Agile Formation Control of Drone Flocking Enhanced With Active Vision-Based Relative Localization

Zhang, Peihan; Chen, Gang; Dong, Wei

doi:10.1109/lra.2022.3171096

Cited by 21 publications

(14 citation statements)

References 30 publications

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“…As a result, robots may have collisions with each other and have chaos in the swarm [16]. Besides, position measurement drifts may also cause robots to reach wrong destinations [20], [21].…”

Section: Start Goalmentioning

confidence: 99%

“…In this paper, the robust curve virtual tube passing through problem is summarized and solved. To achieve collision avoidance among robots, a traditional method is to share selfobservation positions with the known initial positions among robots, which suffers heavily from the position measurement drift and communication uncertainties [21]. The controller proposed in this paper can work autonomously without wireless communication and other robots' IDs, whose premise is that all robots have omnidirectional relative localization equipment to achieve precise relative navigation, namely robots can get their neighboring robots' relative position and relative velocity precisely.…”

Section: Start Goalmentioning

confidence: 99%

See 1 more Smart Citation

Robust Distributed Control within a Curve Virtual Tube for a Robotic Swarm under Self-Localization Drift and Precise Relative Navigation

Gao¹,

Bai²,

Quan³

2022

Preprint

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To guide the movement of a robotic swarm in a corridor-like environment, a curve virtual tube with no obstacle inside is designed in our previous work. This paper generalizes the controller design to the condition that all robots have selflocalization drifts and precise relative navigation, where the flocking algorithm is introduced to reduce the negative impact of the self-localization drift. It is shown that the cohesion behavior and the velocity alignment behavior are able to reduce the influence of the position measurement drift and the velocity measurement error, respectively. For the convenience in practical use, a modified vector field controller with five control terms is put forward. Finally, the effectiveness of the proposed method is validated by numerical simulations and real experiments.

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Section: Start Goalmentioning

confidence: 99%

Section: Start Goalmentioning

confidence: 99%

Robust Distributed Control within a Curve Virtual Tube for a Robotic Swarm under Self-Localization Drift and Precise Relative Navigation

Gao¹,

Bai²,

Quan³

2022

Preprint

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show abstract

“…When extra sensors, e.g., camera, visual-inertial odometry (VIO), etc, are included alongside the UWB [2], [31], as the observability is commonly not considered as a main issue ignoring sensor failures, the sliding window size and the computational efficiency have been not well considered in existing work [32], [39], [40].…”

Section: Sliding Window Filteringmentioning

confidence: 99%

SRIBO: An Efficient and Resilient Single-Range and Inertia Based Odometry for Flying Robots

Wang¹,

Mei²,

Yuanjiong³

et al. 2022

Preprint

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Positioning with one inertial measurement unit and one ranging sensor is commonly thought to be feasible only when trajectories are in certain patterns ensuring observability. For this reason, to pursue observable patterns, it is required either exciting the trajectory or searching key nodes in a long interval, which is commonly highly nonlinear and may also lack resilience. Therefore, such a positioning approach is still not widely accepted in real-world applications. To address this issue, this work first investigates the dissipative nature of flying robots considering aerial drag effects and re-formulates the corresponding positioning problem, which guarantees observability almost surely. On this basis, a dimension-reduced wriggling estimator is proposed accordingly. This estimator slides the estimation horizon in a stepping manner, and output matrices can be approximately evaluated based on the historical estimation sequence. The computational complexity is then further reduced via a dimensionreduction approach using polynomial fittings. In this way, the states of robots can be estimated via linear programming in a sufficiently long interval, and the degree of observability is thereby further enhanced because an adequate redundancy of measurements is available for each estimation. Subsequently, the estimator's convergence and numerical stability are proven theoretically. Finally, both indoor and outdoor experiments verify that the proposed estimator can achieve decimeter-level precision at hundreds of hertz per second, and it is resilient to sensors' failures. Hopefully, this study can provide a new practical approach for self-localization as well as relative positioning of cooperative agents with low-cost and lightweight sensors.

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“…In addition to the traditional algorithms mentioned above, the rapid development of deep learning has enabled it to be applied in many fields [14][15][16][17][18], including the field of UAVs. Vision-based UAV formation maintenance has improved the performance of these algorithms, but still faces the problem of huge communication requirements [19,20] in scenarios with electromagnetic silence.…”

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.

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Agile Formation Control of Drone Flocking Enhanced With Active Vision-Based Relative Localization

Cited by 21 publications

References 30 publications

Robust Distributed Control within a Curve Virtual Tube for a Robotic Swarm under Self-Localization Drift and Precise Relative Navigation

Robust Distributed Control within a Curve Virtual Tube for a Robotic Swarm under Self-Localization Drift and Precise Relative Navigation

SRIBO: An Efficient and Resilient Single-Range and Inertia Based Odometry for Flying Robots

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

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