Collision-free Navigation and Efficient Scheduling for Fleet of Multi-Rotor Drones in Smart City

Bahabry, Ahmed; Wan, Xiangpeng; Ghazzai, Hakim; Vesonder, Gregg; Massoud, Yehia

doi:10.1109/mwscas.2019.8885363

Cited by 12 publications

(8 citation statements)

References 6 publications

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“…In this technique was deliberated as A × A; the entire cluster represents R that is demonstrated in Eq. (14).…”

Section: Parabolic Foragingmentioning

confidence: 99%

“…As a result of the massive number of emerging drones in society, UAV efficacy and use need to be enhanced. In [14], a lower complex architecture is designed for determining the time plans shortest and trajectories for all the members of the fleet when considering the various limitations. A collision can be prevented by forcing several drones to statically hover to permit its peers to securely permit the path segments.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Drones Optimal Path Planning Based on Swarm Intelligence Algorithms

Ragab¹,

Altalbe²,

AL-Ghamdi³

et al. 2022

Computers, Materials &Amp; Continua

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The smart city comprises various interlinked elements which communicate data and offers urban life to citizen. Unmanned Aerial Vehicles (UAV) or drones were commonly employed in different application areas like agriculture, logistics, and surveillance. For improving the drone flying safety and quality of services, a significant solution is for designing the Internet of Drones (IoD) where the drones are utilized to gather data and people communicate to the drones of a specific flying region using the mobile devices is for constructing the Internet-of-Drones, where the drones were utilized for collecting the data, and communicate with others. In addition, the SIRSS-CIoD technique derives a tuna swarm algorithm-based clustering (TSA-C) technique to choose cluster heads (CHs) and organize clusters in IoV networks. Besides, the SIRSS-CIoD technique involves the design of a biogeography-based optimization (BBO) technique to an optimum route selection (RS) process. The design of clustering and routing techniques for IoD networks in smart cities shows the novelty of the study. A wide range of experimental analyses is carried out and the comparative study highlighted the improved performance of the SIRSS-CIoD technique over the other approaches.

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“…In this technique was deliberated as A × A; the entire cluster represents R that is demonstrated in Eq. (14).…”

Section: Parabolic Foragingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Drones Optimal Path Planning Based on Swarm Intelligence Algorithms

Ragab¹,

Altalbe²,

AL-Ghamdi³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Many path planning approaches also use optimization methods such as particle swarm optimization [19,41], ant colony optimization [42,43], genetic algorithm [16,18,25,[43][44][45], evolutionary algorithms [22][23][24], and MILP [46][47][48] to find optimal paths. In [43], ant colony and genetic algorithm are used to find a path considering sensing, energy, time, and risk constraints.…”

Section: Related Workmentioning

confidence: 99%

“…Instead of planning the paths of the UAVs in advance, they prioritize safety and low computational overhead, which can be applied in a large-scale harsh outdoor environment. In [46][47][48], they used MILP to find a collision-free path with minimal total time spent by the UAV.…”

Section: Related Workmentioning

confidence: 99%

Reservation-Based 3D Intersection Traffic Control System for Autonomous Unmanned Aerial Vehicles

Rubenecia

Choi

2022

Electronics

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We present a three-dimensional (3D) intersection traffic management platform for small autonomous Unmanned Aerial Vehicles (UAVs), particularly quadcopters, in urban airspace. Assuming many autonomous UAVs are approaching a shared airspace, where UAVs have varying sources and destinations, we propose a system model for a 3D intersection that aims to provide safe and systematic management of UAVs. We also devised a scheduling scheme to ensure that the intersection is efficiently utilized and that there are no collisions among the UAVs in the intersection. The scheduling scheme applies the reservation-based approach, which is sensitive to the sequence of the UAVs in scheduling, thus genetic algorithm is used to determine the best sequence of the UAVs. Simulations were performed to evaluate the efficiency of the system. We also show through the simulations that our scheduling scheme reduces the UAVs’ average time in the system by 27 percent compared with when the UAVs are scheduled in a first-come, first-served manner for the highly crowded intersection.

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“…Hence, navigating within an environment plenty of obstacles remain a challenging task to address. Therefore, there is a need to optimize the navigation and schedule of the UAV when collecting data while considering the different aspects, e.g., battery limitation [12], communication channel, geo-locations of the ground nodes [13], and obstacle avoidance [14].…”

Section: Introductionmentioning

confidence: 99%

A UAV-Assisted Data Collection for Wireless Sensor Networks: Autonomous Navigation and Scheduling

et al. 2020

Self Cite

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Nowadays, Wireless Sensor Networks (WSNs) are playing a vital and sustainable role in many verticals touching different aspects of our lives including civil, public, and military applications. WSNs majorly consist of a few to several sensor nodes, that are connected to each other via wireless communication links and require real-time or delayed data transfer. In this paper, we propose an autonomous Unmanned Aerial Vehicle (UAV)-enabled data gathering mechanism for delay-tolerant WSN applications. The objective is to employ a self-trained UAV as a flying mobile unit collecting data from ground sensor nodes spatially distributed in a given geographical area during a predefined period of time. In this approach, two Reinforcement Learning (RL) approaches, specifically Deep Deterministic Gradient Decent (DDPG) and Q-learning (QL) algorithms, are jointly employed to train the UAV to understand the environment and provide effective scheduling to accomplish its data collection mission. The DDPG is used to autonomously decide the best trajectory to adopt in an obstacle-constrained environment, while the QL is developed to determine the order of nodes to visit such that the data collection time is minimized. The schedule is obtained while considering the limited battery capacity of the flying unit, its need to return the charging station, the time windows of data acquisition, and the priority of certain sensor nodes. Customized reward functions are designed for each RL model and, through numerical simulations, we investigate their training performances. We also analyze the behavior of the autonomous UAV for different selected scenarios and corroborate the ability of the proposed approach in performing effective data collection. A comparison with the deterministic optimal solution is provided to validate the performance of the learning-based approach. INDEX TERMS Internet-of-things, data gathering, reinforcement learning, scheduling, unmanned areal vehicles

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Collision-free Navigation and Efficient Scheduling for Fleet of Multi-Rotor Drones in Smart City

Cited by 12 publications

References 6 publications

A Drones Optimal Path Planning Based on Swarm Intelligence Algorithms

A Drones Optimal Path Planning Based on Swarm Intelligence Algorithms

Reservation-Based 3D Intersection Traffic Control System for Autonomous Unmanned Aerial Vehicles

A UAV-Assisted Data Collection for Wireless Sensor Networks: Autonomous Navigation and Scheduling

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