UAV Formation Flight Based on Nonlinear Model Predictive Control

Zhou, Chao; Zhou, Shao Lei; Lei, Ming; Zhang, Wen Guang

doi:10.1155/2012/261367

Cited by 90 publications

(76 citation statements)

References 24 publications

(1 reference statement)

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“…We do not rely on following a given trajectory, as in most of the state-of-the-art methods [45], [46], [47]. We propose to integrate the stabilization of followers in the desired positions behind the leader together with the trajectory planning into a desired goal area with obstacle avoidance ability for the entire formation.…”

Section: Multi-robot Scenarios Demonstrating the Practical Usabilmentioning

confidence: 99%

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

et al. 2016

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Abstract-A complex system for control of swarms of Micro Aerial Vehicles (MAV), in literature also called as Unmanned Aerial Vehicles (UAV) or Unmanned Aerial Systems (UAS), stabilized via an onboard visual relative localization is described in this paper. The main purpose of this work is to verify the possibility of self-stabilization of multi-MAV groups without an external global positioning system. This approach enables the deployment of MAV swarms outside laboratory conditions, and it may be considered an enabling technique for utilizing fleets of MAVs in real-world scenarios. The proposed visualbased stabilization approach has been designed for numerous different multi-UAV robotic applications (leader-follower UAV formation stabilization, UAV swarm stabilization and deployment in surveillance scenarios, cooperative UAV sensory measurement) in this paper. Deployment of the system in real-world scenarios truthfully verifies its operational constraints, given by limited onboard sensing suites and processing capabilities. The performance of the presented approach (MAV control, motion planning, MAV stabilization, and trajectory planning) in multi-MAV applications has been validated by experimental results in indoor as well as in challenging outdoor environments (e.g., in windy conditions and in a former pit mine).

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Section: Multi-robot Scenarios Demonstrating the Practical Usabilmentioning

confidence: 99%

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

et al. 2016

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“…Different methods dealing with formation reconfiguration for a group of UGV have been proposed in the literature [4,5,8]. Many methods exploit Model Predictive Control (MPC) based on time horizon and optimization of a cost function [4].…”

Section: Proposed Strategy For Formation Reconfigurationmentioning

confidence: 99%

“…Many methods exploit Model Predictive Control (MPC) based on time horizon and optimization of a cost function [4]. These methods are generally time consuming due to predictive computation w.r.t.…”

Section: Proposed Strategy For Formation Reconfigurationmentioning

confidence: 99%

Adaptive Leader-Follower Formation in Cluttered Environment Using Dynamic Target Reconfiguration

Vilca

Adouane

Mezouar

2016

Springer Tracts in Advanced Robotics

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This paper presents a control architecture for safe and smooth navigation of a group of Unmanned Ground Vehicles (UGV) while keeping a specific formation. The formation control is based on Leader-follower and Behavioral approaches. The proposed control architecture is designed to allow the use of a single control law for different multi-vehicle contexts (navigation in formation, transition between different formation shapes, obstacle avoidance, etc.). The obstacle avoidance strategy is based on the limit-cycle approach while taking into account the dimension of the formation. A new Strategy for Formation Reconfiguration (SFR) of the group of UGVs based on suitable smooth switching of the set-points (according, for instance, to the encountered obstacles or the new task to achieve) is proposed. The inter-vehicles collisions are avoided during the SFR using a penalty function acting on the vehicle velocities. Different simulations on cluttered environments show the performance and the efficiency of the proposal, to obtain fully reactive and distributed control strategy for the navigation in formation of a group of UGVs.

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“…In the formation driving, researchers take advantage of MPC mainly to respond to changes in dynamic environment [31], [32], [33], [12], [13]. In [31], authors introduce a new cost penalty in MPC optimization to guarantee a simple obstacle avoidance. Decentralized receding horizon motion planner introduced in [32] is developed for coordination of UGVs based on a motion planning independent to neighbors.…”

Section: State Of the Artmentioning

confidence: 99%

Fault-Tolerant Formation Driving Mechanism Designed for Heterogeneous MAVs-UGVs Groups

Saska

Krajník

Vonásek

et al. 2013

J Intell Robot Syst

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UAV Formation Flight Based on Nonlinear Model Predictive Control

Cited by 90 publications

References 24 publications

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

Adaptive Leader-Follower Formation in Cluttered Environment Using Dynamic Target Reconfiguration

Fault-Tolerant Formation Driving Mechanism Designed for Heterogeneous MAVs-UGVs Groups

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