Robotic Herding of a Flock of Birds Using an Unmanned Aerial Vehicle

Paranjape, Aditya A.; Chung, Soon‐Jo; Kim, Kyunam; Shim, David Hyunchul

doi:10.1109/tro.2018.2853610

Cited by 114 publications

(55 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An example of adversarial control is the family containment and herding strategies modeled after dolphins [21], sheep-dogs [147]- [152] and birds of prey used to herd a flock of birds [153]. In [153], the authors examined the use of a robotic UAV, possibly one built to resemble a bird of prey like a falcon, to herd flocks of birds away from sensitive areas like airports and solar farms. The UAV interacts with the flock by engaging birds located on the boundary of the flock.…”

Section: E External Control Of Aerial Swarmsmentioning

confidence: 99%

“…The perturbation in the velocity of the birds on the boundary of the flock diffuses through the swarm and causes the flock to alter its heading and speed. It has been shown in [153] that a single robotic agent suffices to herd a flock of birds, while related work [150] suggests that the quality of the herding can be improved substantially by using multiple UAVs.…”

Section: E External Control Of Aerial Swarmsmentioning

confidence: 99%

“…If the model is known, together with the response of the swarm to an adversary, it would be possible to not just design optimal strategies to divert or control the swarm, but also derive guarantees on the performance of such strategies. In [153], experimental data was used to identify a model, based on [154], [155], for the response of a flock of birds to a UAV located within a certain range of the flock. The approach adopted in [153] works for flocks whose response to perturbations is based on a static, deterministic law.…”

Section: E External Control Of Aerial Swarmsmentioning

confidence: 99%

“…In [153], experimental data was used to identify a model, based on [154], [155], for the response of a flock of birds to a UAV located within a certain range of the flock. The approach adopted in [153] works for flocks whose response to perturbations is based on a static, deterministic law. When the response takes a more strategic, dynamic form, it is necessary to use learning-based techniques which expressly account for this behavior [156], [157].…”

Section: E External Control Of Aerial Swarmsmentioning

confidence: 99%

See 3 more Smart Citations

A Survey on Aerial Swarm Robotics

et al. 2018

Self Cite

View full text Add to dashboard Cite

Abstract-The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional (3-D) space, and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of the paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

show abstract

Section: E External Control Of Aerial Swarmsmentioning

confidence: 99%

Section: E External Control Of Aerial Swarmsmentioning

confidence: 99%

Section: E External Control Of Aerial Swarmsmentioning

confidence: 99%

Section: E External Control Of Aerial Swarmsmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Aerial Swarm Robotics

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…The robotic herding approach to herd a flock of birds away from an airport in [4] uses an n-wavefront algorithm, where the birds on the boundary of the flock are influenced based on the locations of the airport and a safe area. Stability and performance guarantees for a bird flock with directed star communication graph are provided in [5], as well as experimental results in [6].…”

Section: Introductionmentioning

confidence: 99%

Herding an Adversarial Swarm in an Obstacle Environment

Chipade

Panagou

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

View full text Add to dashboard Cite

This paper studies a problem of defending safety-critical infrastructure from adversarial swarm. We employ a closed formation ('StringNet') of defending agents around the adversarial agents to restrict their motion and guide them to a safe area through obstacle populated environment. Control laws for forming this StringNet and guiding it to a safe area are developed and the performance is analyzed formally. Flocking motion is considered for the adversarial swarm in the presence of rectangular obstacles for which modifications to the existing flocking control laws are provided.The authors are with the

show abstract

Stabilizing Aerodynamic Dampers for Cooperative Transport of a Suspended Payload with Aerial Robots

Dong

Ding

Bai

et al. 2023

Advanced Intelligent Systems

View full text Add to dashboard Cite

The current paper addresses the problem of stabilizing multiple aerial robots cooperatively transporting a cable‐suspended payload by an aeromechanic method. Instead of relying on global navigation satellite systems (GNSS) or vision and communication for agents to actively estimate and control the state of the multibody dynamics, lightweight air dampers to make the multiagent system inherently stable at its equilibrium state, permitting the robots to safely carry a load at a constant velocity are employed. This is achieved without additional state estimation or active correction. The proposed framework is proven stable and verified by simulations and extensive flight experiments. Lightweight mechanical dampers (under 7 g) are shown to be effective in attenuating undesired oscillations and overcoming disturbances. To this end, a team of four robots cooperatively transporting a payload over 20 m in open space is demonstrated, and three robots safely transporting a point‐mass payload over a distance of 45 m outdoors. The promising outcomes highlight the benefits of the passive strategy, which demands minimal hardware components and computation to realize the sophisticated aerial transport task.

show abstract

Robotic Herding of a Flock of Birds Using an Unmanned Aerial Vehicle

Cited by 114 publications

References 36 publications

A Survey on Aerial Swarm Robotics

A Survey on Aerial Swarm Robotics

Herding an Adversarial Swarm in an Obstacle Environment

Stabilizing Aerodynamic Dampers for Cooperative Transport of a Suspended Payload with Aerial Robots

Contact Info

Product

Resources

About