Cooperative control for multiple autonomous UAV's searching for targets

Flint, Matthew; Polycarpou, Marios M.; Fernández-Gaucherand, E.

doi:10.1109/cdc.2002.1184272

Cited by 92 publications

(54 citation statements)

References 7 publications

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“…As mentioned above, there have been many works on cooperative control of UAVs, where most of them implemented and simulated UAV controllers in a two dimensional space as in ( [33], [47], [34], [35], [36], [37], [38], [39], [32], [40] and [34]). Few of them utilized a three dimensional space but the authors used a simple linear model or did not consider the attitude problem of the UAV ( [44], [46], [48]).…”

Section: A Related Workmentioning

confidence: 99%

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Distributed model predictive control for unmanned aerial vehicles

Mansouri

Nikolakopoulos

Gustafsson

2015

2015 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS)

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Abstract-In this article a distributed model predictive control scheme, for the cooperative motion control of Unmanned Aerial Vehicles (UAVs) is being presented. The UAVs are modeled by a 6-DOF nonlinear kinematic model. Two di↵erent control architectures: a centralized and a distributed MPC, are studied and evaluated in simulation experiments. In the centralized approach, one central MPC controller is responsible for the movement coordination of all the UAVs, while in the distributed approach each aerial vehicle plans only for its own actions, while the objective function is coupled with the behavior of the rest of the team members and the constraints are decoupled. In this approach, each agent only shares the future position of itself with the other agents to avoid collisions. For reducing the computation time and complexity, only one step ahead prediction in the corresponding MPC schemes have been considered without a loss of generality. Finally, the e ciency of the overall suggested decentralized MPC scheme, as well as it comparison with the centralized approach, is being evaluated through the utilization of multiple simulation scenarios.

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Section: A Related Workmentioning

confidence: 99%

“…Previous works in this area utilized di↵erent methods for solving this issue, for example in [33], the controller was implemented for each UAV to search the environment and identify the maximum number of targets at the end of the search. Their model gain was based on maximizing the expected number of targets found.…”

Section: A Related Workmentioning

confidence: 99%

Distributed model predictive control for unmanned aerial vehicles

Mansouri

Nikolakopoulos

Gustafsson

2015

2015 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS)

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“…In a military application, multiple specialized vehicles may be more effective, and cheaper, than a single UAV which must undertake all search and attack tasks by itself, see (Jin, et al, 2003). In civilian terms, UAVs can be used for emergency tasks such as searching for survivors in dangerous environments or locating spills or leaks of dangerous chemicals etc., see (Flint, et al, 2002;Bruemmer, et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Evolutionary Search Algorithm With Obstacle Avoidance for Multiple Uavs

Carruthers

McGookin

Murray-Smith

2005

IFAC Proceedings Volumes

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Unmanned air vehicles (UAVs) can be used for surveillance and searching, usually with a specified goal i.e. a given area to search or, person or object to find. In these cases, multiple UAVs can be more effective for searching than a single vehicle due to the greater area of coverage that they offer. This paper outlines a search methodology, based on evolutionary algorithms (EAs), which guides a swarm of UAVs throughout a search mission. The autonomously navigating vehicles also avoid unforeseen obstacles via reactive obstacle sensing.

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“…The primary requirements of autonomy are the capabilities of detecting internal and external changes, and of reacting to them without human intervention in a safe and efficient manner. This can be achieved by developing and implementing autonomous guidance and control systems (AGCS) to ''pilot'' unmanned vehicles (Rathbun et al, 2002;Finke et al, 2003;Flint et al, 2002;Jun et al, 2002;Pongpunwattana & Rysdyk, 2004;Nikolas et al, 2003;Zhu et al, 2005;Waydo & Murray,2003). Tracking highly mobile targets is a type of mission that can significantly benefit from the use of UAVs (Unmanned Aerial Vehicles) with the capability of autonomy, especially when the pursuit is to take place in an environment where various sources of ''threat'', obstacles and restricted areas may exist.…”

Section: Introductionmentioning

confidence: 99%