Swarm Robotics

Edited by
May 2019
310 pages
  • ISBN978-3-03897-922-7 (Paperback)
  • ISBN978-3-03897-923-4 (PDF)

This book is a reprint of the Special Issue Swarm Robotics that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Environmental & Earth Sciences
Physical Sciences

Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties:


  1. Autonomy—Individuals that create the swarm robotic system are autonomous robots. They are independent and can interact with each other and the environment.
  2. Large number—They are in large number, enabling cooperation.
  3. Scalability and robustness—A new unit can be easily added to the system, so the system can be easily scaled. A greater number of units improves the performance of the system. The system is quite robust to the loss of some units, as some units still remain to perform, although the system will not perform to its maximum capabilities.
  4. Decentralized coordination—The robots communicate with each other and with their environment to make final decisions.
  5. Flexibility—The swarm robotic system has the ability to generate modularized solutions to different tasks.
  • Paperback
License and Copyright
© 2019 by the authors; CC BY-NC-ND license
3D model identification; shape normalization; weighted implicit shape representation; panoramic view; scale-invariant feature transform; optimization; meta-heuristic; parallel technique; Swarm intelligence algorithm; artificial flora (AF) algorithm; bionic intelligent algorithm; particle swarm optimization; artificial bee colony algorithm; swarm robotics; search; surveillance; behaviors; patterns; comparison; swarm behavior; Swarm Chemistry; self-organization; asymmetrical interaction; genetic algorithm; cooperative target hunting; multi-AUV; improved potential field; surface-water environment; signal source localization; multi-robot system; event-triggered communication; consensus control; time-difference-of-arrival (TDOA); Cramer–Rao low bound (CRLB); optimal configuration; UAV swarms; path optimization; multiple robots; formation; sliding mode controller; nonlinear disturbance observer; system stability; formation control; virtual structure; formation reconfiguration; multi-agents; robotics; unmanned aerial vehicle; swarm intelligence; particle swarm optimization; search algorithm; underwater environment; sensor deployment; event-driven coverage; fish swarm optimization; congestion control; modular robots; self-assembly robots; environmental perception; target recognition; autonomous docking; formation control; virtual linkage; virtual structure; formation reconfiguration; mobile robots; robotics; swarm robotics; formation control; coordinate motion; obstacle avoidance; n/a