Multi-vehicle path planning for non-line of sight communication

Schouwenaars, Tom; Féron, Éric; How, Jonathan P.

doi:10.1109/acc.2006.1657643

Cited by 38 publications

(24 citation statements)

References 4 publications

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“…Specifically, system trajectories are optimized in terms of control effort, that is, we design a control law that minimizes the forces and moments needed to operate a UAV formation, while meeting all the mission objectives. Minimizing the control effort is equivalent to minimizing the formation's fuel consumption in the case of vehicles equipped with conventional fuel-based propulsion systems (Schouwenaars et al, 2006) and is a suitable indicator of the energy consumption for vehicles powered by batteries or other power sources.…”

Section: Introductionmentioning

confidence: 99%

A Variational Approach to the Fuel Optimal Control Problem for UAV Formations

Haddad¹

2012

Recent Advances in Aircraft Technology

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Section: Introductionmentioning

confidence: 99%

A Variational Approach to the Fuel Optimal Control Problem for UAV Formations

Haddad¹

2012

Recent Advances in Aircraft Technology

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“…al developed a generic multivehicle path planning algorithm formulated as a mixed integer linear program [25]. Later, this algorithm was improved to coordinate the control of multiple helicopters that cooperate in order to insure a continuous line of communication to some ground station [24]. Bellingham et.…”

Section: Introductionmentioning

confidence: 99%

A cooperative control algorithm for camera based observational systems.

Young¹

2012

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Over the last several years, there has been considerable growth in camera based observation systems for a variety of safety, scientific, and recreational applications. In order to improve the effectiveness of these systems, we frequently desire the ability to increase the number of observed objects, but solving this problem is not as simple as adding more cameras. Quite often, there are economic or physical restrictions that prevent us from adding additional cameras to the system. As a result, we require methods that coordinate the tracking of objects between multiple cameras in an optimal way.In order to accomplish this goal, we present a new cooperative control algorithm for a camera based observational system. Specifically, we present a receding horizon control where we model the underlying optimal control problem as a mixed integer linear program. The benefit of this design is that we can coordinate the actions between each camera while simultaneously respecting its kinematics. In addition, we further improve the quality of our solution by coupling our algorithm with a Kalman filter. Through this integration, we not only add a predictive component to our control, but we use the uncertainty estimates provided by the filter to encourage the system to periodically observe any outliers in the observed area. This combined approach allows us to intelligently observe the entire region of interest in an effective and thorough manner.3 4

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“…The great majority of these papers have focused on the sensor range constraint and often deal with obstaclefree environments. However, [14] and [15] consider communication restrictions involving both limited sensor range and line-of-sight constraints in the presence of obstacles. In the following, both these papers will be discussed in detail.…”

Section: Related Workmentioning

confidence: 99%

“…The problem formulation in [14] considers optimal path planning for a number of relay vehicles that have the mission of maintaining a chain of line-of-sight communication links that connect a given leader vehicle to the ground station. In accordance with their previous work in multi-vehicle path planning, Schouwenaars et al use binary variables to capture connectivity between subsequent relay vehicles and end up solving a mixed integer linear program (MILP).…”

Section: Related Workmentioning

confidence: 99%

Cooperative surveillance missions with multiple unmanned ground vehicles (UGVs)

Anisi¹,

Ögren

Hu³

et al. 2008

2008 47th IEEE Conference on Decision and Control

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Abstract-This paper proposes an optimization based approach to multi-UGV surveillance. In particular, we formulate both the minimum time-and connectivity constrained surveillance problems, show N P-hardness of them and propose decomposition techniques that allow us to solve them efficiently in an algorithmic manner.The minimum time formulation is the following. Given a set of surveillance UGVs and a polyhedral area, find waypointpaths for all UGVs such that every point of the area is visible from a point on a path and such that the time for executing the search in parallel is minimized. Here, the sensor's field of view are assumed to be occluded by the obstacles and limited by a maximal sensor range. The connectivity constrained formulation extends the first by additionally requiring that the information graph induced by the sensors is connected at the time instants when the UGVs stop to perform the surveillance task. The second formulation is relevant to situation when mutual visibility is needed either to transmit the sensor data being gathered, or to protect the team from hostile persons trying to approach the stationary UGVs.

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Multi-vehicle path planning for non-line of sight communication

Cited by 38 publications

References 4 publications

A Variational Approach to the Fuel Optimal Control Problem for UAV Formations

A Variational Approach to the Fuel Optimal Control Problem for UAV Formations

A cooperative control algorithm for camera based observational systems.

Cooperative surveillance missions with multiple unmanned ground vehicles (UGVs)

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