Obstacle avoidance and formation regrouping strategy and control for UAV formation flight

Luo, Delin; Ting, Zhou; Wu, Shunxiang

doi:10.1109/icca.2013.6565093

Cited by 21 publications

(5 citation statements)

References 9 publications

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“…Generally, satellites in constellation are placed in fixed orbits with different altitude and phase [21]. On the contrary, the control of UAV formation utilises the header‐follower strategy to keep a desired position with neighbours [22], where nodes in header UAV set

H = falsefalse{h_{l} | 1 \leq l \leq L falsefalse}

are responsible to guide those in follower UAV set

F = falsefalse{f_{k} | 1 \leq k \leq K falsefalse}

. It is obvious that

U = H \cup F

and

N = L + K

.…”

Section: Problem Formationmentioning

confidence: 99%

Markov decision process‐based routing algorithm in hybrid Satellites/UAVs disruption‐tolerant sensing networks

Yuan

Wang

et al. 2019

IET Communications

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H = falsefalse{h_{l} | 1 \leq l \leq L falsefalse}

are responsible to guide those in follower UAV set

F = falsefalse{f_{k} | 1 \leq k \leq K falsefalse}

. It is obvious that

U = H \cup F

and

N = L + K

.…”

Section: Problem Formationmentioning

confidence: 99%

Markov decision process‐based routing algorithm in hybrid Satellites/UAVs disruption‐tolerant sensing networks

Yuan

Wang

et al. 2019

IET Communications

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“…The range of applications is steadily growing including applications such as surveillance, exploration of regions, creating decoys, delivery of payloads (i.e. distributing ground based sensor networks), and radar and communication jamming [12,13,14,15].…”

Section: Introductionmentioning

confidence: 99%

Formation reconfiguration of cooperative UAVs via Learning Based Model Predictive Control in an obstacle-loaded environment

Hafez

Givigi

2016

2016 Annual IEEE Systems Conference (SysCon)

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Learning Based Model Predictive Control (LBMPC) is a new control policy that combines statistical learning along with control engineering while providing levels of guarantees on safety, robustness and convergence. The designed control policy respects the general rules of flocking such that when static obstacles appear, the UAVs are required to steer around them and also avoid collisions between each other. Also, each UAV in the team match the other team members velocity and stay close to its flockmates during flight. Our main contribution in this paper lays in solving the formation reconfiguration problem for a group of N cooperative UAVs forming a desired formation using LBMPC in the presence of uncertainties and obstacles in simulation.

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“…The second category is the distributed control method, where each UAV exchanges information only with its neighbors. The leader-follower scheme [6][7][8][9][10] selects leader UAVs, and the remaining followers update their locations by exchanging information with the nearest leader. However, this method has poor robustness.…”

Section: Introductionmentioning

confidence: 99%

A Scalable Distributed Control Algorithm for Bearing-Only Passive UAV Formation Maintenance

Gao

Feng

Chen

et al. 2023

Sensors

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Unmanned Aerial Vehicles (UAVs) can cooperate through formations to perform tasks. Wireless communication allows UAVs to exchange information, but for the situations requiring high security, electromagnetic silence is needed to avoid potential threats. The passive UAV formation maintenance strategies can fulfill the requirement of electromagnetic silence at the cost of heavy real-time computing and precise locations of UAVs. To pursue high real-time performance without the localization of UAVs, this paper proposes a scalable distributed control algorithm for bearing-only passive UAV formation maintenance. By minimizing necessary communication, pure angle information is applied to maintain UAV formations through distributed control, without the knowledge of the UAVs’ precise locations. The convergency of the proposed algorithm is proven strictly and the converging radius is derived. Through simulation, the proposed algorithm is proven to be suitable for a general case and demonstrates fast convergence speed, strong anti-interference capability, and high scalability.

show abstract

Obstacle avoidance and formation regrouping strategy and control for UAV formation flight

Cited by 21 publications

References 9 publications

Markov decision process‐based routing algorithm in hybrid Satellites/UAVs disruption‐tolerant sensing networks

Markov decision process‐based routing algorithm in hybrid Satellites/UAVs disruption‐tolerant sensing networks

Formation reconfiguration of cooperative UAVs via Learning Based Model Predictive Control in an obstacle-loaded environment

A Scalable Distributed Control Algorithm for Bearing-Only Passive UAV Formation Maintenance

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