Sensor Planning for a Symbiotic UAV and UGV System for Precision Agriculture

Tokekar, Pratap; Hook, Joshua Vander; Mulla, D. J.; Isler, Volkan

doi:10.1109/tro.2016.2603528

Cited by 416 publications

(180 citation statements)

References 36 publications

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“…One recent approach to this is Rapidlyexploring Random Cycles (RRCs) [182], which is better suited to surveillance and monitoring tasks where areas must be consistently revisited than its cousin, Rapidly-exploring Random Trees (RRTs) [82], which focus on single-use trajectories. Monitoring spatio-temporal fields is a challenging task and may often be better accomplished by using a heterogeneous team [183].…”

Section: Robot Capabilities Rmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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Abstract-The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional (3-D) space, and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of the paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

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Section: Robot Capabilities Rmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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“…Table 1 reveals an increasing interest in UAVs in the field of agriculture in recent years, and most agricultural UAVs currently in use are single-UAV systems except for [19,21]. The main research areas are remote sensing [7,8,10,13,17,18,20,22], mapping [7,8,11,15], and monitoring [9,12,14,19,26], and it is not yet used in various areas such as sowing and harvesting. Furthermore, the research for irrigation and pest control is on the rise nowadays [16,27,28].…”

Section: Review About the Application Of Uav In Agriculturementioning

confidence: 99%

“…In sensors, RGB cameras [13][14][15]19], thermal cameras [7,8], and multi-spectral cameras [7][8][9][10][11][12]17,18,20,22] are used. Recent studies focused on agricultural UAVs through image processing, including preprocessing, onboard-processing and post-processing; thus, it is widely used in camera sensors.…”

Section: Review About the Application Of Uav In Agriculturementioning

confidence: 99%

“…However, when analyzing the existing application of UAV system for agriculture (see, for instance, [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]), most studies execute agricultural tasks using a single UAV with an autonomous control. There are few studies on the use of the multi-UAV system in performing agricultural works; thus, it is only at the advanced stage of research [19,21,23].…”

Section: Introductionmentioning

confidence: 99%

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Multiple UAV Systems for Agricultural Applications: Control, Implementation, and Evaluation

Son

2018

Electronics

136

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Abstract:The introduction of multiple unmanned aerial vehicle (UAV) systems into agriculture causes an increase in work efficiency and a decrease in operator fatigue. However, systems that are commonly used in agriculture perform tasks using a single UAV with a centralized controller. In this study, we develop a multi-UAV system for agriculture using the distributed swarm control algorithm and evaluate the performance of the system. The performance of the proposed agricultural multi-UAV system is quantitatively evaluated and analyzed through four experimental cases: single UAV with autonomous control, multiple UAVs with autonomous control, single UAV with remote control, and multiple UAVs with remote control. Moreover, the performance of each system was analyzed through seven performance metrics: total time, setup time, flight time, battery consumption, inaccuracy of land, haptic control effort, and coverage ratio. Experimental results indicate that the performance of the multi-UAV system is significantly superior to the single-UAV system.

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“…However, such forward propagation is not viable given the proposed perception system, and so we pursue an information-based planning approach that seeks to generate optimal trajectories with respect to a spatially-varying field representing landing site confidence that is updated online via the perception system [16]. Similar to prior work, we choose a Gaussian process representation to guide trajectory planning toward minimizing the variance in the field estimate [11,14,26]. However, our approach differs in that we consider the impact of image space observations on the field estimate, rather than treating the entire image as a single point-observation of the field.…”

Section: Introductionmentioning

confidence: 99%

Vision-based Landing Site Evaluation and Trajectory Generation Toward Rooftop Landing

Desaraju

Michael²,

Humenberger³

et al. 2014

Robotics: Science and Systems X

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Abstract-Autonomous landing is an essential function for micro air vehicles (MAVs) for many scenarios. We pursue an active perception strategy that enables MAVs with limited onboard sensing and processing capabilities to concurrently assess feasible rooftop landing sites with a vision-based perception system while generating trajectories that balance continued landing site assessment and the requirement to provide visual monitoring of an interest point. The contributions of the work are twofold: (1) a perception system that employs a dense motion stereo approach that determines the 3D model of the captured scene without the need of geo-referenced images, scene geometry constraints, or external navigation aids; and (2) an online trajectory generation approach that balances the need to concurrently explore available rooftop vantages of an interest point while ensuring confidence in the landing site suitability by considering the impact of landing site uncertainty as assessed by the perception system. Simulation and experimental evaluation of the performance of the perception and trajectory generation methodologies are analyzed independently and jointly in order to establish the efficacy of the proposed approach.

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Sensor Planning for a Symbiotic UAV and UGV System for Precision Agriculture

Cited by 416 publications

References 36 publications

A Survey on Aerial Swarm Robotics

A Survey on Aerial Swarm Robotics

Multiple UAV Systems for Agricultural Applications: Control, Implementation, and Evaluation

Vision-based Landing Site Evaluation and Trajectory Generation Toward Rooftop Landing

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