An Improved Equilibrium Optimizer with Application in Unmanned Aerial Vehicle Path Planning.

The unmanned aerial vehicle (UAV) path planning problem is an important assignment in the UAV mission planning. Based on the artificial potential field (APF) UAV path planning method, it is reconstructed into the constrained optimisation problem by introducing an additional control force. The constrained optimisation problem is translated into the unconstrained optimisation problem with the help of slack variables in this paper. The functional optimisation method is applied to reform this problem into an optimal control problem. The whole transformation process is deduced in detail, based on a discrete UAV dynamic model. Then, the path planning problem is solved with the help of the optimal control method. The path following process based on the six degrees of freedom simulation model of the quadrotor helicopters is introduced to verify the practicability of this method. Finally, the simulation results show that the improved method is more effective in planning path. In the planning space, the length of the calculated path is shorter and smoother than that using traditional APF method. In addition, the improved method can solve the dead point problem effectively.

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.

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.

Two Approaches for Path Planning of Unmanned Aerial Vehicles with Avoidance Zones

Aiming at the problem of fixed-wing unmanned aerial vehicle (UAV) path planning, considering the actual flight conditions and flight performance of UAV, a multi-constraint UAV path planning model is constructed with the minimum flight range and correction times as the objective function. The improved A* algorithm is used to solve the problem: in order to adapt to the model, the objective function of the model is used as the evaluation function; in order to speed up the search efficiency, the branch and bound method is used for iterative search. The simulation results show that: the model can achieve bi-objective optimization, and it is reasonable. Compared with the traditional A* algorithm, the improved algorithm can better balance the optimization flight range and correction times, and save the algorithm planning time, and effectively complete the fast path planning of fixed-wing UAV with multiple constraints.KeywordsFixed-wing UAVPath planningPositioning errorImproved A* algorithmBranch and bound method

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.

The Black-winged Kite Optimization Algorithm (BKA) is likely to experience a sluggish convergence rate when confronted with the optimization of complex multimodal functions. The fundamental algorithm has a tendency to get stuck in local optima, thus rendering it arduous to identify the global optimal solution. When dealing with large-scale data or high-dimensional optimization challenges, the BKA algorithm entails significant computational expenses, which might lead to excessive memory usage or prolonged running durations. In order to enhance the BKA and tackle these problems, a revised Black-winged Kite Optimization Algorithm (TGBKA) that incorporates the Tent chaos mapping and Gaussian mutation strategies is put forward. The algorithm is simulated and analyzed alongside other swarm intelligence algorithms by utilizing the CEC2017 test function set. The optimization outcomes of the test functions and the function convergence curves indicate that the TGBKA demonstrates superior optimization precision, a quicker convergence speed, as well as robust anti-interference and environmental adaptability. It is also contrasted with numerous similar algorithms via simulation experiments in various scene models for Unmanned Aerial Vehicle (UAV) path planning. In comparison to other algorithms, the TGBKA produces a shorter flight route, a higher convergence speed, and stronger adaptability to complex environments. It is capable of efficiently addressing UAV path planning issues and improving the UAV's path planning abilities.

One of the most fundamental element of the Unmanned Aerial Vehicles (UAV) path planning is the assurance that the path planned avoids any terrain collision. The task to “evaluate how much a UAV path is in collision with the terrain” is particularly crucial for path planners that are optimizing random generated paths. This terrain collision evaluation task can be computationally demanding and its computation time depends on the environment representation used. This paper presents a Field Programmable Gate Arrays (FPGA) based design of a UAV terrain collision evaluator that evaluates in parallel segments of the path in real-time. Its worst case computation time has a fixed upper bounded, based on the size of the map. For a 500×500 grid map, this upper bound is 1.9 μs, which is a promising result for future UAV real-time path planners.

The demand of Unmanned Aerial Vehicle (UAV) to monitor natural disasters extends its use to multiple civil missions. While the use of remotely control UAV reduces the human casualties' rates in hazardous environments, it is reported that most of UAV accidents are caused by human factor errors. In order to automate UAVs, several approaches to path planning for UAVs, mainly based on Genetic Algorithm (GA), have been proposed. However, none of the proposed paradigms optimally solve the path planning problem with contrasting objectives. We are proposing a Master-Slave Parallel Vector-Evaluated Genetic Algorithm (MSPVEGA) to solve the path planning problem. MSPVEGA takes advantage of the advanced computational capabilities to process multiple GAs concurrently. In our present experimental set-up, the MSPVEGA gives optimal results for UAV.

Unmanned aerial vehicle (UAV) path planning plays an important role in UAV flight, and an effective algorithm is needed to realize UAV path planning. The sand cat algorithm is characterized by simple parameter setting and easy implementation. However, the convergence speed is slow, easy to fall into the local optimum. In order to solve these problems, a novel sand cat algorithm incorporating learning behaviors (LSCSO) is proposed. LSCSO is inspired by the life habits and learning ability of sand cats and incorporates a new position update strategy into the basic Sand Cat Optimization Algorithm, which maintains the diversity of the population and improves the convergence ability during the optimization process. Finally, LSCSO is applied to the challenging UAV 3D path planning with cubic B-spline interpolation to generate a smooth path, and the proposed algorithm is compared with a variety of other competing algorithms. The experimental results show that LSCSO has excellent optimization-seeking ability and plans a safe and feasible path with minimal cost consideration among all the compared algorithms.

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.

A novel phase angle-encoded fruit fly optimization algorithm with mutation adaptation mechanism applied to UAV path planning

Wind has a significant effect on the control of fixed-wing unmanned aerial vehicles (UAVs), resulting in changes in their ground speed and direction, which has an important influence on the results of integrated optimization of UAV task allocation and path planning. The objective of this integrated optimization problem changes from minimizing flight distance to minimizing flight time. In this study, the Euclidean distance between any two targets is expanded to the Dubins path length, considering the minimum turning radius of fixed-wing UAVs. According to the vector relationship between wind speed, UAV airspeed, and UAV ground speed, a method is proposed to calculate the flight time of UAV between targets. On this basis, a variable-speed Dubins path vehicle routing problem (VS-DP-VRP) model is established with the purpose of minimizing the time required for UAVs to visit all the targets and return to the starting point. By designing a crossover operator and mutation operator, the genetic algorithm is used to solve the model, the results of which show that an effective UAV task allocation and path planning solution under steady wind can be provided.

Unmanned aerial vehicle (UAV) path planning is a constrained multi-objective optimization problem. With the increasing scale of UAV applications, finding an efficient and safe path in complex real-world environments is crucial. However, existing particle swarm optimization (PSO) algorithms struggle with these problems as they fail to consider UAV dynamics, resulting in many infeasible solutions and poor convergence to optimal solutions. To address these challenges, we propose a spherical vector-based adaptive evolutionary particle swarm optimization (SAEPSO) algorithm. This algorithm, based on spherical vectors, directly incorporates UAV dynamic constraints and introduces improved tent map and reverse learning to enhance the diversity and distribution of initial solutions. Additionally, dynamic nonlinear and adaptive factors are integrated to balance exploration and exploitation capabilities. To avoid local optima in highly complex environments, we propose an adaptive acceleration strategy for poor particles, and an evolutionary programming strategy is incorporated to further improve the optimization capability. Finally, we conducted comparative studies and in six benchmark scenarios with varying threat levels, and the results demonstrated that the proposed algorithm outperforms others in the initial solution effectiveness, the final solution accuracy, convergence stability, and scalability.

Grey wolf optimizer for unmanned combat aerial vehicle path planning