Intelligent UAV Map Generation and Discrete Path Planning for Search and Rescue Operations

Juan, Víctor San; Santos, Matilde; Andújar, José Manuel

doi:10.1155/2018/6879419

Cited by 80 publications

(55 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Swarm technology presents an architecture that is scalable, efficient, and robust and helps to mitigate certain aspects of risk associated with UAV deployment (Howden, ; Zhao, Zhao, Su, Ma, & Zhang, ). UAV swarms can either be controlled using group decision making or individual agent response (Howden, ), with coverage being either “distributed” into defined zones of operation or “free” for optimum coverage through parallel decision making (San Juan, Santos, & Andujar, ). Applications for swarm mapping have included surveillance missions, search and rescue operations, weed mapping, and oil spill mapping (Albani, Nardi, & Trianni, ; Howden, ; Nigam, Bieniawski, Kroo, & Vian, ; Odonkor, Ball, & Chowdhury, ; Pitre, Li, & Delbalzo, ; San Juan et al, ).…”

Section: Future Directionsmentioning

confidence: 99%

“…UAV swarms can either be controlled using group decision making or individual agent response (Howden, ), with coverage being either “distributed” into defined zones of operation or “free” for optimum coverage through parallel decision making (San Juan, Santos, & Andujar, ). Applications for swarm mapping have included surveillance missions, search and rescue operations, weed mapping, and oil spill mapping (Albani, Nardi, & Trianni, ; Howden, ; Nigam, Bieniawski, Kroo, & Vian, ; Odonkor, Ball, & Chowdhury, ; Pitre, Li, & Delbalzo, ; San Juan et al, ). However, studies remain focussed on using simulations to test either algorithms (Almeida, Hildmann, & Solmaz, ; Chen, Ye, & Li, ; Yang, Ji, Yang, Li, & Li, ; Zhao et al, ) or data processing techniques (Casbeer, Kingston, Beard, & McLain, ; Ruiz, Caballero, & Merino, ).…”

Section: Future Directionsmentioning

confidence: 99%

See 1 more Smart Citation

Remote sensing of river corridors: A review of current trends and future directions

Tomsett

Leyland

2019

River Research & Apps

109

View full text Add to dashboard Cite

River corridors play a crucial environmental, economic, and societal role yet also represent one of the world's most dangerous natural hazards, making monitoring imperative to improve our understanding and to protect people. Remote sensing offers a rapidly growing suite of methods by which river corridor monitoring can be performed efficiently, at a range of scales and in difficult environmental conditions. This paper aims to evaluate the current state and assess the potential future of river corridor monitoring, whilst highlighting areas that require further investigation. We initially review established methods that are used to undertake river corridor monitoring, framed by the context and scales upon which they are applied. Subsequently, we review cutting edge technologies that are being developed and focussed around unmanned aerial vehicle and multisensor system advances. We also "horizon scan" for future methods that may become increasingly prominent in research and management, citing examples from within and outside of the fluvial domain. Through review of the literature, it has become apparent that the main gap in fluvial remote sensing lies in the trade-off between resolution and scales. However, prioritising process measurements and simultaneous multisensor data collection is likely to offer a bigger advance in understanding than purely from better surveying methods alone. Challenges regarding the legal deployment of more complex systems, as well as effectively disseminating data into the science community, are amongst those that we propose need addressing. However, the plethora of methods currently available means that researchers and monitoring agencies will be able to identify suitable techniques for their needs.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Section: Future Directionsmentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

Remote sensing of river corridors: A review of current trends and future directions

Tomsett

Leyland

2019

River Research & Apps

109

View full text Add to dashboard Cite

show abstract

“…Unmanned aerial vehicles (UAVs) are widely used in both civilian and military fields, including search and rescue [1], environmental monitoring [2], surveillance and attacks [3], and so on. Based on the multi-agent theory and applications [4][5][6], a group of UAVs flying in a formation [7] rather than a single UAV can obtain higher combating effectiveness, deliver greater coverage, and will have wider applications in the future.…”

Section: Introductionmentioning

confidence: 99%

Path Planning for Multi-UAV Formation Rendezvous Based on Distributed Cooperative Particle Swarm Optimization

et al. 2019

View full text Add to dashboard Cite

This paper studies the problem of generating cooperative feasible paths for formation rendezvous of unmanned aerial vehicles (UAVs). Cooperative path-planning for multi-UAV formation rendezvous is mostly a complicated multi-objective optimization problem with many coupled constraints. In order to satisfy the kinematic constraints, i.e., the maximum curvature constraint and the requirement of continuous curvature of the UAV path, the Pythagorean hodograph (PH) curve is adopted as the parameterized path because of its curvature continuity and rational intrinsic properties. Inspired by the co-evolutionary theory, a distributed cooperative particle swarm optimization (DCPSO) algorithm with an elite keeping strategy is proposed to generate a flyable and safe path for each UAV. This proposed algorithm can meet the kinematic constraints of UAVs and the cooperation requirements among UAVs. Meanwhile, the optimal or sub-optimal paths can be obtained. Finally, numerical simulations in 2-D and 3-D environments are conducted to demonstrate the feasibility and stability of the proposed algorithm. Simulation results show that the paths generated by the proposed DCPSO can not only meet the kinematic constraints of UAVs and safety requirements, but also achieve the simultaneous arrival and collision avoidance between UAVs for formation rendezvous. Compared with the cooperative co-evolutionary genetic algorithm (CCGA), the proposed DCPSO has better stability and a higher searching success rate.

show abstract

“…A centralized mission planner was used for the path-planning, whose objective was to find a sequence of waypoints for each UAV to follow. In [4], an offline algorithm to find a sequence of waypoints to be followed by a team of UAVs in a SAR mission is proposed. The cost function is built to minimize the travelled distance and the risk that people are exposed to.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Unmanned Aerial Vehicles in Search and Rescue Missions Using Real-Time Cooperative Model Predictive Control

Andrade

Hovenburg

Lima

et al. 2019

Sensors

View full text Add to dashboard Cite

Unmanned Aerial Vehicles (UAVs) have recently been used in a wide variety of applications due to their versatility, reduced cost, rapid deployment, among other advantages. Search and Rescue (SAR) is one of the most prominent areas for the employment of UAVs in place of a manned mission, especially because of its limitations on the costs, human resources, and mental and perception of the human operators. In this work, a real-time path-planning solution using multiple cooperative UAVs for SAR missions is proposed. The technique of Particle Swarm Optimization is used to solve a Model Predictive Control (MPC) problem that aims to perform search in a given area of interest, following the directive of international standards of SAR. A coordinated turn kinematic model for level flight in the presence of wind is included in the MPC. The solution is fully implemented to be embedded in the UAV on-board computer with DUNE, an on-board navigation software. The performance is evaluated using Ardupilot’s Software-In-The-Loop with JSBSim flight dynamics model simulations. Results show that, when employing three UAVs, the group reaches 50% Probability of Success 2.35 times faster than when a single UAV is employed.

show abstract

Intelligent UAV Map Generation and Discrete Path Planning for Search and Rescue Operations

Cited by 80 publications

References 26 publications

Remote sensing of river corridors: A review of current trends and future directions

Remote sensing of river corridors: A review of current trends and future directions

Path Planning for Multi-UAV Formation Rendezvous Based on Distributed Cooperative Particle Swarm Optimization

Autonomous Unmanned Aerial Vehicles in Search and Rescue Missions Using Real-Time Cooperative Model Predictive Control

Contact Info

Product

Resources

About