artificial bee colony algorithm based on multi-strategy synthesis (IABC) is proposed to generate the appropriate path for the UAV. The IABC is based on the hybrid mechanism of chaotic mapping and Pareto principle to initialize the population, so as to fully search the solution space and provide an approximate optimal flight path for UAV. Meanwhile, in order to balance exploration and development, two new search equations are designed to generate candidate solutions, which provide more superior flight paths for UAVs. In addition, tangent random evolution mechanism is added to enhance the strategy of updating the algorithm offspring, maximize the quality of flight path generation, and effectively solve the path planning problem. Aiming at the complex urban environment and the multi-constraint optimization problem of UAV flight, the IABC is combined with cubic spline interpolation to generate a smooth path to meet the UAV flight maneuver characteristics. The improved algorithm proposed in this paper has been compared with Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Search Artificial Bee Colony (SABC) and Gbest Artificial Bee Colony (GABC). The algorithm proposed in this paper has good feasibility and effectiveness in solving the UAV path planning problem.

A Multi-Objective Quantum-Inspired Seagull Optimization Algorithm Based on Decomposition for Unmanned Aerial Vehicle Path Planning

The traditional seagull optimization algorithm cannot handle multi-objective optimization problems, so a multi-objective quantum-inspired seagull optimization algorithm based on decomposition (MOQSOA/D) is proposed. Multi-objective computing and quantum computing are introduced into MOQSOA/D. MOQSOA/D transforms the multi-objective problem into multiple scalar optimization sub-problems, and establishes a dynamic archive and a leadership archive at the same time. The Pareto solution of each sub-problem is stored in a dynamic archive, and the non-dominated Pareto solution is stored in the leader archive. While processing each sub-problem, each seagull is represented by a string of qubits, which is used to calculate the current seagull direction of flight, and a variable angular-distance rotation (VAR) gate is used to change the probability amplitude of the qubits, thereby updating the direction of flight. Penalty-based boundary intersection approach is introduced to determine whether the generated Pareto solution is retained. The proposed algorithm and six different algorithms were tested on 69 indicators, and the results show that the algorithm achieved better results in 40 indicators. In addition, a Unmanned Aerial Vehicle (UAV) path planning model in three-dimensional environment is designed to test the utility of MOQSOA/D, and the algorithm is compared with the other algorithm to demonstrate its effectiveness.

Game Theory-Based Optimal Cooperative Path Planning for Multiple UAVs

This paper presents new cooperative path planning algorithms for multiple unmanned aerial vehicles (UAVs) using Game theory-based particle swarm optimization (GPSO). First, the formation path planning is formulated into the minimization of a cost function that incorporates multiple objectives and constraints for each UAV. A framework based on game theory is then developed to cast the minimization into the problem of finding a Stackelberg-Nash equilibrium. Next, hierarchical particle swarm optimization algorithms are developed to obtain the global optimal solution. Simulation results show that the GPSO algorithm can generate efficient and feasible flight paths for multiple UAVs, outperforming other path planning methods in terms of convergence rate and flexibility. The formation can adjust its geometrical shape to accommodate a working environment. Experimental tests on a group of three UAVs confirm the advantages of the proposed approach for a practical application.

Automatic Carrier Landing Control with External Disturbance and Input Constraint

The automatic carrier landing problem of the unmanned aerial vehicle (UAV) is studied in this paper, where external disturbance and input constraint are considered. Deck motion prediction is necessary in classical automatic carrier landing system (ACLS), which is difficult to compensate precisely due to the motion is primarily stochastic. Thus, a relative motion model between the UAV and ideal glide path is established, by which the disturbance of the wave to the carrier is regarded as external disturbance to the model as well as the airwake to the UAV, and the problem of trajectory tracking is transformed into a stabilization problem. Moreover, non-singular fast terminal sliding mode observer (NFTSMO) is deigned to estimate disturbance, and it is proved that observed error can converge to residual in fixed time. In addition, an ACLS based on backstepping control structure is proposed, where input constraint is considered, and the closed-loop control system is proved to be stable by Lyapunov function. Finally, numerical simulations are performed to demonstrate reliability and superiority of the proposed ACLS.

An approach to UAV behaviour classification based on spatial analysis of ADS-B flight data

Prohibited/unauthorized operations of Unmanned Aerial Vehicles (UAV) over civilian aircraft flight space can present safety hazards. We present an approach for classifying UAV flights based on spatial analysis of flight spaces consisting of historical data from commercial aircraft flights, collected using Automatic Dependent Surveillance–Broadcast (ADS–B). We compare the flight path of UAVs with that of random UAV flights, using metrics and techniques derived from spatial analysis and point process theory, to classify UAVs as malicious or not.

Cooperative Conflict Detection and Resolution and Safety Assessment for 6G Enabled Unmanned Aerial Vehicles

The increasing number of Unmanned Aerial Vehicles (UAVs) in the low-altitude airspace and the increasing complexity of the work environment present new challenges for ensuring airspace security, especially the effective conflict detection and resolution (CD&R) of UAVs. In the era of the sixth generation (6G) technology, there is an improvement in communication speed and capacity in comparison with the traditional communication technologies, which contributes to forming a UAV Internet of Things (IoTs) through remote intelligent control platform and improve the effect of CD&R. In this paper, we innovatively develop a cooperative CD&R method in the UAV IoT environment considering UAV relative motion relationships and UAV priorities. Using this method in 6G environment, the real-time and reactive conflict-free paths for UAVs can be generated. The developed method has the advantage of smaller calculation and needs fewer UAVs to take maneuvers than the CD&R methods based on traditional Artificial Potential Field (APF). To verify the effectiveness of CD&R methods, a safety assessment method (evaluate from both conflict feature and network structure perspectives) is also proposed. A Monte Carlo Simulation with ``clone mechanism'' is designed to incorporate the effect of CD&R systems. Three cases of distributed CD&R protocols are simulated and compared. The simulations with different parameter settings are also discussed. Quantitative simulation experiments show that the safety effect of CD&R proposed in this paper is improved a lot due to the improved APF and the UAV priority determination. Meanwhile, the safety assessment method is demonstrated to be feasible for evaluating the safety of CD&R systems.

UAV path planning based on an elite-guided orthogonal diagonalised krill herd algorithm

At present, obstacle avoidance and the shortest path to the destination are the main problems that need to be considered when the UAV (unmanned aerial vehicle) performs its mission. In this paper, an Elite-Guided Orthogonal Diagonalised Krill Herd (EODKH) algorithm is developed to solve the path planning problem of UAV in three-dimensional space with complex terrain. EODKH algorithm divides the krill population into two parts: one part is composed of elite krill, and the evolution of krill population is guided by the experience of elite krill with orthogonal diagonalisation, and the elite krill with higher rank can obtain greater 'respect' in the evolution procedure; the other part evolves according to the original krill herd method, so as to improve the convergence diversity of the whole krill herd. Experimental results show that EODKH is better than other krill herd algorithms in UAV path planning.

Extending Delivery Range and Decelerating Battery Aging of Logistics UAVs using Public Buses

The battery-powered Unmanned Aerial Vehicle (UAV) is a promising alternative to traditional logistics trucks. Using UAVs can achieve much more speedy, cost-effective, and environment-friendly delivery on an urban scale. However, UAVs suffer from insufficient delivery range and battery aging. This paper presents an innovative logistics UAV scheduling framework using public buses, in which logistics UAVs Land and Recharge its battery on Buses (ULRB) to extend its delivery range and decelerate its fading battery capacity. This work correlates physical layer parameters such as the energy consumption rate, the parcels weight, UAV velocity, the battery temperature to the UAVs path planning, the battery discharging, and the capacity fading models. Specifically, the ULRB framework consists of a single-UAV scheduling module and a multi-UAV dispatching module. In the single-UAV module, a Markov-based algorithm is utilized to plan the UAVs flying path to land and dynamically get recharged on the bus. The latter module optimized the delivery progress in a multi-UAV, multi-parcel, and multi-bus scenario. Finally, using a large-scale real-world bus trajectory dataset, extensive evaluations are conducted to verify ULRB. The results show that ULRB can extend the UAVs delivery range by 5.54 and decelerate the battery aging by 3.26 on average.

Quality-Oriented Hybrid Path Planning Based on A* and Q-Learning for Unmanned Aerial Vehicle

Unmanned aerial vehicles (UAVs) are playing an increasingly important role in people’s daily lives due to their low cost of operation, low requirements for ground support, high maneuverability, high environmental adaptability, and high safety. Yet UAV path planning under various safety risks, such as crash and collision, is not an easy task, due to the complicated and dynamic nature of path environments. Therefore, developing an efficient and flexible algorithm for UAV path planning has become inevitable. Aimed at quality-oriented UAV path planning, this paper is designed to analyze UAV path planning from two aspects: global static planning and local dynamic hierarchical planning. Through a theoretical and mathematical approach, a three-dimensional UAV path planning model was established. Based on the A* algorithm, the search strategy, the step size, and the cost function were improved, and the OPEN set was simplified, thereby shortening the planning time and greatly improving the execution efficiency of the algorithm. Moreover, a dynamic exploration factor was added to the exploration mechanism of Q-learning to solve the exploration-exploitation dilemma of Q-learning to adapt to the local dynamic path adjustment for UAVs. The global-local hybrid UAV path planning algorithm was formed by combining the two. The simulation results indicate that the proposed planning model and algorithm can efficiently solve the problem of UAV path planning, improve the path quality, and can be a significant reference for solving other problems related to path planning, such as the reliability, security, and safety of UAV, when embedded into the heuristic function of the proposed algorithm.

Wireless Sensor Network Informed UAV Path Planning for Soil Moisture Mapping

Adaptive and targeted allocation of mobile sensing agents, in the form of unmanned aerial vehicles (UAVs) with software defined radar (UAV-SDRadar) payloads, enable mapping of surface soil moisture in regions where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> wireless sensor networks (WSNs) undersample soil moisture or upscaling models perform poorly. This work presents an optimization-based UAV path planning methodology that seeks to maximize UAV flight coverage over areas where a complementing WSN yields upscaled soil moisture estimates with high uncertainty. By recursively mapping soil moisture over such areas, the combined UAV and WSN instrumentation can gradually capture the domain’s true mean soil moisture. A series of numerical simulations are presented to demonstrate the algorithm’s basic function while considering real-world and feasible operational scenarios.

Radio Map Assisted Path Planning for UAV Anti-Jamming Communications

The communications quality of unmanned aerial vehicle (UAV) has been brought into focus increasingly, which can be severely affected by other users, as well as complex topography. In this letter, we propose a path planning algorithm for UAV anti-jamming communications with the joint assistance of two kinds of radio maps—signal power map and signal to interference plus noise ratio (SINR) map, which contain the abundant information of path loss caused by the topography and interference from jammers. We show how to generate these maps from real topographic data, and provide the optimal UAV path planning by adopting the power and the SNR maps to the heuristic and graphic based A* algorithm. Simulations with various settings for areas with complex topography in real world, show that the proposed algorithm can considerably improve the efficiency by minimizing the UAV flying distance for anti-jamming communications.