Hierarchical adaptive planning in environments with uncertain, spatially-varying disturbance forces

Desaraju, Vishnu R.; Michael, Nathan

doi:10.1109/icra.2014.6907618

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…Decentralized Path Planning for Multi-Agent Teams in Complex Environments using Rapidly-exploring Random Trees [7] was published by Vishnu R.Desarajug. It proposes a decentralized rapidly exploring random trees of multi-agent teams (DMA-RRT), and also is a scalable closed-loop RRT (CL-RRT).…”

Section: Fig 1 Route Planning Diagrammentioning

confidence: 99%

The Frontier of Route Planning Algorithm for UAV

Lin

Zhang

2014

AMR

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In recent years, as the UAVs route planning in the war becomes more and more important, many scholars pay much attention to its research both at home and abroad. Route planning of UAV is the key of successfully complete task and achieve the target of protection. In the route planning process, in order to obtain an optimal or suboptimal route, we needs to consider the terrain, threat, body performance and many other factors. In this paper, we talk the research background of route planning and research status at home and abroad. At last, we give a brief summary for the trend of route planning in the future.

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Section: Fig 1 Route Planning Diagrammentioning

confidence: 99%

The Frontier of Route Planning Algorithm for UAV

Lin

Zhang

2014

AMR

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“…Active drifters are an example of robotic systems under significant continuous external forcing. Other single and multi-robot systems, including underwater vehicles [2] and aerial robots [3], [4], are exposed to external forces in real-world applications. In such settings, tasks such as navigation, station keeping, or formation maintenance are extremely challenging.…”

Section: Introductionmentioning

confidence: 99%

Active drifters: Towards a practical multi-robot system for ocean monitoring

Molchanov

Breitenmoser

Sukhatme

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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We propose a method for controlling multiple active drifters in the presence of external forcing induced by the ocean. Our active drifters have one actuator: they can lower and raise their drogues in depth. By exploiting the vertically stratified nature of ocean currents, we show how classical multi-robot tasks (spreading out and aggregation) can be accomplished by the multi-drifter system. Tests with a realistic simulation based on an ocean model suggest that a practical implementation of active drifters which aggregate and disperse in the coastal ocean could be realized through our control method with relatively inexpensive components. Specifically, we are able to show that over a 90 day deployment a significant fraction of drifters can be made to aggregate in few clusters suitable for recovery.

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“…The method represents the uncertainty as Gaussian noise, thus enables collision free by providing maximum dangerous region for each state. A probabilistic description was used to avoid spatially varying disturbance forces as shown by Desaraju and Michael, 26 which generates dynamically feasible paths. Rodriguez et al 27 solve the uncertainty problem using forward prediction and then slightly adjusting the state according to the predicted results.…”

Section: Related Workmentioning

confidence: 99%

Gart

Yang

Xiao

et al. 2016

International Journal of Advanced Robotic Systems

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The problem of three-dimensional path planning in obstacle-crowded environments is a challenge (an NP-hard problem), which becomes even more complex when considering environmental uncertainty and system control. Int this paper, we mainly focused on more challenging problem, that is, path planning in obstacle-crowded environments, and we try to find the relation between contact information and obstacle modeling. We proposed a newactive exploring sampling-based algorithm based on rapidly exploring random tree (RRT), namely, guiding attraction-based random tree (GART). GART introduces bidirectional potential field to redistribute each newly sampled state, such that the in-collision samples can be redistributed for extension. Furthermore, dynamic constraints are deployed to establish forward extending region by GART. Thus, GART can ensure kinodynamic reachability as well as smoothness. Theoretical analysis demonstrate that GART is probabilistic complete, and it obtains faster convergence rate because of its redistribution ability. In addition to theoretical analysis, this article provides comparative simulations as well as experiments under typical situations. Results demonstrate that GART has a much better time-efficiency performance than RRT*, retraction-based RRT, and other referred algorithms when applying redistribution and dynamic constraints on random exploration. KeywordsSampling-based planning algorithm, bidirectional potential field, control and sensing uncertainty, percentage of useful samples Date

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Hierarchical adaptive planning in environments with uncertain, spatially-varying disturbance forces

Cited by 6 publications

References 26 publications

The Frontier of Route Planning Algorithm for UAV

The Frontier of Route Planning Algorithm for UAV

Active drifters: Towards a practical multi-robot system for ocean monitoring

Gart

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