UAV cooperative path planning

Chandler, Phillip; Rasmussen, Steven; Afb, Wright-Patterson; Pachter, Meir

doi:10.2514/6.2000-4370

Cited by 180 publications

(93 citation statements)

References 2 publications

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“…The main idea is similar to what was described in Chandler et al, 2000: make the vertices of the line segments smooth by tangential arcs of a circle of radius r min , where r min is the minimum radius that a UAV can execute.…”

Section: Path Refinementmentioning

confidence: 99%

“…The Djikstra algorithm was used in Chandler et al, 2000 to find the shortest path in a Voronoi graph. In this paper, we will use the Bellman-Ford algorithm.…”

Section: Shortest Path Algorithmmentioning

confidence: 99%

“…The approach in this paper is similar to those in Bortoff, 2000 andChandler et al, 2000 in that we decompose the problem into two steps -first the generation of a preliminary polygonal path by using a graph, and then a refinement of the path. Our approach differs in that it is based on a map of the probability of threats, and it does not use a Voronoi graph to find a preliminary path.…”

Section: Introductionmentioning

confidence: 99%

“…The main difference between robot path planning and UAV path planning is that a UAV must maintain its velocity above a minimum velocity, which implies that it cannot follow a path with sharp turns or vertices. There has been research on path planning for UAVs in the presence of risk -see Bortoff, 2000, Chandler et al, 2000, McLain and Beard, 2000, Zabarankin et al, 2001, and the references therein. The first three approaches are similar.…”

Section: Introductionmentioning

confidence: 99%

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Path Planning for Unmanned Aerial Vehicles in Uncertain and Adversarial Environments

Murai

D’Andrea

2003

Cooperative Systems

116

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One of the main objectives when planning paths for unmanned aerial vehicles in adversarial environments is to arrive at the given target, while maximizing the safety of the vehicles. If one has perfect information of the threats that will be encountered, a safe path can always be constructed by solving an optimization problem. If there are uncertainties in the information, however, a different approach must be taken. In this paper we propose a path planning algorithm based on a map of the probability of threats, which can be built from a priori surveillance data. An extension to this algorithm for multiple vehicles is also described, and simulation results are provided.

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Section: Path Refinementmentioning

confidence: 99%

“…The Djikstra algorithm was used in Chandler et al, 2000 to find the shortest path in a Voronoi graph. In this paper, we will use the Bellman-Ford algorithm.…”

Section: Shortest Path Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Path Planning for Unmanned Aerial Vehicles in Uncertain and Adversarial Environments

Murai

D’Andrea

2003

Cooperative Systems

116

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“…In particular, the instantaneous radius of curvature is constrained to be no less than ⇢. This model is often referred to as the Dubins vehicle, in recognition of Dubins' work in computing minimum-length paths for such model (Dubins, 1957), and is typically considered appropriate to model the kinematics of UAVs (Beard et al, 2002;Chandler et al, 2000). The UAVs are assumed to be identical, and have unlimited range.…”

Section: B Uav Routing With Motion Constraints and Unlimited Sensingmentioning

confidence: 99%

UAV Routing and Coordination in Stochastic, Dynamic Environments

Enright¹,

Pavone

Savla

2014

Handbook of Unmanned Aerial Vehicles

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Recent years have witnessed great advancements in the science and technology for unmanned aerial vehicles (UAVs), e.g., in terms of autonomy, sensing, and networking capabilities. This chapter surveys algorithms on task assignment and scheduling for one or multiple UAVs in a dynamic environment, in which targets arrive at random locations at random times, and remain active until one of the UAVs flies to the target's location and performs an on-site task. The objective is to minimize some measure of the targets' activity, e.g., the average amount of time during which a target remains active. The chapter focuses on a technical approach that relies upon methods from queueing theory, combinatorial optimization, and stochastic geometry. The main advantage of this approach is its ability to provide analytical estimates of the performance of the UAV system on a given problem, thus providing insight into how performance is affected by design and environmental parameters, such as the number of UAVs and the target distribution. In addition, the approach provides provable guarantees on the system's performance with respect to an ideal optimum. To illustrate this approach, a variety of scenarios are considered, ranging from the simplest case where one UAV moves along continuous paths and has unlimited sensing capabilities, to the case where the motion of the UAV is subject to curvature constraints, and finally to the case where the UAV has a finite sensor footprint. Finally, the problem of cooperative routing algorithms for multiple UAVs is considered, within the same queueing-theoretical framework, and with a focus on control decentralization.

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LP‐Based Multi‐Vehicle Path Planning with Adversaries

Chasparis

Shamma

2007

Cooperative Control of Distributed Multi‐Agent Systems

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UAV cooperative path planning

Cited by 180 publications

References 2 publications

Path Planning for Unmanned Aerial Vehicles in Uncertain and Adversarial Environments

Path Planning for Unmanned Aerial Vehicles in Uncertain and Adversarial Environments

UAV Routing and Coordination in Stochastic, Dynamic Environments

LP‐Based Multi‐Vehicle Path Planning with Adversaries

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