scholarly journals Motion-Encoded Electric Charged Particles Optimization for Moving Target Search Using Unmanned Aerial Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6568
Author(s):  
Mohammed A. Alanezi ◽  
Houssem R. E. H. Bouchekara ◽  
Mohammad S. Shahriar ◽  
Yusuf A. Sha’aban ◽  
Muhammad S. Javaid ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Unmanned Aerial Vehicles ◽  
Optimization Problem ◽  
Charged Particles ◽  
Moving Target ◽  
Moving Targets ◽  
Aerial Vehicles ◽  
Motion Paths ◽  
Moving Target Search ◽  
Different Characteristics

In this paper, a new optimization algorithm called motion-encoded electric charged particles optimization (ECPO-ME) is developed to find moving targets using unmanned aerial vehicles (UAV). The algorithm is based on the combination of the ECPO (i.e., the base algorithm) with the ME mechanism. This study is directly applicable to a real-world scenario, for instance the movement of a misplaced animal can be detected and subsequently its location can be transmitted to its caretaker. Using Bayesian theory, finding the location of a moving target is formulated as an optimization problem wherein the objective function is to maximize the probability of detecting the target. In the proposed ECPO-ME algorithm, the search trajectory is encoded as a series of UAV motion paths. These paths evolve in each iteration of the ECPO-ME algorithm. The performance of the algorithm is tested for six different scenarios with different characteristics. A statistical analysis is carried out to compare the results obtained from ECPO-ME with other well-known metaheuristics, widely used for benchmarking studies. The results found show that the ECPO-ME has great potential in finding moving targets, since it outperforms the base algorithm (i.e., ECPO) by as much as 2.16%, 5.26%, 7.17%, 14.72%, 0.79% and 3.38% for the investigated scenarios, respectively.

Lyapunov vector-based formation tracking control for unmanned aerial vehicles with obstacle/collision avoidance

2019 ◽  
Vol 42 (5) ◽  
pp. 942-950
Author(s):  
Kai Chang ◽  
Dailiang Ma ◽  
Xingbin Han ◽  
Ning Liu ◽  
Pengpeng Zhao
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Potential Field ◽  
Formation Control ◽  
Control Method ◽  
Spherical Surface ◽  
Moving Target ◽  
Local Optima ◽  
Formation Tracking ◽  
Aerial Vehicles ◽  
Repulsive Forces

This paper presents a formation control method to solve the moving target tracking problem for a swarm of unmanned aerial vehicles (UAVs). The formation is achieved by the artificial potential field with both attractive and repulsive forces, and each UAV in the swarm will be driven into a leader-centered spherical surface. The leader is controlled by the attractive force by the moving target, while the Lyapunov vectors drive the leader UAV to a fly-around circle of the target. Furthermore, the rotational vector-based potential field is applied to achieve the obstacle avoidance of UAVs with smooth trajectories and avoid the local optima problem. The efficiency of the developed control scheme is verified by numerical simulations in four scenarios.

Tracking Multiple Ground Targets in Urban Environments Using Cooperating Unmanned Aerial Vehicles

2015 ◽  
Vol 137 (5) ◽  
Author(s):  
Vitaly Shaferman ◽  
Tal Shima
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Optimization Problem ◽  
Task Assignment ◽  
Monte Carlo Study ◽  
Urban Environments ◽  
Test Bed ◽  
Team Members ◽  
Distributed Approach ◽  
Aerial Vehicles ◽  
Cooperative Tracking

A distributed approach is proposed for planning a cooperative tracking task for a team of heterogeneous unmanned aerial vehicles (UAVs) tracking multiple predictable ground targets in a known urban environment. The solution methodology involves finding visibility regions, from which the UAV can maintain line-of-sight to each target during the scenario, and restricted regions, in which a UAV cannot fly, due to the presence of buildings or other airspace limitations. These regions are then used to pose a combined task assignment and motion planning optimization problem, in which each UAV's cost function is associated with its location relative to the visibility and restricted regions, and the tracking performance of the other UAVs in the team. A distributed co-evolution genetic algorithm (CEGA) is derived for solving the optimization problem. The proposed solution is scalable, robust, and computationally parsimonious. The algorithm is centralized, implementing a distributed computation approach; thus, global information is used and the computational workload is divided between the team members. This enables the execution of the algorithm in relatively large teams of UAVs servicing a large number of targets. The viability of the algorithm is demonstrated in a Monte Carlo study, using a high fidelity simulation test-bed incorporating a visual database of an actual city.

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