An intelligent cooperative mission planning scheme of UAV swarm in uncertain dynamic environment

This paper addressed a well-documented open problem regarding multiple unmanned aerial vehicles (multi-UAVs) formation keeping. A cooperative guidance control method based on the back-stepping approach is proposed to rapidly form the desired formation and reach the multi-UAV steady state. The UAV formation system consists of four UAVs, forming a regular triangle formation. One of the UAVs is a virtual leader and is located at the center of the triangle; the other three UAVs are located at the vertices of the triangle. The forward speed of leader is used as the forward direction of the formation, and the followers follow the leader in a formation flight. The error dynamics model of each follower is established by using the leader guidance mechanism, and the communication mode between any two UAVs is established based on graph theory. In addition, the guidance control law is obtained via the back-stepping approach. We reasonably construct the Lyapunov function to prove the stability of the proposed cooperative guidance control law in formation aggregation and keeping. Moreover, the proposed method is compared with the model prediction method (MPC) and the Lyapunov method to further verify the effectiveness of the proposed method. The simulation results show that each UAV can converge to the desired motion trajectory and fly in the desired formation with fast convergence speed and small steady-state error.

Multi-UAV Formation Control Based on a Novel Back-Stepping Approach

Unmanned aerial vehicles (UAVs) have been widely applied in civilian and military applications due to their high autonomy and strong adaptability. Although UAVs can achieve effective cost reduction and flexibility enhancement in the development of large-scale systems, they result in a serious path planning and task allocation problem. Coverage path planning, which tries to seek flight paths to cover all of regions of interest, is one of the key technologies in achieving autonomous driving of UAVs and difficult to obtain optimal solutions because of its NP-Hard computational complexity. In this paper, we study the coverage path planning problem of autonomous heterogeneous UAVs on a bounded number of regions. First, with models of separated regions and heterogeneous UAVs, we propose an exact formulation based on mixed integer linear programming to fully search the solution space and produce optimal flight paths for autonomous UAVs. Then, inspired from density-based clustering methods, we design an original clustering-based algorithm to classify regions into clusters and obtain approximate optimal point-to-point paths for UAVs such that coverage tasks would be carried out correctly and efficiently. Experiments with randomly generated regions are conducted to demonstrate the efficiency and effectiveness of the proposed approach. 

A Clustering-Based Coverage Path Planning Method for Autonomous Heterogeneous UAVs

The dynamic uncertain environment and complex tasks determine that the unmanned aerial vehicle (UAV) system is bound to develop towards clustering, autonomy, and intelligence. In this article, we present a comprehensive survey of UAV swarm intelligence from the hierarchical framework perspective. Firstly, we review the basics and advances of UAV swarm intelligent technology. Then we look inside to investigate the research work by classifying UAV swarm intelligence research into five layers, i.e., decision-making layer, path planning layer, control layer, communication layer, and application layer. Furthermore, the relationship between each level is explicitly illustrated, and the research trends of each layer are given. Finally, limitations and possible technology trends of swarm intelligence are also covered to enable further research interests. Through this in-depth literature review, we intend to provide novel insights into the latest technologies in UAV swarm intelligence.

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UAV Swarm Intelligence: Recent Advances and Future Trends

For multiple unmanned aerial vehicles (UAVs) performing aerial search-attack tasks, there is a tradeoff between maximizing total benefit and minimizing consumption under the validity of constraints. This article proposes a dynamic discrete pigeon-inspired optimization algorithm to handle cooperative search-attack mission planning for UAVs, which integrates the centralized task assignment and distributed path generation aspects of the problem. Besides, a solution acceptance strategy is proposed to avoid frequent task switching. To design a reasonable objective function, the probability map is constructed and updated by Bayes formula to guide the following search motion, and a response threshold sigmoid model is adopted for target allocation during executing attack. Moreover, the flyable trajectories are generated by B-spline curves based on the simplified waypoints. Finally, numerical experiments prove that the proposed methods can provide feasible solutions for multiple UAVs considering different scenarios, such as the absence or presence of threats and insufficient resources. The results also show that the solution acceptance strategy is effective to improve performance. Moreover, the extensible mission planning system also integrates with an interactive 3D visualization simulation module, where the multi-UAV coordinated flight processes are demonstrated dynamically.

Dynamic Discrete Pigeon-Inspired Optimization for Multi-UAV Cooperative Search-Attack Mission Planning

Cooperative search-attack mission planning for multi-UAV based on intelligent self-organized algorithm

The invention discloses a self-organizing cooperative reconnaissance and strike mission planning method for multi-unmanned-aerial-vehicle (UAV) clusters, which includes the following steps: S1, a two-dimensional mission area is discretized, and a discrete state space for UAVs to make reconnaissance and strike mission decisions is established by gridding; S2, the route points of UAVs are obtained through decision making based on a distributed ant colony algorithm, and cooperative search and attack by a multi-UAV cluster is realized; each ant agent in the distributed ant colony algorithm corresponds to one UAV, the ant agents are connected through a network to form a distributed system, and the ant agents exchange their respective information through a communication network; and an independent processor is allocated to each ant agent, and each ant agent makes autonomous decisions according to the state thereof and the known states of other ant agents; and S3, a flyable route from the current route point to the route point at next moment is generated. Compared with the prior art, the method has better robustness and can adapt to the joining and exiting of UAVs.

Chen Gao

Papers

Cooperative search-attack mission planning for multi-UAV based on intelligent self-organized algorithm

Self-organizing cooperative reconnaissance and strike mission planning method for multi-unmanned-aerial-vehicle clusters