Due to their strong risk tolerance, low manufacturing cost and good maneuverability, unmanned aerial vehicles (UAVs) have been widely used in various fields. Among related challenges, coordinated task assignment is a key scientific issue for autonomous control of UAVs. In this paper, based on the idea of fuzzy C-means (FCM) clustering and the ant colony optimization (ACO) algorithm, a cooperative multiple task reallocation problem with target precedence constraints for heterogeneous UAVs is proposed. The contributions of this research are the performance evaluation of the original algorithms in a dynamic context, consideration of changes in some attributes of the environment, and the extension of these algorithms to properly address more realistic dynamic emergent adjustment scenarios. According to the corresponding task reallocation strategy, the scenarios are divided into three categories: the complete redistribution strategy can effectively cope with scenarios where tasks have changed significantly, the partial adjustment strategy can induce partial responses to the changes of individual tasks, and group redistribution can effectively solve the problem of task target threat rating changes. The simulation results show that the dynamic reallocation model of multi-UAV tasks in dynamic emergent adjustment scenarios can achieve better performance to complete the corresponding tasks based on the proposed scheme. In addition, we deployed the developed graphical modeling and analysis software (GMAS) platform to implement the dynamic reallocation model of multi-UAV tasks in dynamic emergent scenarios, and the validity and reliability of the proposed task reallocation model were verified.

Effectively improving the optimization performance of artificial electric field algorithm (AEFA) and broadening its application domain can aid in providing robot path planning in three-dimensional (3D) complex scenes. This paper effectively proposes an improved AEFA (I-AEFA) and creatively applies it to robot path planning. The algorithm introduces three mechanisms to enhance the exploration ability and convergence accuracy of the population: parameter adaptation, reverse learning, and Cauchy mutation. Next, the benchmark terrain model accurately models the 3D environment, and the global path planning problem is solved using a combination of I-AEFA and cubic spline interpolation. Then, a large number of virtual simulation experiments are conducted to evaluate the algorithm's three improved mechanisms, various control point counts, as well as single and multi-robot configurations before migrating the algorithm to the graphical modeling and analysis software (GMAS) for hardware-in-the-loop simulation experiments. Finally, the experimental results are analyzed qualitatively and quantitatively using a variety of visualization techniques and two nonparametric test methods, demonstrating that the I-AEFA proposed in this paper has good optimization performance and is highly effective, reliable, and scalable for solving robot path planning problems.

The multi-UAV path planning method based on artificial potential field (APF) has the advantage of rapid processing speed and the ability to deal with dynamic obstacles, though some problems remain—such as a lack of consideration of the initial heading constraint of the UAVs, making it easy to fall into a local minimum trap, and the path not being sufficiently smooth. Consequently, a fixed-wing UAV formation path planning method based on piecewise potential field (PPF) is proposed, where the problem of UAV formation flight path planning in different states can be solved by suitable design of the PPF function. Firstly, the potential field vector can be used to represent the potential field functions of obstacles and target points to meet the kinematic constraints of the UAV. Secondly, the local minimum region can be detected, the additional potential field vector being set to break away from this region. Finally, the change rules of the potential field vector of a UAV in the formation reconstruction scene can be designed, a smooth formation flight track being assured by adjusting the corresponding speed of each UAV track point. Considering the path planning of a five-UAV formation as an example, we conducted simulation experiments. The results showed that—compared with the existing methods based on APF—the results obtained using the PPF-based method considered the initial heading limits of the UAVs, the planned path being considerably smoother. Moreover, the proposed method could plan multiple UAV tracks, satisfying the known constraints without conflict in complex scenarios.

Deep reinforcement learning has demonstrated flexibility advantages in the control field of quadrotor aircraft. However, when there are sudden disturbances in the environment, especially special disturbances beyond experience, the algorithm often finds it difficult to maintain good control performance. Additionally, due to the randomness in the algorithm’s exploration of states, the model’s improvement efficiency during the training process is low and unstable. To address these issues, we propose a deep reinforcement learning framework guided by Multi-PID Self-Attention to tackle the challenges in the training speed and environmental adaptability of quadrotor aircraft control algorithms. In constructing the simulation experiment environment, we introduce multiple disturbance models to simulate complex situations in the real world. By combining the PID control strategy with deep reinforcement learning and utilizing the multi-head self-attention mechanism to optimize the state reward function in the simulation environment, this framework achieves an efficient and stable training process. This experiment aims to train a quadrotor simulation model to accurately fly to a predetermined position under various disturbance conditions and subsequently maintain a stable hovering state. The experimental results show that, compared with traditional deep reinforcement learning algorithms, this method achieves significant improvements in training efficiency and state exploration ability. At the same time, this study deeply analyzes the application effect of the algorithm in different complex environments, verifies its superior robustness and generalization ability in dealing with environmental disturbances, and provides a new solution for the intelligent control of quadrotor aircraft.

Model reuse is a key issue to be resolved in parallel and distributed simulation at present. However, component models built by different domain experts usually have diversiform interfaces, couple tightly, and bind with simulation platforms closely. As a result, they are difficult to be reused across different simulation platforms and applications. To address the problem, this paper first proposed a reusable component model framework. Based on this framework, then our reusable model development approach is elaborated, which contains two phases: (1) domain experts create simulation computational modules observing three principles to achieve their independence; (2) model developer encapsulates these simulation computational modules with six standard service interfaces to improve their reusability. The case study of a radar model indicates that the model developed using our approach has good reusability and it is easy to be used in different simulation platforms and applications.