Concept of Indoor 3D-Route UAV Scheduling System

Khosiawan, Yohanes; Nielsen, Izabela; Dung, Ngoc Ang; Yahya, Bernardo Nugroho

doi:10.1007/978-3-319-28555-9_3

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…From the literature, it is evident that the decision criteria in UAV mission planning are many and complex in nature [3,4,14]. Specifically, the decision space comprises aspects related to routing and scheduling [1,25], changing weather conditions (wind speed, wind direction, air density) [26], UAVs' specifications [16,27], energy consumption affected by weather conditions [16], and the payload carried by the UAVs [2] as well as collision avoidance with respect to both moving objects and fixed obstacles [3,25]. Together, these elements emphasize the potential intractability of mission planning as it is highly challenging to develop models considering all these influencing aspects concurrently [3].…”

Section: Literature Reviewmentioning

confidence: 99%

UAV Mission Planning Resistant to Weather Uncertainty

Thibbotuwawa

Bocewicz

Radzki

et al. 2020

Sensors

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Fleet mission planning for Unmanned Aerial Vehicles (UAVs) is the process of creating flight plans for a specific set of objectives and typically over a time period. Due to the increasing focus on the usage of large UAVs, a key challenge is to conduct mission planning addressing changing weather conditions, collision avoidance, and energy constraints specific to these types of UAVs. This paper presents a declarative approach for solving the complex mission planning resistant to weather uncertainty. The approach has been tested on several examples, analyzing how customer satisfaction is influenced by different values of the mission parameters, such as the fleet size, travel distance, wind direction, and wind speed. Computational experiments show the results that allow assessing alternative strategies of UAV mission planning.

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Section: Literature Reviewmentioning

confidence: 99%

UAV Mission Planning Resistant to Weather Uncertainty

Thibbotuwawa

Bocewicz

Radzki

et al. 2020

Sensors

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“…This high level map leaves out the details of physical components so that the number of steps in the scheduling model is admissible for computation. However, low level map consists of paths (edges) and turns (positions) represented as parameterized layers in a 3D trajectory layout (Khosiawan, Nielsen, Do, & Yahya, 2016), depicting all waypoints of a path from one position to another, as shown in Figure 8. Each low level position (turn) is defined as a three-dimensional point.…”

Section: Hierarchy Of Schedule and Mapmentioning

confidence: 99%

A system of UAV application in indoor environment

Khosiawan

Nielsen

2016

Production & Manufacturing Research

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In recent years, there has been an increased demand in the use of multiple unmanned aerial vehicles (UAVs) in indoor environments such as material handling task in a manufacturing environment and plant/ environment monitoring task in a greenhouse. However, there is a lack of work reported on this topic. This paper presents a detailed study on several UAV systems and UAV scheduling systems. It is followed by a proposed system of UAV application in indoor environment, which comprises components of UAV system addressed in detail; focused on scheduler as the heart of operations. Consequently, system architecture of UAV scheduling system is presented and the framework of scheduler component is included. Scheduler component works in a phased manner to provide a systematic abstraction and achieve an efficient computation time. This study serves as a reference guide for UAV application in indoor environment.

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“…A schedule creation mainly aims at assigning tasks to UAVs in a manner that efficiently utilizes the available UAVs. There is a minimal number of reported works on UAV applications in indoor environment [23]. In this paper, tasks and other (on-demand) actions (i.e.…”

Section: Introductionmentioning

confidence: 99%

Task scheduling system for UAV operations in indoor environment

Khosiawan

Park

Moon

et al. 2018

Neural Comput & Applic

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The application of unmanned aerial vehicle (UAV) in indoor environment is emerging nowadays due to the advancements in technology. UAV brings more space flexibility in an occupied or hardly accessible indoor environment, e.g. shop floor of manufacturing industry, greenhouse, and nuclear powerplant. UAV helps in creating an autonomous manufacturing system by executing tasks with less human intervention in a time-efficient manner. Consequently, a scheduler is an essential component to be focused on; yet the number of reported studies on UAV scheduling has been minimal. This work proposes a mathematical model of the problem and a heuristic-based methodology to solve it. To suit near real-time operations, a quick response towards uncertain events and a quick creation of new high-quality feasible schedule are needed. Hence, the proposed heuristic is incorporated with particle swarm optimization algorithm to find a near optimal schedule in a short computation time. This proposed methodology is implemented into a scheduler and tested on a few scales of datasets generated based on real flight demonstrations. Performance evaluation of scheduler is discussed in detail, and the best solution obtained from a selected set of parameters is reported.

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Concept of Indoor 3D-Route UAV Scheduling System

Cited by 7 publications

References 22 publications

UAV Mission Planning Resistant to Weather Uncertainty

UAV Mission Planning Resistant to Weather Uncertainty

A system of UAV application in indoor environment

Task scheduling system for UAV operations in indoor environment

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