Equilibrium Optimizer with Generalized Opposition-based Learning for Multiple Unmanned Aerial Vehicles Path Planning

Multiple Unmanned Aerial Vehicles (UAVs) path planning is the benchmark problem of multiple UAVs application, which belongs to the non-deterministic polynomial problem. Its objective is to require multiple UAVs flying safely to the goal position according to their specific start position in three-dimensional space. This issue can be defined as a high-dimensional optimization problem, the solution of which requires optimization techniques with global optimization capabilities. Equilibrium optimizer (EO) is a population-based meta-heuristic algorithm. In order to improve the optimization ability of EO to solve high dimensional problems, this paper proposes a modified equilibrium optimizer with generalized opposition-based learning (MGOEO), which improves the population activity by increasing the internal mutation and cross of the population. In addition, the generalized opposition-based learning is used to construct the population, which can effectively ensure that the algorithm has ability to jump out of the limitation of local optimal. Firstly, numerical experiments show that MGOEO has better optimization precision than EO and several other swarm intelligent algorithms. Then, the paths of UAVs are simulated in three different obstacle environments. The simulation results show that MGOEO can obtain safe and smooth paths, which are better than EO and other eight state-of-the-art optimization algorithms.

3D placements of drones in a millimeter-wave network to maximize the lifetime of wireless devices

In the last few years, the use of drones is increasing day by day in wireless networks and the applications of them are rapidly increased on different sides. Now, we can use the drone as an aerial base station (BS) to support cellular networks in emergency cases and in natural disasters. To take the advantage of both drones and fifth-generation (5G) and link between their features, we study an aerial BS considering millimeter waves (mm-waves). In this paper, we optimize the 3D placements for multiple unmanned aerial vehicles (UAVs) in an mm-wave network to achieve maximum time durations of the uplink transmission. First, we present a formulation for the placement problem, where we aim to allocate 3D locations for multiple UAVs to achieve the maximum sum of time durations of uplink transmissions. We propose an efficient algorithm to find the placements of UAVs. We propose an algorithm that starts by grouping the wireless devices into a number of clusters, and each cluster is served by a single UAV. After the clustering process, it applies the gradient projection-based algorithm (GP) or particle swarm optimization (PSO) in each cluster. In the results section, our proposed approach and the center projection algorithm will be compared to prove the efficiency of our approach.

Tree structures are employed by pigeons as leadership structures to flock

Collective behaviors displaying a variety of fascinating movement patterns are thought to be products of complex interplay among individuals. Previous studies have proposed the hierarchical leadership networks and the coexistence of compromise and leadership in pigeon flocks, but these conclusions have not been confirmed by theoretical or modeling studies. Here, based on the same datasets, using a more reasonable research route, we found a more concise leadership structure in pigeon flocks. i.e., the tree structure, which was verified by our modeling studies. We showed that each individual may follow its only pilot (leader) during collective flights of pigeon flocks, and the only top leader of a certain flock determines the flight direction of the whole flock. Our results confirmed the leadership hypothesis, denying the illusion of compromise between individuals at the same level. The findings shed light on the hierarchical leadership structure in pigeon flocks and have implications for artificial collective systems, e.g., autonomous formation control of multiple unmanned aerial vehicles and unmanned surface vehicles.

Model decomposition-based optimal formation control for multiple unmanned aerial vehicles

This paper deals with the optimal formation control problem based on model decomposition for multiple unmanned aerial vehicles (UAVs). The main contribution of this paper is to integrate the formation control and the trajectory tracking into one unified feedforward control and feedback control framework in an optimal mode. We first establish the dynamic model of the leader-follower UAV formation system, and the communication network topology which only depends on the position information given by the leader. Second, to reduce the complexity of the model, each follower is decomposed into three isolated subsystems. Third, a step-by-step formation controller design scheme decomposed into feedforward control and optimal control of formation control is proposed. Finally, the proposed scheme has been extensively simulated and the results demonstrate the stability and the optimality.