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Cooperative path planning of multiple unmanned aerial vehicles is a complex task. The collision avoidance and coordination between multiple unmanned aerial vehicles is a global optimal issue. This research addresses the path planning of multi-colonies with multiple unmanned aerial vehicles in dynamic environment. To observe the model of whole scenario, we combine maximum–minimum ant colony optimization and differential evolution to make metaheuristic optimization algorithm. Our designed algorithm, controls the deficiencies of present classical ant colony optimization and maximum–minimum ant colony optimization, has the contradiction among the excessive information and global optimization. Moreover, in our proposed algorithm, maximum–minimum ant colony optimization is used to lemmatize the pheromone and only best ant of each colony is able to construct the path. However, the path escape by maximum–minimum ant colony optimization and it treated as the object for differential evolution constraints. Now, it is ensuring to find the best global colony, which provides optimal solution for the entire colony. Furthermore, the proposed approach has an ability to increase the robustness while preserving the global convergence speed. Finally, the simulation experiment results are performed under the rough dynamic environment containing some high peaks and mountains.

This article reviews remotely operated underwater vehicle (ROUV) and its different types focusing on the control systems. This study offers a brief introduction of unmanned underwater vehicle (UUV) together with ROUV. Underwater robots are designed to work as an alternative to humans because of a difficult and hazardous underwater environment. The applications and demand of marine robots are increasing with the passage of time. There are several research articles and publications available on these topics but, a complete review of old and recent research about this technology is still hard to find. This article also assesses some recently published research papers on underwater systems. It presents the comparison of different control systems and designs of underwater vehicles. There have been major developments in marine technology depending on the needs, applications and cost of different missions. Scientists design many remotely operated vehicles based on the educational or industrial purposes. This article is presented in order to help and assist the future researchers as a massive review of the field of remotely operated underwater vehicles and their possible future developments are presented.

Attitude, speed, and position of unmanned aerial vehicles are susceptible to wind disturbance. The types, characteristics, and mathematical models of the wind, which have great influence on unmanned aerial vehicle in the low-altitude environment, are summarized, including the constant wind, turbulent flow, many kinds of wind shear, and the propeller vortex. Combined with the mathematical model of the unmanned aerial vehicle, the mechanism of unmanned aerial vehicle movement in the wind field is illustrated from three different kinds of viewpoints including velocity viewpoint, force viewpoint, and energy viewpoint. Some simulation tests have been implemented to show the effects of different kinds of wind on unmanned aerial vehicle’s path and flight states. Finally, some proposals are presented to tell reader in which condition, which wind model should be added to simulation, and how to enhance the stability of unmanned aerial vehicle for different kinds of wind fields.

Arm mounted unmanned aerial vehicles provide more feasible and attractive solution to manipulate objects in remote areas where access to arm mounted ground vehicles is not possible. In this research, an under-actuated quadrotor unmanned aerial vehicle model equipped with gripper is utilized to grab objects from inaccessible locations. A dual control structure is proposed for controlling and stabilization of the moving unmanned aerial vehicle along with the motions of the gripper. The control structure consists of model reference adaptive control augmented with an optimal baseline controller. Although model reference adaptive control deals with the uncertainties as well as attitude controlling of unmanned aerial vehicle, baseline controller is utilized to control the gripper, remove unwanted constant errors and disturbances during arm movement. The proposed control structure is applied in 6-degree-of-freedom nonlinear model of a quadrotor unmanned aerial vehicle equipped with gripper having (2 degrees of freedom) robotic limb; it is applicable for the simulations to desired path of unmanned aerial vehicle and to grasp object. Moreover, the efficiency of the presented control structure is compared with optimal baseline controller. It is observed that the proposed control algorithm has good transient behavior, better robustness in the presence of continuous uncertainties and gripper movement involved in the model of unmanned aerial vehicle.

To improve the performance of multi-unmanned aerial vehicle path planning in plateau narrow area, a control strategy based on Cauchy mutant pigeon-inspired optimization algorithm is proposed in this article. The Cauchy mutation operator is chosen to improve the pigeon-inspired optimization algorithm by comparing and analyzing the changing trend of fitness function of the local optimum position and the global optimum position when dealing with unmanned aerial vehicle path planning problems. The plateau topography model and plateau wind field model are established. Furthermore, a variety of control constrains of unmanned aerial vehicles are summarized and modeled. By combining with relative positions and total flight duration, a cooperative path planning strategy for unmanned aerial vehicle group is put forward. Finally, the simulation results show that the proposed Cauchy mutant pigeon-inspired optimization method gives better robustness and cooperative path planning strategy which are effective and advanced as compared with traditional pigeon-inspired optimization algorithm.

This study proposes a collective motion and self-organization control of a swarm of 10 UAVs, which are divided into two clusters of five agents each. A cluster is a group of UAVs in a dedicated area and multiple clusters make a swarm. This paper designs the 3D model of the whole environment by applying graph theory. To address the aforesaid issues, this paper designs a hybrid meta-heuristic algorithm by merging the particle swarm optimization (PSO) with the multi-agent system (MAS). First, PSO only provides the best agents of a cluster. Afterward, MAS helps to assign the best agent as the leader of the nth cluster. Moreover, the leader can find the optimal path for each cluster. Initially, each cluster contains agents at random positions. Later, the clusters form a formation by implementing PSO with the MAS model. This helps in coordinating the agents inside the nth cluster. However, when two clusters combine and make a swarm in a dynamic environment, MAS alone is not able to fill the communication gap of n clusters. This study does it by applying the Vicsek-based MAS connectivity and synchronization model along with dynamic leader selection ability. Moreover, this research uses a B-spline curve based on simple waypoint defined graph theory to create the flying formations of each cluster and the swarm. Lastly, this article compares the designed algorithm with the NSGA-II model to show that the proposed model has better convergence and durability, both in the individual clusters and inside the greater swarm.

As time progresses with vast development of information technology, a large number of industries are more dependent on network connections for sensitive business trading and security matters. Communications and networks are highly vulnerable to threats because of increase in hacking. Personnel, governments, and armed classified networks are more exposed to difficulties, so the need of the hour is to install safety measures for network to prevent illegal modification, damage, or leakage of serious information. Intrusion detection, an important entity towards network security, has the ability to observe network activity as well as detect intrusions/attacks. This study highlights the developing research about the application of machine learning and data mining in Internet security. We provide background, enthusiasm, discussion of challenges, and recommendations for the application of ML/DM in the field of intrusion detection.

In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control (MRAC), in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC) motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability.

Novel hardware architectures for dynamic reconfigurable implementation of 64-bit MISTY1 and KASUMI block ciphers are proposed to enhance the performance of cryptographic chips for secure IoT applications. The SRL32 primitive (Reconfigurable Look up Tables—RLUTs) and DPR (Dynamic Partial Reconfiguration) are employed to reconfigure single round MISTY1 / KASUMI algorithms on the run-time. The RLUT based architecture attains dynamic logic functionality without extra hardware resources by internally modifying the LUT contents. The proposed adaptive reconfiguration can be adopted as a productive countermeasure against malicious attacks with the added advantage of less reconfiguration time (RT). On the other hand, the block architecture reconfigures the core hardware by externally uploading the partial bit stream and has significant advantages in terms of low area implementation and power reduction. Implementation was carried out on FPGA, Xilinx Virtex 7. The results showed remarkable results with very low area of 668 / 514 CLB slices consuming 460 / 354 mW for RLUT and DPR architectures respectively. Moreover, the throughput obtained for RLUT architecture was found as 364 Mbps with very less RT of 445 nsec while DPR architecture achieved speed of 176 Mbps with RT of 1.1 msec. The novel architectures outperform the stand-alone existing hardware designs of MISTY1 and KASUMI implementations by adding the dynamic reconfigurability while at the same achieving high performance in terms of area and throughput. Design details of proposed unified architectures and comprehensive analysis is described.

Unmanned Aerial Vehicles (UAVs) seem to be the most efficient way of achieving the intended aerial tasks, according to recent improvements. Various researchers from across the world have studied a variety of UAV formations and path planning methodologies. However, when unexpected obstacles arise during a collective flight, path planning might get complicated. The study needs to employ hybrid algorithms of bio-inspired computations to address path planning issues with more stability and speed. In this article, two hybrid models of Ant Colony Optimization were compared with respect to convergence time, i.e., the Max-Min Ant Colony Optimization approach in conjunction with the Differential Evolution and Cauchy mutation operators. Each algorithm was run on a UAV and traveled a predetermined path to evaluate its approach. In terms of the route taken and convergence time, the simulation results suggest that the MMACO-DE technique outperforms the MMACO-CM approach.