Decentralized optimization, with application to multiple aircraft coordination

Tsourdos, Antonios; Stipanović, Dušan M.; Tomlin, Claire J.

doi:10.1109/cdc.2002.1184667

Cited by 195 publications

(139 citation statements)

References 11 publications

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“…Basically, the flight path of an aircraft can be divided into modes, and these modes serve as reference blocks to a control system, and the control system regulates these modes with given specifications by possibly using nonlinear control laws (Tomlin and Rosh, 2000). Note that this modal approach can also be used for trajectory optimization for multiple vehicles (Inalhan, Stipanovic, Tomlin, 2002). (Frazzoli et.…”

Section: Previous and Related Workmentioning

confidence: 99%

A Probabilistic Algorithm for Mode Based Motion Planning of Agile Unmanned Air Vehicles in Complex Environments

Koyuncu

Üre

Tsourdos

2008

IFAC Proceedings Volumes

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Abstract:In this work, we consider the design of a probabilistic trajectory planner for a highly maneuverable unmanned air vehicle flying in a dense and complex city-like environment. Our design hinges on the decomposition of the problem into a) flight controls of fundamental agile-maneuvering flight modes and b) trajectory planning using these controlled flight modes from which almost any aggressive maneuver (or a combination of) can be created. This allows significant decreases in control input space and thus search dimensions, resulting in a natural way to design controllers and implement trajectory planning using the closed-form flight modes. Focusing on the trajectory planning part, we provide a three-step probabilistic trajectory planner. In the first step, the algorithm rapidly explores the environment through a randomized reachability tree search using an approximate line segment models. The resulting connecting path is converted into flight milestones through a line-of-sight segmentation. This path and the corresponding milestones are refined with a single-query Probabilistic Road Map (PRM) implementation that creates dynamically feasible flight paths with distinct flight mode selections. We address the problematic issue of narrow passages through non-uniform distributed capture regions, which prefer state solutions that align the vehicle to enter the milestone region in line with the next milestone to come. Numerical simulations in 3D and 2D demonstrate the ability of the method to provide real-time solutions in dense and complex environments.

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Section: Previous and Related Workmentioning

confidence: 99%

A Probabilistic Algorithm for Mode Based Motion Planning of Agile Unmanned Air Vehicles in Complex Environments

Koyuncu

Üre

Tsourdos

2008

IFAC Proceedings Volumes

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“…Whereas most decentralized algorithms [10], [11] consist of computing the optimal trajectory for each vehicle by freezing the state of other agents, ours proceeds differently. We will first present an intuitive description of the algorithm; its mathematical justification and analysis will be presented in Section 3.…”

Section: B Decentralized Algorithmmentioning

confidence: 99%

“…Examples include trajectory optimization for formation control of vehicles [11], [18], [19], [9], [20], [10], decentralized control of power systems [17], distributed control of large scale systems [7], [5], [12], and problems of distributed optimization on sensor networks [14]. In most of these instances, the treatment is control oriented: the goal is to prove connectivity and stability of the system under decentralized control.…”

Section: Introductionmentioning

confidence: 99%

Distributed optimization for cooperative agents: application to formation flight

Raffard

Tomlin²,

Boyd³

2004

2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601)

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192

145

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Abstract-We present a simple decentralized algorithm to solve optimization problems involving cooperative agents. Cooperative agents share a common objective and simultaneously pursue private goals. Furthermore, agents are constrained by limited communication capabilities. The algorithm is based on dual decomposition techniques and appears to be very intuitive. It solves the dual problem of an artificially decomposed version of the primal problem, replacing one large computationally intractable problem with many smaller tractable problems. It returns a feasible solution to the primal problem as well as an upper bound on the distance between this solution and the global optimum. Both convex and nonconvex examples are presented, the complexity of the convex case is analyzed, and the savings in complexity are demonstrated for both examples. Finally, by showing that there is no duality gap in these examples, optimality is certified.

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“…Multi agent coordination techniques are used in various tasks like Air Traffic Management [16] while there are several decentralized algorithms like [13] for aircraft-like vehicles. Air Traffic Management of the future allows for the possibility of free flight, in which aircrafts choose their own optimal routes, altitudes, and velocities.…”

Section: Spatial Multi Agent Systems and Their Coordinationmentioning

confidence: 99%

Virtual Spring-Based 3D Multi-Agent Group Coordination

Daneshvar

Shih

2012

Unifying Themes in Complex Systems VII

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As future personal vehicles start enjoying the ability to fly, tackling safe transportation coordination can be a tremendous task, far beyond the current challenge on radar screen monitoring of the already saturated air traffic control. Our focus is on the distributed safe-distance coordination among a group of autonomous flying vehicle agents, where each follows its own current straight-line direction in a 3D space with variable speeds. A virtual spring-based model is proposed for the group coordination. Within a specified neighborhood radius, each vehicle forms a virtual connection with each neighbor vehicle by a virtual spring. As the vehicle changes its position, speed and altitude, the total resultant forces on each virtual spring try to maintain zero by moving to the mechanical equilibrium point. The agents then add the simple total virtual spring constraints to their movements to determine their next positions individually. Together, the multi-agent vehicles reach a group behavior, where each of them keeps a minimal safe-distance with others. A new safe behavior thus arises in the group level. With the proposed virtual spring coordination model, the vehicles need no direct communication with each other, require only minimum local processing resources, and the control is completely distributed. New behaviors can now be formulated and studied based on the proposed model, e.g., how a fast driving vehicle can find its way though the crowd by avoiding the other vehicles effortlessly 1 .

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Decentralized optimization, with application to multiple aircraft coordination

Cited by 195 publications

References 11 publications

A Probabilistic Algorithm for Mode Based Motion Planning of Agile Unmanned Air Vehicles in Complex Environments

A Probabilistic Algorithm for Mode Based Motion Planning of Agile Unmanned Air Vehicles in Complex Environments

Distributed optimization for cooperative agents: application to formation flight

Virtual Spring-Based 3D Multi-Agent Group Coordination

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