Analysis of local-camera-based shepherding navigation

Tsunoda, Yusuke; Sueoka, Yuichiro; Sato, Yuto; Osuka, Koichi

doi:10.1080/01691864.2018.1539410

Cited by 26 publications

(21 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While most shepherding control approaches assume that the shepherd(s) have global knowledge of the swarm agent positions, Tsunado et al [56] tested a more sheepdog-realistic scenario in which the shepherd only had swarm position information collected via a local camera. Three different shepherding control strategies were used.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…As in the literature (see, e.g., [14], [15]), we adopt the following equation as the mathematical model for describing the movement of the sheep agents:…”

Section: Problem Statementmentioning

confidence: 99%

“…In this section, we describe the algorithm we propose for the movement of the shepherd agents. We start by recalling the Farthest-Agent Targeting (FAT) algorithm [14] designed for the case of a single shepherd (i.e., M = 1). In the algorithm, the movement of the (1st) shepherd is specified as q 1 (t + 1) = q 1 (t) + v 1 (t), where v 1 (t) ∈ R 2 represents the movement vector of the shepherd.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…Despite being simple, the FAT algorithm is known for its effectiveness in performing the shepherding navigation with a single shepherd [14]. However, the algorithm requires a global knowledge of the positions of all sheep.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…The objective of this paper is to propose an algorithm for communication-free shepherding navigation with multiple steering agents. Our approach is to start from an existing single-shepherd algorithm called Farthest-Agent Targeting algorithm [14]. Leveraging on the simplicity of the algorithm, we then construct an algorithm for the shepherding by multiple steering agents under the assumption that each shepherd knows its relative position to the goal, and the relative position of other agents within the shepherd's recognition range to the goal.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Communication-Free Shepherding Navigation with Multiple Steering Agents

Li¹,

Ogura²,

Wakamiya³

2022

Preprint

View full text Add to dashboard Cite

Swarm guidance addresses a challenging problem considering the navigation and control of a group of passive agents. To solve this problem, shepherding offers a bio-inspired technique of navigating such group of agents by using external steering agents with appropriately designed movement law. Although most shepherding researches are mainly based on the availability of centralized instructions, these assumptions are not realistic enough to solve some emerging application problems. Therefore, this paper presents a decentralized shepherding method where each steering agent makes movements based on its own observation without any inter-agent communication. Our numerical simulations confirm the effectiveness of the proposed method by showing its high success rate and low costs in various placement patterns. These advantages particularly improve with the increase in the number of steering agents.

show abstract