Scalable and Robust Shepherding via Deformable Shapes

Harrison, Joseph F.; Vo, Christopher; Lien, Jyh-Ming

doi:10.1007/978-3-642-16958-8_21

Cited by 22 publications

(7 citation statements)

References 21 publications

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“…Following their previous research ( [9], [10], [13], [51]), Harrison, Vo and Lien [52] produced a new control method for shepherding a swarm, in which the shepherd models the swarm as an abstract, deformable shape. Using this abstraction, the shepherd continually updates the current swarm shape by calculating a new desired distribution, then moving to steering points behind the swarm (with respect to the goal location) to achieve it.…”

Section: Shepherding Control Methodsmentioning

confidence: 99%

A Comprehensive Review of Shepherding as a Bio-inspired Swarm-Robotics Guidance Approach

Long¹,

Sammut²,

Sgarioto³

et al. 2019

Preprint

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The simultaneous control of multiple coordinated robotic agents represents an elaborate problem. If solved, however, the interaction between the agents can lead to solutions to sophisticated problems. The concept of swarming, inspired by nature, can be described as the emergence of complex systemlevel behaviors from the interactions of relatively elementary agents. Due to the effectiveness of solutions found in nature, bioinspired swarming-based control techniques are receiving a lot of attention in robotics. One method, known as swarm shepherding, is founded on the sheep herding behavior exhibited by sheepdogs, where a swarm of relatively simple agents are governed by a shepherd (or shepherds) which is responsible for high-level guidance and planning. Many studies have been conducted on shepherding as a control technique, ranging from the replication of sheep herding via simulation, to the control of uninhabited vehicles and robots for a variety of applications. We present a comprehensive review of the literature on swarm shepherding to reveal the advantages and potential of the approach to be applied to a plethora of robotic systems in the future.

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Section: Shepherding Control Methodsmentioning

confidence: 99%

A Comprehensive Review of Shepherding as a Bio-inspired Swarm-Robotics Guidance Approach

Long¹,

Sammut²,

Sgarioto³

et al. 2019

Preprint

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show abstract

“…The design of an adversarial strategy that the UAVs can apply when the AALVs are actively trying to escape is considered to be a counter-strategy to the well-studied herding controllers discussed in refs. [1, 4, 5], where the objective is to guide a group of agents toward a certain area. Some of these controllers are bio-inspired herding mechanisms (e.g., sheepdogs guiding a herd of sheep) [2, 3] that can also be applied to swarming agents on both ends, as seen in ref.…”

Section: Related Workmentioning

confidence: 99%

“…It was later found that shepherd movement based only on the agents' center of mass is effective [3], although cooperation and/or synchronization was not considered. Other non-traditional herding methods involve the abstraction of the flock's geometry as a deformable blob [4], which is useful when the flock has a complex motion or grows considerably in size. In general, shepherding methods and their outcomes thrive in simulation environments but real applications are intended for swarm robotic systems [5].…”

Section: Introductionmentioning

confidence: 99%

Adversarial scenarios for herding UAVs and counter-swarm techniques

Vasquez

Barca²

2023

Robotica

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The present paper aims to design and simulate an adversarial strategy where a swarm of quadrotor UAVs is herding anti-aircraft land vehicles (AALV) that actively oppose the swarm’s objective by potentially taking them down. The main strategy is to block the AALVs’ line of sight to their goal zone (AALVs’ objective), shifting its trajectory so it reaches a kill zone instead (UAVs’ objective). The counter-swarm strategy performed by the AALVs consists of taking down the closest aerial units to the goal zone. As a result, a consensus algorithm is executed by the UAVs in order to assess the communication network and re-group. Consensus is based on the propagation of local observations that converge into a global agreement on a communication graph. Re-grouping is done via positioning around the kill zone vector or preferring an anti-clockwise formation to better close gaps. The adversarial strategy was tested in an empty arena and urban setting, the latter making use of a path-planning procedure that re-routes the AALV trajectory based on its current destination. Simulation results show a maximum UAV mission success rate converging to roughly 80% in the empty arena. When targeted elimination procedures are executed, UAV mission performance drops 5%, making no distinction between re-grouping strategies in the empty arena. The urban setting shows lower performance due to navigation complexity but favors the decision to re-group based on a formation that close gaps rather than positioning around the kill zone vector.

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“…For instance, Vo et al [10] proposed a behavior-based method which selects intermediate goals on the roadmap of the workspace medial axis. Harrison et al [11] developed a deformable shape model for shepherding control, they modeled the swarm robots as a deformable shape, in their method, the shepherd continually computes the swarm shape and move to the steering points based on the swarm shape. Strömbom et al [12] proposed a heuristic method and described two behaviors: driving and collecting.…”

Section: Related Workmentioning

confidence: 99%

Learning to Herd Agents Amongst Obstacles: Training Robust Shepherding Behaviors using Deep Reinforcement Learning

Zhi

Lien

2020

Preprint

Self Cite

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Robotic shepherding problem considers the control and navigation of a group of coherent agents (e.g., a flock of bird or a fleet of drones) through the motion of an external robot, called shepherd. Machine learning based methods have successfully solved this problem in an empty environment with no obstacles. Rule-based methods, on the other hand, can handle more complex scenarios in which environments are cluttered with obstacles and allow multiple shepherds to work collaboratively. However, these rule-based methods are fragile due to the difficulty in defining a comprehensive set of rules that can handle all possible cases. To overcome these limitations, we propose the first known learning-based method that can herd agents amongst obstacles. By using deep reinforcement learning techniques combined with the probabilistic roadmaps, we train a shepherding model using noisy but controlled environmental and behavioral parameters. Our experimental results show that the proposed method is robust, namely, it is insensitive to the uncertainties originated from both environmental and behavioral models. Consequently, the proposed method has a higher success rate, shorter completion time and path length than the rule-based behavioral methods have. These advantages are particularly prominent in more challenging scenarios involving more difficult groups and strenuous passages.

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Scalable and Robust Shepherding via Deformable Shapes

Cited by 22 publications

References 21 publications

A Comprehensive Review of Shepherding as a Bio-inspired Swarm-Robotics Guidance Approach

A Comprehensive Review of Shepherding as a Bio-inspired Swarm-Robotics Guidance Approach

Adversarial scenarios for herding UAVs and counter-swarm techniques

Learning to Herd Agents Amongst Obstacles: Training Robust Shepherding Behaviors using Deep Reinforcement Learning

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