Efficient Trajectory Planning for WSN Data Collection with Multiple UAVs

Alejo, David; Cobano, J. A.; Heredia, Guillermo; Dios, J.R. Martínez-de; Ollero, A.

doi:10.1007/978-3-319-18299-5_3

Cited by 15 publications

(12 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They aim to find the optimal ground terminal transmit power and UAV trajectory that achieve different Pareto optimal energy trade-offs between the ground terminal and the UAV. The authors in [238] study the problem of trajectory planning for wireless sensor network data collecting deployed in remote areas with a cooperative system of UAVs. The missions are given by a set of ground points which define wireless sensor network gathering zones and each UAV should pass through them to gather the data while avoiding passing over forbidden areas and collisions between UAVs.…”

Section: ) Deployment Strategies To Collect Data Using Uavsmentioning

confidence: 99%

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

et al. 2019

View full text Add to dashboard Cite

The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains including realtime monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate the more that $45 Billion market value of UAV usage. In this survey, we present UAV civil applications and their challenges. We also discuss current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including: charging challenges, collision avoidance and swarming challenges, and networking and security related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.

show abstract

Section: ) Deployment Strategies To Collect Data Using Uavsmentioning

confidence: 99%

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Over the past decade, mobile robots have been effectively adapted to carry out vital unmanned tasks in various fields. Application areas of path-planning algorithms include but are not confined to security, vigilance [1], planetary exploration [2], route planning of Unmanned Aerial Vehicle (UAV) [3,4], and molecular simulation [5]. Path-planning for mobile robots deals with feasible path generation from a starting position to a goal position by avoiding collision with obstacles in an environment [6].…”

Section: Introductionmentioning

confidence: 99%

A Path-Planning Performance Comparison of RRT-AB with MEA in a 2-Dimensional Environment

et al. 2019

View full text Add to dashboard Cite

With the advent of mobile robots in commercial applications, the problem of path-planning has acquired significant attention from the research community. An optimal path for a mobile robot is measured by various factors such as path length, collision-free space, execution time, and the total number of turns. MEA* is an efficient variation of A* for optimal path-planning of mobile robots. RRT*-AB is a sampling-based planner with rapid convergence rate, and improved time and space requirements than other sampling-based methods such as RRT*. The purpose of this paper is the review and performance comparison of these planners based on metrics, i.e., path length, execution time, and memory requirements. All planners are tested in structured and complex unstructured environments cluttered with obstacles. Performance plots and statistical analysis have shown that MEA* requires less memory and computational time than other planners. These advantages of MEA* make it suitable for off-line applications using small robots with constrained power and memory resources. Moreover, performance plots of path length of MEA* is comparable to RRT*-AB with less execution time in the 2D environment. However, RRT*-AB will outperform MEA* in high-dimensional problems because of its inherited suitability for complex problems.

show abstract

“…Alejo et al [3] presented RRT*i for efficient motion planning of UAVs. They compared the proposed approach with genetic algorithm, RRT and RRT*.…”

Section: Non-holonomic and Kinodynamic Rrt* Approachesmentioning

confidence: 99%

“…Path planning algorithms are of vital importance for motion planning of mobile robots due to their numerous applications in autonomous cars [2], Unmanned Aerial Vehicles (UAVs) [3], forklifts [4], surveillance operations [5], medical [6], planetary and space missions [1,7]. Initial complete practical planners such as Road Map (RM), Potential Fields, and Cell Decomposition (CD) techniques are unable to deal with dynamic and complex high dimension problems [1,[7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Path Planning using RRT* based Approaches: A Survey and Future Directions

Noreen¹,

Khan²,

Habib³

2016

ijacsa

199

View full text Add to dashboard Cite

Abstract-Optimal path planning refers to find the collision free, shortest, and smooth route between start and goal positions. This task is essential in many robotic applications such as autonomous car, surveillance operations, agricultural robots, planetary and space exploration missions. Rapidly-exploring Random Tree Star (RRT*) is a renowned sampling based planning approach. It has gained immense popularity due to its support for high dimensional complex problems. A significant body of research has addressed the problem of optimal path planning for mobile robots using RRT* based approaches. However, no updated survey on RRT* based approaches is available. Considering the rapid pace of development in this field, this paper presents a comprehensive review of RRT* based path planning approaches. Current issues relevant to noticeable advancements in the field are investigated and whole discussion is concluded with challenges and future research directions.

show abstract

Efficient Trajectory Planning for WSN Data Collection with Multiple UAVs

Cited by 15 publications

References 34 publications

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

A Path-Planning Performance Comparison of RRT-AB with MEA in a 2-Dimensional Environment

Optimal Path Planning using RRT* based Approaches: A Survey and Future Directions

Contact Info

Product

Resources

About

Efficient Trajectory Planning for WSN Data Collection with Multiple UAVs

Cited by 15 publications

References 34 publications

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

A Path-Planning Performance Comparison of RRT*-AB with MEA* in a 2-Dimensional Environment

Optimal Path Planning using RRT* based Approaches: A Survey and Future Directions

Contact Info

Product

Resources

About

A Path-Planning Performance Comparison of RRT-AB with MEA in a 2-Dimensional Environment