A Path Planning Algorithm for Collective Monitoring Using Autonomous Drones

Islam, Shafkat; Razi, Abolfazl

doi:10.1109/ciss.2019.8693023

Cited by 25 publications

(6 citation statements)

References 24 publications

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“…There is a trend in the literature to apply multiple light robots instead of developing drones with the capabilities of planes and helicopters. For instance, a drone fleet is proposed in [48] and a drone swarm in [49]. When multiple drones work in the same scenario, the coordination of the fleet becomes relevant.…”

Section: Extinguishingmentioning

confidence: 99%

A Survey on Robotic Technologies for Forest Firefighting: Applying Drone Swarms to Improve Firefighters’ Efficiency and Safety

2021

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Forest firefighting missions encompass multiple tasks related to prevention, surveillance, and extinguishing. This work presents a complete survey of firefighters on the current problems in their work and the potential technological solutions. Additionally, it reviews the efforts performed by the academy and industry to apply different types of robots in the context of firefighting missions. Finally, all this information is used to propose a concept of operation for the comprehensive application of drone swarms in firefighting. The proposed system is a fleet of quadcopters that individually are only able to visit waypoints and use payloads, but collectively can perform tasks of surveillance, mapping, monitoring, etc. Three operator roles are defined, each one with different access to information and functions in the mission: mission commander, team leaders, and team members. These operators take advantage of virtual and augmented reality interfaces to intuitively get the information of the scenario and, in the case of the mission commander, control the drone swarm.

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Section: Extinguishingmentioning

confidence: 99%

A Survey on Robotic Technologies for Forest Firefighting: Applying Drone Swarms to Improve Firefighters’ Efficiency and Safety

2021

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“…In [30], the authors proposed a ML pipeline for autonomous mobile terminals that first extracts spatial features through a Convolutional Neural Network (CNN), and utilizes Multi-Agent Deep Deterministic Policy (MA-DDPG) to learn to autonomously collect specified data in a region of interest. In [31], the study presented a missionoriented path planning algorithm based on Q-learning for UAVs to autonomously navigate between tasks in a specified mission area whilst avoiding obstacles. Most of the developed models are limited to simplified 2D navigation space (i.e., fixed altitude), where the UAV is not able to change its altitude to cross over obstacles.…”

Section: A Literature Reviewmentioning

confidence: 99%

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

et al. 2020

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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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“…Under urban environments, in order to minimize the risk to the population, the authors proposed a risk-aware trajectory planning algorithm for multi-UAV [28]. Islam et al proposed a task-oriented trajectory planning scheme for multi-UAV [29]. The UAVs taken autonomous decisions to find their trajectories for flying to the mission area while avoiding collision to barriers.…”

Section: Related Workmentioning

confidence: 99%

Trajectory Planning for Data Collection of Energy-Constrained Heterogeneous UAVs

Qin

Dong

Wang

et al. 2019

Sensors

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Nowadays, Unmanned Aerial Vehicles (UAVs) have received growing popularity in the Internet-of-Things (IoT) which often deploys many sensors in a relatively wide region. Since the battery capacity is limited, sensors cannot transmit over a long distance. It is necessary for designing efficient sensor data collection mechanisms to prolong the lifetime of the IoT and enhance data collection efficiency. In this paper, we consider a UAV-enabled data collection scenario, where multiple heterogeneous UAVs with different energy constraints are employed to collect data from sensors. The value of data depends on the importance of the monitoring area of the sensor and the freshness of collected data. Our objective is to maximize the data collection utility by jointly optimizing the communication scheduling and trajectory of each UAV. The data collection utility is determined by the amount and value of the collected data. This problem is a variant of multiple knapsack problem, which is a classical NP-hard problem. First, we transform the initial problem into a submodular function maximization problem under energy constraints, and then we design a novel trajectory planning algorithm to maximize the data collection utility, while accounting for different values of data and different energy constraints of heterogeneous UAVs. Finally, under different network settings, the performance of the proposed trajectory planning algorithm is evaluated via extensive simulations. The results show that the proposed algorithm can obtain maximum data collection utility.

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A Path Planning Algorithm for Collective Monitoring Using Autonomous Drones

Cited by 25 publications

References 24 publications

A Survey on Robotic Technologies for Forest Firefighting: Applying Drone Swarms to Improve Firefighters’ Efficiency and Safety

A Survey on Robotic Technologies for Forest Firefighting: Applying Drone Swarms to Improve Firefighters’ Efficiency and Safety

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

Trajectory Planning for Data Collection of Energy-Constrained Heterogeneous UAVs

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