A Variable Communication Approach for Decentralized Receding Horizon Control of Multi-Vehicle Systems

Izadi, Hojjat A.; Gordon, Brandon W.; Rabbath, C.A.

doi:10.1109/acc.2007.4283112

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…which contains four UA Vs. Tn this work, the UA Vs are assumed to form a connected network, and the network is also assumed to be lossless and have negligible delay. A multi hopping communication scheme [17] is used when more than two UAVs cooperate, which enables a UAV to communicate with more UA Vs through its communication neighbors (see red dash line in Fig. 1).…”

Section: Problem Formulationmentioning

confidence: 99%

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Decentralized cooperative trajectory planning for multiple UAVs in dynamic and uncertain environments

Zhang

Yang

Chen

et al. 2015

2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS)

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This paper studies the problem of planning collision-free dynamically feasible trajectories for a team of autonomous unmanned aerial vehicles (UAVs) in real-time, where the UAVs try to fly through a complex 3-D environment to reach their specified destinations. First, the cooperative trajectory planning problem is mathematically formulated as a decentralized receding horizon optimal control problem (DRH OCP). Second, a decentralized coordination strategy for multi vehicle real-time trajectory planning is designed by effectively combining the benefits of inverse dynamics optimization method and receding horizon optimal control technique. This coordination strategy mainly features that each vehicle only solves a sub-problem for its own plan and vehicles in the team make plans in parallel. Finally, simulation results show the feasibility of the proposed planning strategies.

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Section: Problem Formulationmentioning

confidence: 99%

“…may produce a collision or may lose communication). For each VA V p, the collision conflict set lPtan and communication conflict set lPtamm are defmed as follows: (17) Accordingly, in (14) and (15) This coordination strategy is described by the flowchart given in Fig. 2.…”

Section: A Decentralized Coordination Strategymentioning

confidence: 99%

Decentralized cooperative trajectory planning for multiple UAVs in dynamic and uncertain environments

Zhang

Yang

Chen

et al. 2015

2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS)

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show abstract

“…In general, the communication topology of the UCAVs can be modeled by a graph structure, which contained some nodes and edges. 15 For each edge, there is one corresponding communication constraint between UCAV j and UCAV k , which is related to the limited range of transmitters and receivers, and can be written as…”

Section: Cooperative Constraint Modelmentioning

confidence: 99%

“…Stage 2 Calculate the coordination variable " T go ðt i Þ, and determine the coordination function f j cd . 15 Stage 2.1 Build the complete model of the UCAV j , DCRH À OCP 00 j , and obtain DCRH À NLP 00 j . 16…”

Section: Two-stage Iterative Optimization Algorithmmentioning

confidence: 99%

Real-time decentralized cooperative robust trajectory planning for multiple UCAVs air-to-ground target attack

Zhang

Chen

2014

Proceedings of the Institution of Mechanical Engineers, Part G:

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Planning cooperative trajectories in real time is of great importance for multiple unmanned combat aerial vehicles (multi-UCAV) in performing autonomous time-critical cooperative air-to-ground target attack missions. The models of the vehicle, constraints, and a multi-criteria objective function are set up, and then the problem is formulated as a decentralized cooperative receding horizon optimal control problem. An elaborate framework for provable effective decentralized cooperative trajectory planning in two-degree-of-freedom decentralized receding horizon control is presented to provide the robustness and adaptivity for trajectory planning in dynamic and uncertain environments. Furthermore, a two-stage iterative optimization algorithm is developed to solve the problem. The novel algorithm integrates the differential flatness theory to generate trajectories, and the coordination variable-based strategy to coordinate the arrival time of multi-UCAV online, which can improve the computational efficiency and achieve the cooperative objective. The results of numerical simulations show that the proposed approach is feasible and well suited for the real-time implementation in dynamic and uncertain environment.

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Communication bandwidth allocation for decentralized receding horizon control of multiple vehicles

Izadi

Gordon

Rabbath

2008

2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

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We consider the decentralized receding horizon control (DRHC) of a team of cooperative vehicles with limited communication bandwidth. The results of analyzing the feasibility, stability and performance of DRHC imply that the mismatch between predicted and actual plans of each agent plays an important role in stability and performance of the entire fleet. Hence, based on this key result, a new improved algorithm is proposed which leads to superior stability and performance of the overall team. The key idea is to reduce the mismatch between the predicted and actual plans of each agent by efficient communication bandwidth allocation. Several simulations for the formation flight of rotorcrafts verify the analytical results.

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A Variable Communication Approach for Decentralized Receding Horizon Control of Multi-Vehicle Systems

Cited by 5 publications

References 14 publications

Decentralized cooperative trajectory planning for multiple UAVs in dynamic and uncertain environments

Decentralized cooperative trajectory planning for multiple UAVs in dynamic and uncertain environments

Real-time decentralized cooperative robust trajectory planning for multiple UCAVs air-to-ground target attack

Communication bandwidth allocation for decentralized receding horizon control of multiple vehicles

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