Unmanned Combat Aerial Vehicle Path Research Articles

A Three-Dimensional UCAV Path Planning Approach Based on Immune Plasma Algorithm

Path planning of an unmanned aerial vehicle (UAV) or its variant supported with weapon systems, also called unmanned combat aerial vehicle (UCAV), is one of the most crucial steps for the autonomous flight and requires compelling decisions being made by considering the existence of the enemy defence, obstacles, and battery or fuel capacity of the vehicle optimally before starting the flight. Immune plasma algorithm (IP algorithm or IPA), inspired by the convalescent plasma treatment which is a medical method being popularized again with the rise of new coronavirus or COVID-19, has been introduced recently as a new optimization technique and the promising performance of the mentioned algorithm validated through a set of numerical and engineering problems. In this study, the IP algorithm was specialized as a three-dimensional UCAV path planner for the first time. Its performance was investigated particularly by using three different battlefield environments and assigning various constants to the population size. Moreover, the effect of two IPA-specific control parameters that are responsible for determining the number of donors and number of receivers on the qualities of the calculated paths were analyzed in detail. The results obtained by the IPA were compared with the results of other three-dimensional path planners guiding optimization algorithms such as simulated annealing (SA), gray wolf optimizer (GWO) and symbiotic organism search (SOS). Experimental studies showed that the main idea lying behind the usage of qualified solutions as donors and transferring information directly from them to the receivers representing the poor solutions of the problem gives a positive contribution to the exploitation-dominant operations of IPA and fully complies with the challenging requirements of the path planning problem. Especially in the battlefields for which an optimal or near optimal path contains complex maneuvers, the advantages from the subtly balanced operational steps of the IPA become more apparent by setting the number of donors and receivers appropriately and IPA is found to be more stable and successful compared to the other tested algorithms.

A Spider Monkey Optimization Based on Beta-Hill Climbing Optimizer for Unmanned Combat Aerial Vehicle (UCAV)

Unmanned Combat Aerial Vehicle (UCAV) path planning is a challenging optimization problem that seeks the optimal or near-optimal flight path for military operations. The problem is further complicated by the need to operate in a complex battlefield environment with minimal military risk and fewer constraints. To address these challenges, highly sophisticated control methods are required, and Swarm Intelligence (SI) algorithms have proven to be one of the most effective approaches. In this context, a study has been conducted to improve the existing Spider Monkey Optimization (SMO) algorithm by integrating a new explorative local search algorithm called Beta-Hill Climbing Optimizer (BHC) into the three main phases of SMO. The result is a novel SMO variant called SMOBHC, which offers improved performance in terms of intensification, exploration, avoiding local minima, and convergence speed. Specifically, BHC is integrated into the main SMO algorithmic structure for three purposes: to improve the new Spider Monkey solution generated in the SMO Local Leader Phase (LLP), to enhance the new Spider Monkey solution produced in the SMO Global Leader Phase (GLP), and to update the positions of all Local Leader members of each local group under a specific condition in the SMO Local Leader Decision (LLD) phase. To demonstrate the effectiveness of the proposed algorithm, SMOBHC is applied to UCAV path planning in 2D space on three different complex battlefields with ten, thirty, and twenty randomly distributed threats under various conditions. Experimental results show that SMOBHC outperforms the original SMO algorithm and a large set of twenty-six powerful and recent evolutionary algorithms. The proposed method shows better results in terms of the best, worst, mean, and standard deviation outcomes obtained from twenty independent runs on small-scale (D = 30), medium-scale (D = 60), and large-scale (D = 90) battlefields. Statistically, SMOBHC performs better on the three battlefields, except in the case of SMO, where there is no significant difference between them. Overall, the proposed SMO variant significantly improves the obstacle avoidance capability of the SMO algorithm and enhances the stability of the final results. The study provides an effective approach to UCAV path planning that can be useful in military operations with complex battlefield environments.

A multi-population immune plasma algorithm for path planning of unmanned combat aerial vehicle

Autonomous flight of an unmanned aerial vehicle (UAV) or its weaponized variant named unmanned combat aerial vehicle (UCAV) requires a route or path determined carefully by considering the optimization objectives about the enemy threats and fuel consumption of the system being operated. Immune Plasma algorithm (IP algorithm or IPA) is one of the most recent optimization techniques and directly models the fundamental steps of a medical method also used for the COVID-19 disease and known as convalescent or immune plasma treatment. In this study, IP algorithm for which a promising performance has already been validated with a single population was first extended to a multi-population domain supported by a migration schema. Moreover, the usage of the donor as a source of plasma for the treatment operations of a receiver was remodeled. The new variant of the IPA empowered with the multi-population and modified donor usage approach was called Multi-IP algorithm or MULIPA. For investigating the solving capabilities of the MULIPA as a UCAV path planner, different battlefield scenarios and algorithm specific parameter configurations were used. The results obtained by the MULIPA were compared with the results of other meta-heuristic based path planners. The comparative studies between MULIPA and other techniques showed that newly proposed IPA variant is capable of finding more secure and fuel efficient paths for a UCAV system.

A double-layer coding model with a rotation-based particle swarm algorithm for unmanned combat aerial vehicle path planning

Unmanned combat aerial vehicle (UCAV) technology has to address many challenges in complex battlefield environments. To produce a safe and low-energy flying path, a UCAV requires many points to build a path that avoids threats, which increases the problem dimension, consumes more computational resources and makes the results unstable. To address this issue, this paper employs a new model known as double-layer coding (DLC) for path planning with unevenly distributed points to decrease the number of superfluous points on the path. Meanwhile, the RPSO algorithm, which introduces a novel strategy of rotating particles in high-dimensional space to search for targets, is proposed as an enhanced particle swarm optimization (PSO) algorithm. The proposed method effectively improves PSO′s exploration capacity. Furthermore, RPSO is employed to implement the double-layer coding model for path planning (DLCRPSO). The experimental results show that the proposed DLCRPSO method for path planning always produces feasible flight paths in complex environments.

An adaptive neighborhood-based search enhanced artificial ecosystem optimizer for UCAV path planning

Path planning is an essential component of the unmanned combat aerial vehicle (UCAV) system and is the precondition for achieving tasks such as aerial reconnaissance, monitoring, and fire attack. Its objective is to find a satisfactory route with full consideration of the threat area and constraints, which is a multi-constraint global optimization problem. The work proposes an adaptive neighborhood-based search enhanced artificial ecosystem optimizer (NSEAEO) to address the UCAV path planning problem. The new algorithm uses distance-fitness-based information in the consumption phase to construct an adaptive neighborhood for consumers, who choose the better individuals within the neighborhood for predation. The strategy not only facilitates a thorough search of the problem space but also enhances the global exploration capability of the algorithm. In addition, due to the lack of diversity of the population in the decomposition stage of the AEO algorithm, it is easy to fall into the local extremum. A novel updating decomposition mechanism is designed to dynamically choose the decomposition method according to the threshold in each iteration, which effectively averts insufficient diversity and increases the ability to escape from local extrema. Furthermore, to further improve the search capability of the algorithm, a quadratic interpolation (QI) operator is embedded in the AEO algorithm. The core idea is to choose three agents to match a quadratic function close to the goal function and adopt the extremum of the quadratic function to produce new agents. The above approach achieves a good balance between exploitation and exploration. The experimental results on a series of benchmark functions and UCAV path planning in complex environments demonstrate that NSEAEO outperforms other algorithms in terms of solution quality.

An immune plasma algorithm based approach for UCAV path planning

Immune Plasma algorithm for short IPA is one of the most recent meta-heuristics and differs when the speciality of the process tried to be modeled is considered. The competitive performance of the IPA on different numerical optimization or engineering problems gave inspiration for the studies related with the specializing or porting IPA to more complex real world problems. In this study, IPA was ported in order to solve unmanned combat aerial vehicle (UCAV) path planning problem and its capabilities were investigated in detail by assigning different values to the population size, number of donors-receivers and using challenging battlefield scenarios. The results found by the IPA were also compared with the results obtained by a set of well-known meta-heuristic algorithms. The comparative studies informed that IPA is capable of finding more robust path or paths for the UCAV operating on a battlefield and outperforms other executed algorithms for almost all the test scenarios.

UCAV Path Planning for Avoiding Obstacles using Cooperative Co-evolution Spider Monkey Optimization

The unmanned combat aerial vehicle (UCAV) path planning problem is a complex global optimization problem that aims to seek an optimal flight route that avoids the threats and constraints on the battlefield. However, as the number and degree of the path threats increases, the recent UCAV path planning algorithms all suffer from being trapped into local optima with a low convergence rate, which leads to the poor performance. Therefore, to resolve these problems, we propose a Cooperative co-Evolution (CE)-based Spider Monkey Optimization (SMO) algorithm, named CESMO to address UCAV path planning problem for avoiding obstacles. First, a cooperative co-evolution strategy is proposed to prevent the algorithms from falling into local optima easily, enhancing the coherence of the path. After that, a further division is applied to the algorithm for diversifying the search direction of each individual in the sub-groups. Finally, the spider monkey optimization-based search mechanism is designed to further strengthen the search efficiency and boost the convergence rate. Experimental results demonstrate that our proposed method is more competitive than other state-of-the-art evolutionary algorithms for UCAV path planning problem considering the quality and stability of the final paths.

A Hybrid Differential Evolution Algorithm and Its Application in Unmanned Combat Aerial Vehicle Path Planning

CIPDE and JADE are two powerful and effective Differential Evolution (DE) algorithms with strong exploration and exploitation capabilities. In order to take advantage of these two algorithms, we present a hybrid differential evolution algorithm combining modified CIPDE (MCIPDE) with modified JADE (MJADE) called CIJADE. In CIJADE, the population is first partitioned into two subpopulations according to the fitness value, i.e., superior and inferior subpopulations, to maintain the population diversity. The superior subpopulation evolves using the operation defined in MCIPDE. The MCIPDE adds an external archive to the mutation scheme to enhance the population diversity and exploration capability of original CIPDE. While the inferior subpopulation evolves using the operation defined in MJADE. The MJADE modifies the original JADE by adjusting the parameter p in linear decreasing way to balance the exploration and exploitation ability of original JADE. A new crossover operation is designed to original JADE to deal with the problem of stagnation. Furthermore, the parameters CR and F values of CIJADE are updated according to a modified parameter adaptation strategy in each generation. We validate the performance of the proposed CIJADE algorithm over 28 benchmark functions of the CEC2013 benchmark set. The experimental results indicate that the proposed CIJADE performs better than the eleven popular stateof-the-art DE variants. What's more, we apply the proposed CIJADE to deal with Unmanned Combat Aerial Vehicle (UCAV) path planning problem. The simulation results show that the proposed CIJADE can efficiently find the optimal or near optimal flight path for UCAV.

Unmanned Combat Aerial Vehicle Path Planning by Brain Storm Optimization Algorithm

The use of the unmanned aerial vehicles is rapidly growing in ever wider range of applications where military use is among the oldest ones. One of the fundamental problems in the unmanned combat aerial vehicles control is the path planning problem that refers to establish the optimal route from the start position to the target, where optimality can be defined in numerous ways. Path planning represents a multi-objective constrained hard optimization problem. In this paper, we adjusted a recent swarm intelligence brain storm optimization algorithm for finding the unmanned combat aerial vehicle optimal path considering fuel consumption and safety degree. The proposed method was tested and compared to eleven different methods from literature. Based on the simulation results, it can be concluded that our proposed approach is robust, exhibits better performance in almost all cases and has potential for further improvements.

A modified symbiotic organisms search algorithm for unmanned combat aerial vehicle route planning problem

Route planning is the core component of unmanned combat aerial vehicle (UCAV) systems and the premise for implementation of airborne reconnaissance, surveillance, combat, and other tasks. The purpose is to find the optimal flight route under certain constraints, and its essence is a multi-constrained global optimisation problem. This paper presents a modified symbiotic organisms search algorithm based on the simplex method (SMSOS) to solve the UCAV path planning problem. In addition to the flight environment for the fixed threat area, this paper tested the flight environment of the randomly generated threat area because of the complexity of the actual battlefield threat area. After many simulation tests, it was concluded that SMSOS can find the shortest flight path while avoiding the threat areas. The experimental results show that SMSOS has faster convergence speed, higher precision, and stronger robustness than the other main swarm intelligence algorithms for solving the UCAV flight planning problem.

An Improved Heuristic Algorithm for UCAV Path Planning

The study of unmanned combat aerial vehicle (UCAV) path planning is increasingly important in military and civil field. This paper presents a new mathematical model and an improved heuristic algorithm based on Sparse A⁎ Search (SAS) for UCAV path planning problem. In this paper, flight constrained conditions will be considered to meet the flight restrictions and task demands. With three simulations, the impacts of the model on the algorithms will be investigated, and the effectiveness and the advantages of the model and algorithm will be validated.

Grey wolf optimizer for unmanned combat aerial vehicle path planning

Unmanned combat aerial vehicle (UCAV) path planning is a fairly complicated global optimum problem, which aims to obtain an optimal or near-optimal flight route with the threats and constraints in the combat field well considered. A new meta-heuristic grey wolf optimizer (GWO) is proposed to solve the UCAV two-dimension path planning problem. Then, the UCAV can find the safe path by connecting the chosen nodes of the two-dimensional coordinates while avoiding the threats areas and costing minimum fuel. Conducted simulations show that the proposed method is more competent for the UCAV path planning scheme than other state-of-the-art evolutionary algorithms considering the quality, speed, and stability of final solutions.

An improved artificial bee colony algorithm based on balance-evolution strategy for unmanned combat aerial vehicle path planning.

Unmanned combat aerial vehicles (UCAVs) have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. UCAV path planning aims to obtain an optimal flight route with the threats and constraints in the combat field well considered. In this work, a novel artificial bee colony (ABC) algorithm improved by a balance-evolution strategy (BES) is applied in this optimization scheme. In this new algorithm, convergence information during the iteration is fully utilized to manipulate the exploration/exploitation accuracy and to pursue a balance between local exploitation and global exploration capabilities. Simulation results confirm that BE-ABC algorithm is more competent for the UCAV path planning scheme than the conventional ABC algorithm and two other state-of-the-art modified ABC algorithms.