Optimized charging-station placement and UAV trajectory for enhanced uncertain target detection in intelligent UAV tracking systems

This study explores the application of Particle Swarm Optimization (PSO) in Unmanned Aerial Vehicle (UAV) path planning within a simulated three-dimensional environment. UAVs, increasingly prevalent across various sectors, demand efficient navigation solutions that account for dynamic and unpredictable elements. Traditional pathfinding algorithms often fall short in complex scenarios, hence the shift towards PSO, a bio-inspired algorithm recognized for its adaptability and robustness. We developed a Python-based framework to simulate the UAV path planning scenario. The PSO algorithm was tasked to navigate a UAV from a starting point to a predetermined destination while avoiding spherical obstacles. The environment was set within a 3D grid with a series of waypoints, marking the UAV's trajectory, generated by the PSO to ensure obstacle avoidance and path optimization. The PSO parameters were meticulously tuned to balance the exploration and exploitation of the search space, with an emphasis on computational efficiency. A cost function penalizing proximity to obstacles guided the PSO in real-time decision-making, resulting in a collision-free and optimized path. The UAV's trajectory was visualized in both 2D and 3D perspectives, with the analysis focusing on the path's smoothness, length, and adherence to spatial constraints. The results affirm the PSO's effectiveness in UAV path planning, successfully avoiding obstacles and minimizing path length. The findings highlight PSO's potential for practical UAV applications, emphasizing the importance of parameter optimization. This research contributes to the advancement of autonomous UAV navigation, indicating PSO as a viable solution for real-world path planning challenges.

Efficient Unmanned Aerial Vehicle (UAV) trajectory generation is crucial for successful area coverage missions, aiming to maximize coverage while minimizing resource consumption. In this research, we present a comprehensive study on optimizing UAV trajectory generation using Deep Neural Networks (DNNs) with the Adam optimization algorithm. The DNNs are trained on historical data to produce smooth and continuous trajectories, thereby reducing abrupt changes in direction and enhancing overall efficiency during the mission. To evaluate the performance of the proposed approach, we conducted experiments comparing different activation functions, namely tanh, sigmoid, and ReLU, with the Adam-optimized DNN model. The trajectories generated by each activation function were analyzed using key metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) scores for both X and Y coordinates. The results of the comparative analysis revealed that the DNN model with the Adam optimizer exhibited superior performance over the other activation functions. It achieved lower MSE, MAE, and RMSE values, indicating better trajectory accuracy and smoother paths. Additionally, the R2 scores demonstrated a higher correlation between the generated trajectories and the actual trajectories, highlighting the model's ability to capture underlying patterns effectively. The findings underscore the significance of leveraging the Adam-optimized DNN approach for UAV trajectory planning, offering promising opportunities for resource optimization, increased mission success, and further advancements in autonomous aerial systems. This research contributes to the ongoing efforts in UAV path planning, optimization, and intelligent control strategies, paving the way for enhanced autonomous systems in various real-world applications.

To solve the travelling salesman problem (TSP) for unmanned aerial vehicle (UAV) path planning, we propose two parallel optimization algorithms. One is the improved genetic algorithm (IGA), and the other is the particle-swarm-optimization-based ant colony optimization algorithm (PSO-ACO). As an indispensable part of UAV cooperative mission assignment, the research of UAV path planning has attracted much attention of scholars. In this paper, according to the characteristics of UAV path planning, we firstly establish a corresponding multi-objective multi-constrained combinatorial optimization model-TSP. In the TSP model, the UAV is considered as the travelling salesman, and the mission target is regarded as the travelling city. Then, considering that TSP is a complex NP-hard problem, this paper carries out two optimization algorithms as IGA and PSO-ACO to solve the TSP model, which both can obtain effective and reasonable UAV path planning schemes. IGA is a kind of evolutionary algorithm with implicit parallel ability and global optimization ability. Through the rational selection of encoding mode and fitness function, and valid setting of selection operator, crossover operator and mutation operator, IGA can solve the TSP with great convergence. PSO-ACO is a swarm intelligence optimization algorithm with inherently parallel ability and self-organizing ability, which is perfect for solving TSP. Adopting the idea of particle optimization into ant colony optimization algorithm, ants in PSO-ACO system have the particle characteristics that can adjust the local optimal solution and global optimal solution after completing every single traversal. Finally, in the simulation part, based on the stochastic dynamic map, this paper builds the TSP model for UAV path planning. Through the comprehensive analyses of the optimization results of two proposed parallel optimization algorithms and one contrast approach, we can conclude that the proposed IGA and PSO-ACO algorithms are more rational and effective for solving UAV path planning problem compared with the contrast approach.

Chapter 4 - Path planning and task assignment for multiple UAVs in dynamic environments

Survey on computational-intelligence-based UAV path planning

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.

The Grey Wolf Optimizer (GWO) algorithm is recognized for its simplicity and ease of implementation, and has become a preferred method for solving global optimization problems due to its adaptability and search capabilities. Despite these advantages, existing Unmanned Aerial Vehicle (UAV) path planning algorithms are often hindered by slow convergence rates, susceptibility to local optima, and limited robustness. To surpass these limitations, we enhance the application of GWO in UAV path planning by improving its trajectory evaluation function, convergence factor, and position update method. We propose a collaborative UAV path planning model that includes constraint analysis and an evaluation function. Subsequently, an Enhanced Grey Wolf Optimizer model (NI–GWO) is introduced, which optimizes the convergence coefficient using a nonlinear function and integrates the Dynamic Window Approach (DWA) algorithm into the model based on the fitness of individual wolves, enabling it to perform dynamic obstacle avoidance tasks. In the final stage, a UAV path planning simulation platform is employed to evaluate and compare the effectiveness of the original and improved algorithms. Simulation results demonstrate that the proposed NI–GWO algorithm can effectively solve the path planning problem for UAVs in uncertain environments. Compared to Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), GWO, and MP–GWO algorithms, the NI–GWO algorithm can achieve the optimal fitness value and has significant advantages in terms of average path length, time, number of collisions, and obstacle avoidance capabilities.

A novel UAV path planning approach: Heuristic crossing search and rescue optimization algorithm

A new era of the fifth-generation (5G) networks is realized to satisfy user demands on higher data rate and massive connectivity for information sharing and utilization. The vertical applications such as vehicle-to-everything (V2X) communications, industrial automation, smart factory, smart farm and smart cities require ultra-fast communications and wide service range. Coverage extension is a key issue to support the required demands on higher performance, but requires an additional deployment of base or relay stations. Therefore, an efficient solution needs to be cost-effective and easy, in order to deploy more stations. An unmanned aerial vehicle (UAV) has been considered as a candidate to overcome these issues because it is much more cost-effective than the ground stations and does not require network or cell replanning, thereby enhancing the network coverage without additional excessive deployment procedures of the existing networks. UAVs will play important roles in 5G and beyond networks assisting as macro base stations, relay stations, small cells, or a moving aggregator. The performance of UAV wireless networks highly depends on the position or the trajectory of UAVs and the resource managements of entire networks. Recently, there have been extensive studies on performance analysis, UAV deployment, UAV trajectory and resource management of UAV wireless networks to achieve the required demands on performance. This paper surveys research conducted for the UAV deployment and trajectory to construct UAV wireless networks for the coverage extension, the throughput improvement and the resource management for different use cases and scenarios, so as to encourage further studies in this area.

Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges

Path smoothing is an important part of UAV (Unmanned Aerial Vehicle) path planning, because the smoothness of the planned path is related to the flight safety and stability of UAVs. In existing smooth UAV path planning methods, different characteristics of a path curve are not considered comprehensively, and the optimization functions established based on the arc length or curvature of the path curve are complex, resulting in low efficiency and quality of path smoothing. To balance the arc length and smoothness of UAV paths, this paper proposes to use energy-minimizing Bézier curves based on composite energy for smooth UAV path planning. In order to simplify the calculation, a kind of approximate stretching energy and bending energy are used to control the arc length and smoothness, respectively, of the path, by which the optimal path can be directly obtained by solving a linear system. Experimental validation in multiple scenarios demonstrates the methodology’s effectiveness and real-time computational viability, where the planned paths by this method have the characteristics of curvature continuity, good smoothness, and short arc length. What is more, in many cases, compared to path smoothing methods based solely on bending energy optimization, the proposed method can generate paths with a smaller maximum curvature, which is more conducive to the safe and stable flight of UAVs. Furthermore, the design of collision-free smooth path for UAVs based on the piecewise energy-minimizing Bézier curve is studied. The new method is simple and efficient, which can help to improve UAV path planning efficiency and thus improve UAV reaction speed and obstacle avoidance ability.

Due to flexible scheduling, and better transmission channel, unmanned aerial vehicle (UAV) can improve transmit performance of Internet of Things (IoT). In this paper, we propose an UAV-enabled multichannel IoT based on intelligent fog computing, where UAV as a relay forwards IoT's information to the data center under the control of fog computing base station in the case of terrestrial channel fading. The IoT's throughput are maximized by jointly optimizing subcarrier, power of IoT, and UAV, and UAV trajectory, subject to the constraints of information causality, maximum transmit power, and maximum UAV speed. The subcarriers are dynamically allocated according to their channel gains, the water filling algorithm is adopted to optimize the power for UAV, and IoT by fixing UAV trajectory, and the optimal UAV trajectory is achieved with successive convex approximation under the fixed power allocation. Then a jointly iterative optimization on subcarrier, power, and trajectory is presented to get the optimal solution. In addition, we propose a fairness optimization scheme to maximize the minimum transmit rate of IoT nodes. The simulations indicate the IoT with mobile UAV, and dynamic subcarrier allocation may achieve better transmit performance, and the fairness optimization can decrease the rate difference of nodes effectively.

This article studies the unmanned aerial vehicle (UAV) trajectory planning problem in a UAV-enabled environmental monitoring system and considers a typical data collection scenario where a UAV is dispatched to a geographical area to collect time-constrained data in a set of monitoring areas and transmit collected data to a ground base station (GBS). We formulate the UAV trajectory planning problem as an optimization problem with the objective to minimize the UAV’s mission completion time by jointly optimizing the UAV’s flying speeds, hovering positions, and visiting sequence, taking into account the Age of Information (AoI) of data in monitoring areas, and the on-board energy of the UAV. To solve the problem, we decompose the formulated optimization problem into two subproblems: a UAV speed optimization problem and a UAV path optimization problem, and propose successive convex approximation (SCA) method-based and generic algorithm (GA)-based algorithms to solve the subproblems. Based on the proposed algorithms, we further propose an AoI-and-energy-aware trajectory optimization (AoI-EaTO) algorithm to solve the main problem. Simulation results show that the proposed AoI-EaTO algorithm can find a better solution to the problem than two benchmark algorithms. Moreover, given the UAV’s on-board energy and maximum speed as well as the positions of the GBS and monitoring areas, the AoI limitation threshold that the system is able to satisfy can be obtained through simulation results. This threshold can be used to decide if the UAV is able to finish a particular data collection mission, which is useful to the deployment of the mission.

In this paper, we investigate jamming-resilient unmanned aerial vehicle (UAV) path planning strategies for data collection in Internet of Things (IoT) networks, in which the typical UAV can learn the optimal trajectory to elude such jamming attacks. Specifically, the typical UAV is required to collect data from multiple distributed IoT nodes under collision avoidance, mission completion deadline, and kinematic constraints in the presence of jamming attacks. We first design an intelligent UAV jammer, which utilizes reinforcement learning to choose actions based on its observation. Then, an intelligent UAV anti-jamming strategy is constructed to deal with such attacks, and the optimal trajectory of the typical UAV is obtained via dueling double deep Q-network (D3QN). Simulation results show that the intelligent jamming attack has great influence on the UAV's performance, and the proposed defense strategy can recover the performance close to that in no-jammer scenarios.

In a surveillance mission, the task of Unmanned Aerial Vehicles (UAV) path planning can in some cases be addressed using Sequential Monte Carlo (SMC) simulation. If sufficient a priori information about the target and the environment is available an assessment of the future state of the target is obtained by the SMC simulation. This assessment is used in a set of "what-if" simulations to compare different alternative UAV paths. In a static environment this simulation can be conducted prior to the mission. However, if the environment is dynamic, it is required to run the "what-if" simulations on-line i.e. in real-time. In this paper the details of this on-line simulation approach in UAV path planning is studied and its performance is compared with two other methods: an off-line simulationaided path planning and an exhaustive search method. The conducted simulations indicate that the on-line simulation has generally a higher performance compared with the two other methods.