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

Survey on computational-intelligence-based UAV path planning

Unmanned aerial vehicle (UAV) path planning represents a pivotal component in autonomous navigation systems, with its primary objective being the determination of safe, efficient, and optimized flight trajectories within complex operational environments. Among various optimization methodologies, particle swarm optimization (PSO) has emerged as a prominent solution for path planning challenges, owing to its computational efficiency and straightforward implementation. Nevertheless, the efficacy of PSO is intrinsically linked to its parameter configuration, where different parameter selections can substantially influence the algorithm's search capacity and convergence dynamics. This research systematically examines the effects of four critical PSO parametersinertia weight, social weight, cognitive weight, and swarm sizeon both optimization quality and computational performance in UAV path planning scenarios. The investigation commences with the formulation of a mathematical framework for UAV path planning, incorporating path length and obstacle avoidance capability as primary evaluation metrics. Subsequently, through comprehensive simulation experiments, we meticulously analyze the influence of various parameter configurations on PSO's convergence characteristics, employing advanced data fitting techniques to establish precise mathematical relationships between parameter settings and algorithmic performance. The empirical results reveal that strategic parameter optimization substantially enhances PSO's global search capability, improves path quality, and expedites convergence rates. Based on rigorous analysis, the optimal parameter configuration is determined to be: inertia weight (w) = 0.659, social weight (c) = 1.349, cognitive weight (c) = 2.108, and swarm size (M) = 88. These findings not only provide theoretical foundations but also offer practical optimization strategies for parameter selection in UAV path planning applications, with potential extensions to other intelligent optimization domains.

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.

Neighborhood global learning based flower pollination algorithm and its application to unmanned aerial vehicle path planning

To address the issues of slow convergence and susceptibility to local optima prevalent in mainstream bio-inspired algorithms, this paper proposes a Enhanced Black-winged Kite Algorithm (WCTBKA) for Unmanned Aerial Vehicle (UAV) path planning and obstacle avoidance. Specifically targeting the original Black-winged Kite Algorithm’s slow convergence and susceptibility to local optima in UAV path planning and obstacle avoidance, WCTBKA incorporates three key enhancements: tent chaotic mapping, a chaotic dynamic weight factor, and a water wave dynamic evolution factor. Tent chaotic mapping replaces the standard random initialization to augment initial population diversity. The chaotic dynamic weight factor processes individuals during both the attacking and migrating phases,
thereby accelerating convergence. Concurrently, water wave dynamic evolution factor adaptively balances global exploration and local exploitation. Simulation results demonstrate that WCTBKA achieves superior comprehensive performance in convergence speed and local optima avoidance compared to current state-of-the-art methods. Quantitatively, the proposed algorithm averagely improves the optimal fitness and convergence performance by 30% and 31%, respectively.

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 this paper, the development and demonstration of a modified Voronoi algorithm for unmanned aerial vehicle (UAV) path planning and obstacle avoidance is presented. This algorithm is intended to produce a flyable collision-free path through a series of obstacles/threats represented by cylindrical risk zones. A model of the West Virginia University (WVU) YF-22 research aircraft implemented within the WVU UAV simulation environment is used to demonstrate the functionality of the proposed algorithm and its effectiveness in the presence of several risk zones configurations. As compared to traditional methods, the approach is more general, coping with obstacles and risk zones of variable intensity and providing additional flexibility and better coverage of the possible solution space.

In this paper, an environment modeling method with height dimension reduction for unmanned aerial vehicle (UAV) path planning is presented. Based on the traditional grid method, a plane being vertical to yz-plane and including the start point and end point is considered in this method. Comparing the height of this plane and obstacles, the environment model could be set up through re-dividing the plane by grid method. Above idea reduces the dimension of the 3D environment model and ignores the obstacles which are lower than the flying height of UAV. In this way, the accuracy of the proposed environment model method could be increased, and the efficiency of the path planning algorithm could be ensured further. Simulation experiment shows the effectiveness of this environment modeling method by applying A* algorithm for path planning.

Unmanned Aerial Vehicle (UAV) path planning is a critical challenge in autonomous navigation, requiring intelligent trajectory generation in complex environments that balance safety, efficiency, and adaptability. Particle Swarm Optimization (PSO), a prominent swarm intelligence algorithm, is widely used for its robust global search capabilities. However, PSO’s performance heavily depends on parameter configurations, particularly the inertia weight (ω). This paper investigates the impact of parameter settings on PSO-based UAV path planning and proposes optimization strategies to enhance its efficacy.Simulation experiments reveal that when ω = 0.5, PSO exhibits strong local search capabilities and fast convergence but is prone to local optima, reducing path planning precision. Increasing ω enhances global search, enabling broader solution space exploration, but at the cost of slower convergence and limited optimization gains due to computational constraints. Notably, when ω = 1.5, the algorithm avoids local optima but shows minimal performance improvement and reduced efficiency.The study also evaluates optimization strategies, including a linearly varying inertia weight and a mutant particle strategy. The linearly varying inertia weight significantly improves optimization performance but slows convergence, making it suitable for high-accuracy scenarios with ample computational resources. The mutant particle strategy, however, achieves a better balance between optimization and convergence, ideal for practical applications requiring both efficiency and accuracy. Combining these strategies does not yield substantial improvements, suggesting that a single strategy may be more computationally efficient.This research provides valuable insights into PSO parameter configuration and optimization for UAV path planning, advancing the field of autonomous navigation technology.

Feedback RRT* algorithm for UAV path planning in a hostile environment

With the rapid development of the unmanned aerial vehicle (UAV) industry and artificial intelligence industry, UAV has been used in the civil field. For civil UAVs, the distribution of logistics UAV is a hot research topic. Based on the UAV data set, the value function model of logistics UAV task planning is established by deep learning. It is found that there are obvious differences in value function for different deep learning methods. Moreover, it is also found that using the Random Forest Model has a good effect on the modeling of the value function. On this basis, the task planning model of logistics UAVs based on K-Means is established. In order to find an optimization algorithm suitable for the logistics system, this research uses an improved Genetic Algorithm, improved TS Algorithm, and Particle Swarm Optimization Algorithm to solve the model. By comparing three intelligent optimization algorithms, it is found that the improved TS Algorithm not only converges faster for this model but also has fast convergence for a large number of logistics task points by comparing the flight route and convergence curve of the objective function, which can meet the needs of largescale tasks in the actual logistics system.

In recent years, unmanned aerial vehicle (UAV), has been increasingly applied to research in various fields due to its small size and fast speed. Among them, the path planning of UAV under various actual environmental constraints has been one of the hot issues for the past few years. UAV path planning is to find the optimal or feasible route from the starting point to the target point and meet the UAV performance indicators under specific constraints. The essence of the problem is the optimization problem of multi-objective function to find the extreme value under multi-constraints. In this paper, under the background of logistics distribution, Through the research of UAV path planning model and algorithm, this paper summarizes the UAV path planning problems, analyzes several common path planning algorithms in recent years, and carries out simulation experiments with MATLAB on several algorithms with high attention. On this basis, the future research direction is prospected.KeywordsLogistics distributionUnmanned aerial vehicle (UAV)Path planning modelPath planning algorithm

Path planning of the unmanned aerial vehicle (UAV) is one of the complex optimization problems due to the model complexity and a high number of constraints. Evolutionary algorithms are a conventional method to solve complex optimization problems with multiple constraints. However, they are time-consuming algorithms which make their implementation difficult. UAV path planning is usually a real-time problem that is needed to solve in limited time steps. Parallelization is one of the most effective methods for reducing the computation time of evolutionary algorithms. In the industrial applications, usually there is a communication platform between devices which can be used for the implementation of the parallelization process. For example, controller area network (CAN) is a protocol used for reliable communication in aerospace systems. Due to the development of this protocol on UAVs and its characteristics, it can be used for parallel optimization of path planning problem. In this paper, an asynchronous distributed multi-master parallel genetic algorithm and a parallel particle swarm optimization algorithm are implemented on CAN bus to improve the speed and performance of the UAV path planning.

In the field of unmanned aerial vehicle (UAV) path planning, the conventional deep Q-network (DQN) algorithm encounters the issue of action space discretization, which results in the generation of unsmooth and inefficient planned paths. To address this issue, we introduce the particle swarm optimization (PSO) algorithm into DQN to convert the discrete action space into a continuous one. This method divides the agent’s surrounding space into discrete and continuous action spaces. The PSO algorithm performs a global search in the continuous space to obtain a continuous candidate solution, while DQN learns a policy in the discrete space to obtain a discrete candidate solution. Then, the two candidate solutions are combined using a weighted vector method to determine a direction that balances global search and policy learning. Additionally, we introduce a novel feature matrix as the state space for DQN, providing more accurate environmental and positional representations. Furthermore, we incorporate a mechanism into the base prioritized experience replay (PER) and N-step updates, which combines the current temporal difference error (TD-error) with historical priorities and includes a policy entropy penalty term, thereby enhancing DQN’s ability to learn long-term dependencies. The performance of the PSO-DQN model is further improved through an enhanced ε-greedy policy and learning rate decay strategy. Simulation results and experiments using the Flightmare simulator demonstrate that the proposed method generates smoother and more efficient paths for drones, exhibiting strong robustness in complex environments.

When the traditional Genetic Algorithm(GA) is used for unmanned aerial vehicle(UAV) path planning, the shortcomings of local search ability are reflected when the planning time is more urgent. To address this shortcoming, this paper proposes an improved genetic algorithm that limits the new gene's generating region. The generating region of the evolution operator is dynamically adjusted. The algorithm is applied to UAV online path planning for tracking moving target. The simulation results show that the method enhances the local search ability of the algorithm and improves the searching efficiency. UAV online path planning can be completed well with the improved genetic algorithm.