664 research outputs found
Improved GWO Algorithm for UAV Path Planning on Crop Pest Monitoring
Agricultural information monitoring is the monitoring of the agricultural production process, and its task is to monitor the growth process of major crops systematically. When assessing the pest situation of crops in this process, the traditional satellite monitoring method has the defects of poor real-time and high operating cost, whereas the pest monitoring through Unmanned Aerial Vehicles (UAVs) effectively solves the above problems, so this method is widely used. An important key issue involved in monitoring technology is path planning. In this paper, we proposed an Improved Grey Wolf Optimization algorithm, IGWO, to realize the flight path planning of UAV in crop pest monitoring. A map environment model is simulated, and information traversal is performed, then the search of feasible paths for UAV flight is carried out by the Grey Wolf Optimization algorithm (GWO). However, the algorithm search process has the defect of falling into local optimum which leading to path planning failure. To avoid such a situation, we introduced the probabilistic leap mechanism of the Simulated Annealing algorithm (SA). Besides, the convergence factor is modified with an exponential decay mode for improving the convergence rate of the algorithm. Compared with the GWO algorithm, IGWO has the 8.3%, 16.7%, 28.6% and 39.6% lower total cost of path distance on map models with precision of 15, 20, 25 and 30 respectively, and also has better path planning results in contrast to other swarm intelligence algorithms
A Novel Airborne Self-organising Architecture for 5G+ Networks
Network Flying Platforms (NFPs) such as unmanned aerial vehicles, unmanned
balloons or drones flying at low/medium/high altitude can be employed to
enhance network coverage and capacity by deploying a swarm of flying platforms
that implement novel radio resource management techniques. In this paper, we
propose a novel layered architecture where NFPs, of various types and flying at
low/medium/high layers in a swarm of flying platforms, are considered as an
integrated part of the future cellular networks to inject additional capacity
and expand the coverage for exceptional scenarios (sports events, concerts,
etc.) and hard-to-reach areas (rural or sparsely populated areas). Successful
roll-out of the proposed architecture depends on several factors including, but
are not limited to: network optimisation for NFP placement and association,
safety operations of NFP for network/equipment security, and reliability for
NFP transport and control/signaling mechanisms. In this work, we formulate the
optimum placement of NFP at a Lower Layer (LL) by exploiting the airborne
Self-organising Network (SON) features. Our initial simulations show the NFP-LL
can serve more User Equipment (UE)s using this placement technique.Comment: 5 pages, 2 figures, conference paper in IEEE VTC-Fall 2017, in
Proceedings IEEE Vehicular Technology Conference (VTC-Fall 2017), Toronto,
Canada, Sep. 201
PSO and Kalman Filter-Based Node Motion Prediction for Data Collection from Ocean Wireless Sensors Network with UAV
Source at https://ctsoc.ieee.org/In this paper, we consider a wireless sensor network
of nodes at the sea surface drifting due to wind and sea currents.
In our scenario an Unmanned Aerial Vehicle (UAV) will be used
to gather data from the sensor nodes. The goal is to find a
flyable path which is optimal in terms of sensor node energy
consumption, total channel throughput between the UAV and
sensor nodes, flight time for the UAV and frequency of the
node visits by the UAV. Finally, the path should also be optimal
concerning node position estimation uncertainty. A Kalman Filter
(KF) is used to estimate the nodes motions and Particle Swarm
Optimization (PSO) is the method used to calculate the UAV
path taking all of these objectives into account. The proposed
node tracking aware path planning solution is compared to two
other scenarios: One where the path planning is based on full
knowledge of the node positions at all times, and one where path
planning is based on the last known positions of the nodes
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Motion Planning of UAV Swarm: Recent Challenges and Approaches
The unmanned aerial vehicle (UAV) swarm is gaining massive interest for researchers as it has huge significance over a single UAV. Many studies focus only on a few challenges of this complex multidisciplinary group. Most of them have certain limitations. This paper aims to recognize and arrange relevant research for evaluating motion planning techniques and models for a swarm from the viewpoint of control, path planning, architecture, communication, monitoring and tracking, and safety issues. Then, a state-of-the-art understanding of the UAV swarm and an overview of swarm intelligence (SI) are provided in this research. Multiple challenges are considered, and some approaches are presented. Findings show that swarm intelligence is leading in this era and is the most significant approach for UAV swarm that offers distinct contributions in different environments. This integration of studies will serve as a basis for knowledge concerning swarm, create guidelines for motion planning issues, and strengthens support for existing methods. Moreover, this paper possesses the capacity to engender new strategies that can serve as the grounds for future work
Artificial Intelligence Applications for Drones Navigation in GPS-denied or degraded Environments
L'abstract è presente nell'allegato / the abstract is in the attachmen
UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms
Recent technological advancements in space, air and ground components have
made possible a new network paradigm called "space-air-ground integrated
network" (SAGIN). Unmanned aerial vehicles (UAVs) play a key role in SAGINs.
However, due to UAVs' high dynamics and complexity, the real-world deployment
of a SAGIN becomes a major barrier for realizing such SAGINs. Compared to the
space and terrestrial components, UAVs are expected to meet performance
requirements with high flexibility and dynamics using limited resources.
Therefore, employing UAVs in various usage scenarios requires well-designed
planning in algorithmic approaches. In this paper, we provide a comprehensive
review of recent learning-based algorithmic approaches. We consider possible
reward functions and discuss the state-of-the-art algorithms for optimizing the
reward functions, including Q-learning, deep Q-learning, multi-armed bandit
(MAB), particle swarm optimization (PSO) and satisfaction-based learning
algorithms. Unlike other survey papers, we focus on the methodological
perspective of the optimization problem, which can be applicable to various
UAV-assisted missions on a SAGIN using these algorithms. We simulate users and
environments according to real-world scenarios and compare the learning-based
and PSO-based methods in terms of throughput, load, fairness, computation time,
etc. We also implement and evaluate the 2-dimensional (2D) and 3-dimensional
(3D) variations of these algorithms to reflect different deployment cases. Our
simulation suggests that the D satisfaction-based learning algorithm
outperforms the other approaches for various metrics in most cases. We discuss
some open challenges at the end and our findings aim to provide design
guidelines for algorithm selections while optimizing the deployment of
UAV-assisted SAGINs.Comment: Submitted to the IEEE Internet of Things Journal in June 202
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