Optimal Control of Multi-Vehicle-Systems Under Communication Constraints Using Mixed-Integer Linear Programming

Reinl, Christian; Stryk, Oskar von

doi:10.4108/icst.robocomm2007.2211

Cited by 24 publications

(15 citation statements)

References 16 publications

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“…To put the previous constructions into a manageable form we use mixed integer programming Jünger et al [2009], Reinl and von Stryk [2007]. The particularity is that we use the hyperplane arrangement construct to both simplify the formulation Prodan et al [2012] and to exploit the piecewise nature of the shadow regions Stoican et al [2014].…”

Section: Introductionmentioning

confidence: 99%

On corner cutting in multi-obstacle avoidance problems

Stoican¹,

Grøtli

Prodan

et al. 2015

IFAC-PapersOnLine

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Abstract:One challenging and not extensively studied issue in obstacle avoidance is the corner cutting problem. Avoidance constraints are usually imposed at the sampling time without regards to the intra-sample behavior of the dynamics. This paper improves upon state of the art by describing a multi-obstacle environment over a hyperplane arrangement scaffolding, provides a piecewise description of the "shadow" regions and represents them into a combined mixed integer and predictive control formulation. Furthermore, over-approximation constraints which reduce to strictly binary formulations are discussed in detail. Illustrative proofs of concept, comparisons with the state of the art and simulation results over a classical multi-obstacle avoidance problem validate the benefits of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

On corner cutting in multi-obstacle avoidance problems

Stoican¹,

Grøtli

Prodan

et al. 2015

IFAC-PapersOnLine

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“…MILP formulations for real-time mission scheduling of UAVs in hostile environment is treated in [26]. In [27] a optimal control problem of multiple UAVs under terrain-and communication constraints is transformed into a MILP problem. Although the number of binary variables is often a poor indicator of the complexity of integer programming problems [18], it might be useful to reduce this number as the number of nodes in the solution tree grows exponentially with the number of binary variables.…”

Section: Previous Work and Available Technologymentioning

confidence: 99%

“…The approach taken in this section is motivated by the approach of [27]. We will in the following refer to base stations and vehicles involved in the radio communication as nodes.…”

Section: Connectivity Constraintsmentioning

confidence: 99%

Path Planning for UAVs Under Communication Constraints Using SPLAT! and MILP

Grøtli

Johansen

2011

J Intell Robot Syst

102

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We will in this paper address the problem of offline path planning for Unmanned Aerial Vehicles (UAVs). Our goal is to find paths that meet mission objectives, are safe with respect to collision and grounding, fuel efficient and satisfy criteria for communication. Due to the many nonconvex constraints of the problem, Mixed Integer Linear Programming (MILP) will be used in finding the path. Approximate communication constraints and terrain avoidance constraints are used in the MILP formulation. To achieve more accurate prediction of the ability to communicate, the path is then analyzed in the radio propagation toolbox SPLAT!, and if the UAVs are not able to communicate according to design criteria for bandwidth, constraints are modified in the optimization problem in an iterative manner. The approach is exemplified with the following setup: The path of two UAVs are planned so they can serve as relay nodes between a target without line of sight to the base station.

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“…Also working on UAVs, Ademoye and Davari produced another mixed integer control algorithm for the coordinated control between multiple air vehicles [2]. Likewise, Reinl and von Stryk produced a slight variation on this general theme where they produced a mixed integer control for UAVs while adding constraints that insure a continuous line of communication between aircraft [19]. In a different application area, but using a similar approach, Earl and D'Andrea created a mixed integer control algorithm for a series of robots competing in the RoboFlag competition [10].…”

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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Optimal Control of Multi-Vehicle-Systems Under Communication Constraints Using Mixed-Integer Linear Programming

Cited by 24 publications

References 16 publications

On corner cutting in multi-obstacle avoidance problems

On corner cutting in multi-obstacle avoidance problems

Path Planning for UAVs Under Communication Constraints Using SPLAT! and MILP

A cooperative control algorithm for camera based observational systems.

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