UAV Flight Instructional Design for Industry 4.0 based on the Framework of Educational Mechatronics

Luque-Vega, Luis F.; López-Neri, Emmanuel; Arellano-Muro, Carlos A.; Gonzalez-Jimenez, Luis Enrique; Ghommam, Jawhar; Carrasco-Navarro, Rocío

doi:10.1109/iecon43393.2020.9255295

Cited by 13 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The olive trees are the static models, while pickup can move in the Z f ree plane changing acceleration P a and velocity P v by every agent's iteration with the environment. Equation (5) shows how these two vectors are described, using (Θ, Φ) as a random vector to describe the orientation and (X 1 , Y 1 ) (X 2 , Y 2 ) to determine the module.…”

Section: Algorithm 1 Rapidly Exploring Random Treementioning

confidence: 99%

“…As stated in Almaki et al [2], the fourth industrial revolution (4IR) is bringing new opportunities to employ advanced technology to achieve an efficient and sustainable means of boosting crop productivity. For instance, unmanned aerial vehicles (UAVs) open new paths that advance the solution of existing problems, such as for package delivery in logistics [3], in the field of agriculture 4.0 [4], Industry 4.0 [5], artificial intelligence [6], the Internet of Things (IoT) [7], remote sensing [8] and many others. In precision agriculture, UAVs are widely used to map the spatial and temporal variability of various parameters in the cultivation environment, helping producers in decision-making and consequently improving agricultural management [9,10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs

et al. 2023

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAV) are a suitable solution for monitoring growing cultures due to the possibility of covering a large area and the necessity of periodic monitoring. In inspection and monitoring tasks, the UAV must find an optimal or near-optimal collision-free route given initial and target positions. In this sense, path-planning strategies are crucial, especially online path planning that can represent the robot’s operational environment or for control purposes. Therefore, this paper proposes an online adaptive path-planning solution based on the fusion of rapidly exploring random trees (RRT) and deep reinforcement learning (DRL) algorithms applied to the generation and control of the UAV autonomous trajectory during an olive-growing fly traps inspection task. The main objective of this proposal is to provide a reliable route for the UAV to reach the inspection points in the tree space to capture an image of the trap autonomously, avoiding possible obstacles present in the environment. The proposed framework was tested in a simulated environment using Gazebo and ROS. The results showed that the proposed solution accomplished the trial for environments up to 300 m3 and with 10 dynamic objects.

show abstract

Section: Algorithm 1 Rapidly Exploring Random Treementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs

et al. 2023

View full text Add to dashboard Cite

show abstract

“…For more reference on formal language when dealing with drones and its mathematical model, see [ 20 ]. Moreover, a drone flight instructional design can be found in [ 21 ].…”

Section: Application Of the Medara To The Drone Flight Case Studymentioning

confidence: 99%

Methodology for the Development of Augmented Reality Applications: MeDARA. Drone Flight Case Study

Zamora-Antuñano

Luque-Vega²,

Carlos-Mancilla³

et al. 2022

Sensors

View full text Add to dashboard Cite

Industry 4.0 involves various areas of engineering such as advanced robotics, Internet of Things, simulation, and augmented reality, which are focused on the development of smart factories. The present work presents the design and application of the methodology for the development of augmented reality applications (MeDARA) using a concrete, pictorial, and abstract approach with the intention of promoting the knowledge, skills, and attitudes of the students within the conceptual framework of educational mechatronics (EMCF). The flight of a drone is presented as a case study, where the concrete level involves the manipulation of the drone in a simulation; the graphic level requires the elaboration of an experiential storyboard that shows the scenes of the student’s interaction with the drone in the concrete level; and finally, the abstract level involves the planning of user stories and acceptance criteria, the computer design of the drone, the mock-ups of the application, the coding in Unity and Android Studio, and its integration to perform unit and acceptance tests. Finally, evidence of the tests is shown to demonstrate the results of the application of the MeDARA.

show abstract

“…UAVs are a promising solution applicable across various domains, including search and rescue [7,8], inspection [9], Industry 4.0 [10], and remote sensing [11], among other fields. Over the years, these robots have proven to be valuable tools for exploring complex and dynamic environments [12,13].…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Multi-Robot Collaboration for Coverage Path Planning in Partially Known Dynamic Environments

de Castro,

Santos,

Andrade

et al. 2024

Machines

View full text Add to dashboard Cite

This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots.

show abstract

UAV Flight Instructional Design for Industry 4.0 based on the Framework of Educational Mechatronics

Cited by 13 publications

References 12 publications

Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs

Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs

Methodology for the Development of Augmented Reality Applications: MeDARA. Drone Flight Case Study

Heterogeneous Multi-Robot Collaboration for Coverage Path Planning in Partially Known Dynamic Environments

Contact Info

Product

Resources

About