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• A mathematical model of the UAV route planning problem is established. • A hybrid ACO-IWD algorithm, which combines the advantages of the ACO and IWD algorithms, is proposed to solve the UAV route planning problem. • A novel node selection strategy is designed to guide the attempts of the agents to search for routes in a reasonable direction and increase their search efficiency. • The advantages of the proposed algorithm over the state-of-the-art algorithms are demonstrated by extensive experimental results. Route planning is a crucial element in unmanned aerial vehicle (UAV) systems, particularly in autonomous UAV technology. In the past decades, various algorithms have been proposed for UAV route planning. However, they still have defects, such as stagnation and slow search rates. In this study, a novel hybrid algorithm which integrates ant colony optimization (ACO) and intelligent water drop (IWD) is proposed for UAV route planning. First, the advantages of the IWD and ACO algorithms are combined in an iterative strategy, to ensure mutual cooperation via exchange of information for route optimization. Initially, the water drops optimize the soil and pheromones within the environment simultaneously to generate good approximate solutions and an initial pheromone distribution for the ant colony. Based thereupon, the ant colony roams the solution space to further optimize the routes, thus combining the exploration potential and exploitability of two types of agents. Additionally, a novel node selection strategy is proposed to guide the agents' route planning along a reasonable direction. Compared with state-of-the-art algorithms, the convergence accuracy, success rate, and stability of the proposed algorithm exhibited significant improvements of approximately 8.25%, 4.20%, and 66.20%, respectively. [ABSTRACT FROM AUTHOR]

Until now, routes evaluation for unmanned aerial vehicle still faces a variety of difficulties, which is due to the fact that during route evaluating, subjective judgments, quantitative data, and random information need to be considered simultaneously. In this paper, by formulating route evaluation as a multi-criteria decision making problem including uncertainties, an integrated route evaluation approach based on type-2 fuzzy sets is proposed. Firstly, a systemic evaluation framework that incorporates models for scoring evaluation criteria is proposed. Specifically, a survivability model incorporating dynamics and uncertainties in battlefield is developed, including some special features, such as calculating the probability of detecting, tracking, and destroying an unmanned aerial vehicle, and modeling the location of pop-up threats as a Markov chain. Then, type-2 fuzzy sets are introduced to represent linguistic values, managing linguistic uncertainty effectively and making the evaluation process realistic and reliable. Finally, the architecture of perceptual computer is extended, and the computing with words engine by means of linguistic weighted average method is adopted to obtain the overall score of each route, enabling both random and fuzzy uncertainties existing universally in the data to be effectively managed in a unified format. The proposed method has the advantages of diverse inputs such as numbers, probability distributions and words. All these can be aggregated to a final decision. Furthermore, it provides a useful tool to handle route evaluation problem in a highly reliable and intelligent manner, and it can be applied to solve multi-criteria decision making problems in many disciplines. Experimental results demonstrate the feasibility and effectiveness of our method. [ABSTRACT FROM AUTHOR]

This study investigates the problem of cooperative search path planning for multiple unmanned aerial vehicles. Firstly, we present a search probability map based environmental model, and formulate a multi-UAV cooperative search path optimization problem. Then, we propose a solution strategy based on an improved intelligent water drops (IWD) optimization algorithm. Compared with the traditional IWD algorithm, the proposed algorithm can simultaneously model the cooperation and competition among UAVs by introducing several heterogeneous water drop populations and a co-evolutionary mechanism. Water drops in the same population cooperate with each other, whereas water drops among different populations compete with each other to obtain the optimal path and reduce meaningless search effort. Meanwhile, the proposed algorithm can improve its search capability using a new soil update mechanism. Simulation results demonstrate the advantages of the proposed method over other baselines. [ABSTRACT FROM AUTHOR]