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The formation of unmanned aerial vehicle (UAV) swarm plays a critical role in numerous applications, such as unmanned agriculture, environmental monitoring and cooperative fencing. Meanwhile, the self-organized swarm model exhibits excellent performance in amorphous formation flight, and its collective motion pattern displays great potential in dense obstacle avoidance. The paper aims to realize the formation maintenance of UAVs while combining the advantage of the self-organized swarm model in avoiding dense obstacles. Thereby enhancing the flexibility, adaptability and safety of UAV swarms in dense and unpredictable scenarios.

In this paper, a self-organized formation (SOF) swarm model with a constrained coordination mechanism is proposed. A global information-based formation rule is designed to flexibly maintain the formation. A constraint coordination mechanism is designed to resolve the problem of constraint conflicts between formation rules and self-organized behavior rules. The model introduces a new obstacle avoidance rule to prevent deadlocks. Extensive experiments including simulations, real flights and comparative experiments are conducted to evaluate the performance of the model.

The simulation results show that SOF swarm enables the formation elastically to dense obstacles. Compared to the Vasarhelyi model, swarm performance metrics are improved. For example, the task completion time of SOF swarm is reduced by 16%, 28% and 39% across the three obstacle densities, and the order of SOF swarm is improved by 4%, 13% and 18%, respectively. The proposed model is also validated with a swarm of seven quadcopters that can successfully navigate and maintain formation in a real-world indoor environment with dense obstacles. Video at: https://youtu.be/V8hYgOHxWls.

The proposed formation rule is based on global information construction, which presents challenges in terms of communication overhead in distributed systems.

An SOF swarm model is proposed, which achieves formation maintenance by incorporating formation rule and constraint coordination mechanism and improves obstacle avoidance performance by introducing a new obstacle avoidance rule. After real UAVs verification, the model is feasible for practical deployment and provides a new solution to the formation flight and formation maintenance problems encountered in dense environments.

Methylene blue (MB) is classified as a cationic dye which is widely used as chemical indicator, coloring agent and biological stain. The discharge of this dye to the water streams is harmful to the human beings. For this reason, this study investigated the removal of MB from aqueous solution by hydrogel nanocomposite.

In experimental part, at first, ultraviolet (UV)-curable hydrogel/chitosan nanocomposite, which improves its elasticity by urethane acrylate, was synthesized and characterized by FTIR and SEM analysis. Afterward, the synthesized hydrogel nanocomposite was applied for the removal of MB and the influence of operational condition including nanocomposite loading, dye concentration, contact time and pH of solution was specified. Moreover, isotherm studies as well as kinetics survey were performed.

Langmuir, Freundlich, Brunauer, Emmett and Teller and Tempkin adsorption isotherms were assessed for the analysis of experimental data indicating the Freundlich isotherm was the best fitted one. The adsorption kinetics data was examined indicating the adsorption kinetics appropriate to pseudo-second-order kinetics model.

The predominant water absorption property of the UV-curable hydrogel/chitosan nanocomposite to 8.5 steps and outstanding adsorption capacity for the elimination of MB on hydrogel nanocomposite subscribed that the synthesized hydrogel could be a favorable adsorbent for simultaneous absorption of water and removal of cationic dyes.