A Comprehensive Review of Shepherding as a Bio-inspired Swarm-Robotics Guidance Approach
December 17, 2019 ยท The Cartographer ยท ๐ IEEE Transactions on Emerging Topics in Computational Intelligence
"No code URL or promise found in abstract"
"Title-pattern auto-detect: A Comprehensive Review of Shepherding as a Bio-inspired Swarm-Robotics Guidance Approach"
Evidence collected by the PWNC Scanner
Authors
Nathan K Long, Karl Sammut, Daniel Sgarioto, Matthew Garratt, Hussein Abbass
arXiv ID
1912.07796
Category
cs.RO: Robotics
Cross-listed
cs.AI,
eess.SY
Citations
102
Venue
IEEE Transactions on Emerging Topics in Computational Intelligence
Last Checked
1 day ago
Abstract
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 system-level behaviors from the interactions of relatively elementary agents. Due to the effectiveness of solutions found in nature, bio-inspired 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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Robotics
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
๐
๐
The Cartographer
A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles
๐
๐
The Cartographer
Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges
R.I.P.
๐ป
Ghosted
LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
๐
๐
The Cartographer