Reinforcement-based black-winged kite algorithm for UAV path planning based on chaotic mapping and water wave dynamic evolution mechanism

Survey on computational-intelligence-based UAV path planning

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.

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.

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.

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

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.

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.

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

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.

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.

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

Wireless sensor networks are used in different application areas. Many applications require network sink mobility or monitored area borders dynamicity. A possible way to accomplish these requirements is development of platforms that enable the cooperation of ground with unmanned aerial vehicles to perform sensed data gathering. In case of sparse wireless sensor network nodes location unmanned aerial vehicle path planning becomes important problem which affects system efficiency. Unmanned Aerial Vehicle path length reduction algorithm for is proposed in this work.

In recent years, the availability of low-cost and autonomous unmanned aerial vehicles (UAVs) results in the use of them for different types of military and commercial applications. The crucial part of the autonomous UAVs is their online or offline path planning algorithms. In the literature, there are many types of solutions, which use evolutionary and/or swarm intelligence approaches. Ant colony optimization is one of the mostly used algorithms, which has been applied to solve different type of path planning problems. Mainly, most of these studies have focused on a single colony ant colony optimization (ACO), which can find better solutions in fewer computation times. However, it is able to converge to a sub-optimal solution in the planning process. One approach to avoid the premature convergence is the use of Multi-Colony ACO, in which a number of ant colonies try to find an optimal solution cooperatively by exchanging their valuable information with each other. In this paper, it is aimed to implement an obstacle avoidance UAV path planning by using Multi-Colony ACO algorithm. We experimentally investigate the use of Multi-Colony ACO approach results from an effective path planning for UAVs with a comparison to a single colony ACO approach.

Path planning and obstacle avoidance are pivotal for intelligent unmanned aerial vehicle (UAV) systems in various domains, such as postdisaster rescue, target detection, and wildlife conservation. Currently, reinforcement learning (RL) has become increasingly popular in UAV decision-making. However, the RL approaches confront the challenges of partial observation and large state space when searching for random targets through continuous actions. This paper proposes a representation enhancement-based proximal policy optimization (RE-PPO) framework to address these issues. The representation enhancement (RE) module consists of observation memory improvement (OMI) and dynamic relative position-attitude reshaping (DRPAR). OMI reduces collision under partially observable conditions by separately extracting perception features and state features through an embedding network and feeding the extracted features to a gated recurrent unit (GRU) to enhance observation memory. DRPAR compresses the state space when modeling continuous actions by transforming movement trajectories of different episodes from an absolute coordinate system into different local coordinate systems to utilize similarity. In addition, three step-wise reward functions are formulated to avoid sparsity and facilitate model convergence. We evaluate the proposed method in three 3D scenarios to demonstrate its effectiveness. Compared to other methods, our method achieves a faster convergence during training and demonstrates a higher success rate and a lower rate of timeout and collision during inference. Our method can significantly enhance the autonomy and intelligence of UAV systems under partially observable conditions and provide a reasonable solution for UAV decision-making under uncertainties.