scholarly journals Smooth 3D Path Planning by Means of Multiobjective Optimization for Fixed-Wing UAVs

Electronics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 51 ◽  
Author(s):  
Franklin Samaniego ◽  
Javier Sanchis ◽  
Sergio Garcia-Nieto ◽  
Raul Simarro
Keyword(s):  
Path Planning ◽  
Multiobjective Optimization ◽  
Unmanned Aerial Vehicles ◽  
Design Methodology ◽  
Optimization Problem ◽  
Three Dimensional ◽  
Control Points ◽  
3D Space ◽  
Single Solution ◽  
Constant Altitude

Demand for 3D planning and guidance algorithms is increasing due, in part, to the increase in unmanned vehicle-based applications. Traditionally, two-dimensional (2D) trajectory planning algorithms address the problem by using the approach of maintaining a constant altitude. Addressing the problem of path planning in a three-dimensional (3D) space implies more complex scenarios where maintaining altitude is not a valid approach. The work presented here implements an architecture for the generation of 3D flight paths for fixed-wing unmanned aerial vehicles (UAVs). The aim is to determine the feasible flight path by minimizing the turning effort, starting from a set of control points in 3D space, including the initial and final point. The trajectory generated takes into account the rotation and elevation constraints of the UAV. From the defined control points and the movement constraints of the UAV, a path is generated that combines the union of the control points by means of a set of rectilinear segments and spherical curves. However, this design methodology means that the problem does not have a single solution; in other words, there are infinite solutions for the generation of the final path. For this reason, a multiobjective optimization problem (MOP) is proposed with the aim of independently maximizing each of the turning radii of the path. Finally, to produce a complete results visualization of the MOP and the final 3D trajectory, the architecture was implemented in a simulation with Matlab/Simulink/flightGear.

A biomimetic 3D airflow sensor made of an array of two piezoelectric metal-core fibers

Sensor Review ◽  
2017 ◽  
Vol 37 (3) ◽  
pp. 312-321 ◽  
Author(s):  
Yixiang Bian ◽  
Can He ◽  
Kaixuan Sun ◽  
Longchao Dai ◽  
Hui Shen ◽  
...  
Keyword(s):  
Design Methodology ◽  
Spherical Surface ◽  
Three Dimensional ◽  
Three Dimensions ◽  
Piezoceramic Cylinder ◽  
Metal Core ◽  
Content Type ◽  
3D Space ◽  
Highly Sensitive ◽  
Airflow Sensor

Purpose The purpose of this paper is to design and fabricate a three-dimensional (3D) bionic airflow sensing array made of two multi-electrode piezoelectric metal-core fibers (MPMFs), inspired by the structure of a cricket’s highly sensitive airflow receptor (consisting of two cerci). Design/methodology/approach A metal core was positioned at the center of an MPMF and surrounded by a hollow piezoceramic cylinder. Four thin metal films were spray-coated symmetrically on the surface of the fiber that could be used as two pairs of sensor electrodes. Findings In 3D space, four output signals of the two MPMFs arrays can form three “8”-shaped spheres. Similarly, the sensing signals for the same airflow are located on a spherical surface. Originality/value Two MPMF arrays are sufficient to detect the speed and direction of airflow in all three dimensions.

scholarly journals Optimal 3D Angle of Arrival Sensor Placement with Gaussian Priors

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1379
Author(s):  
Rongyan Zhou ◽  
Jianfeng Chen ◽  
Weijie Tan ◽  
Qingli Yan ◽  
Chang Cai
Keyword(s):  
Optimization Problem ◽  
Target Location ◽  
Mean Squared Error ◽  
Information Matrix ◽  
Sensor Placement ◽  
Three Dimensional ◽  
Invariance Property ◽  
Angle Of Arrival ◽  
3D Space ◽  
Sensor Placement Optimization

Sensor placement is an important factor that may significantly affect the localization performance of a sensor network. This paper investigates the sensor placement optimization problem in three-dimensional (3D) space for angle of arrival (AOA) target localization with Gaussian priors. We first show that under the A-optimality criterion, the optimization problem can be transferred to be a diagonalizing process on the AOA-based Fisher information matrix (FIM). Secondly, we prove that the FIM follows the invariance property of the 3D rotation, and the Gaussian covariance matrix of the FIM can be diagonalized via 3D rotation. Based on this finding, an optimal sensor placement method using 3D rotation was created for when prior information exists as to the target location. Finally, several simulations were carried out to demonstrate the effectiveness of the proposed method. Compared with the existing methods, the mean squared error (MSE) of the maximum a posteriori (MAP) estimation using the proposed method is lower by at least 25% when the number of sensors is between 3 and 6, while the estimation bias remains very close to zero (smaller than 0.15 m).

An Improved Flower Pollination Algorithm for Optimal Unmanned Undersea Vehicle Path Planning Problem

2016 ◽  
Vol 30 (04) ◽  
pp. 1659010 ◽  
Author(s):  
Yongquan Zhou ◽  
Rui Wang
Keyword(s):  
Path Planning ◽  
Three Dimensional ◽  
Optimization Method ◽  
Population Based ◽  
Flower Pollination Algorithm ◽  
Planning Problem ◽  
Flower Pollination ◽  
3D Space ◽  
Undersea Vehicle ◽  
Path Planning Problem

Path planning of Unmanned Undersea Vehicle (UUV) is a rather complicated global optimum problem which is about seeking a superior sailing route considering the different kinds of constrains under complex combat field environment. Flower pollination algorithm (FPA) is a new optimization method motivated by flower pollination behavior. In this paper, a variant of FPA is proposed to solve the UUV path planning problem in two-dimensional (2D) and three-dimensional (3D) space. Optimization strategies of particle swarm optimization are applied to the local search process of IFPA to enhance its search ability. In the progress of iteration of this improved algorithm, a dimension by dimension based update and evaluation strategy on solutions is used. This new approach can accelerate the global convergence speed while preserving the strong robustness of standard FPA. The realization procedure for this improved flower pollination algorithm is also presented. To prove the performance of this proposed method, it is compared with nine population-based algorithms. The experiment result shows that the proposed approach is more effective and feasible in UUV path planning in 2D and 3D space.

A meta-heuristic based three-dimensional path planning environment for unmanned aerial vehicles

SIMULATION ◽  
2012 ◽  
Vol 89 (8) ◽  
pp. 903-920 ◽  
Author(s):  
Isil Oz ◽  
Haluk Rahmi Topcuoglu ◽  
Murat Ermis
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Three Dimensional ◽  
Aerial Vehicles

scholarly journals A Design Methodology for Energy-Aware Processing in Unmanned Aerial Vehicles

2022 ◽  
Vol 27 (1) ◽  
pp. 1-20
Author(s):  
Jingyu He ◽  
Yao Xiao ◽  
Corina Bogdan ◽  
Shahin Nazarian ◽  
Paul Bogdan
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Homeland Security ◽  
Design Methodology ◽  
Energy Savings ◽  
Visual Navigation ◽  
Parallel Execution ◽  
Disaster Mitigation ◽  
Energy Aware ◽  
Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) have rapidly become popular for monitoring, delivery, and actuation in many application domains such as environmental management, disaster mitigation, homeland security, energy, transportation, and manufacturing. However, the UAV perception and navigation intelligence (PNI) designs are still in their infancy and demand fundamental performance and energy optimizations to be eligible for mass adoption. In this article, we present a generalizable three-stage optimization framework for PNI systems that (i) abstracts the high-level programs representing the perception, mining, processing, and decision making of UAVs into complex weighted networks tracking the interdependencies between universal low-level intermediate representations; (ii) exploits a differential geometry approach to schedule and map the discovered PNI tasks onto an underlying manycore architecture. To mine the complexity of optimal parallelization of perception and decision modules in UAVs, this proposed design methodology relies on an Ollivier-Ricci curvature-based load-balancing strategy that detects the parallel communities of the PNI applications for maximum parallel execution, while minimizing the inter-core communication; and (iii) relies on an energy-aware mapping scheme to minimize the energy dissipation when assigning the communities onto tile-based networks-on-chip. We validate this approach based on various drone PNI designs including flight controller, path planning, and visual navigation. The experimental results confirm that the proposed framework achieves 23% flight time reduction and up to 34% energy savings for the flight controller application. In addition, the optimization on a 16-core platform improves the on-time visit rate of the path planning algorithm by 14% while reducing 81% of run time for ConvNet visual navigation.

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