Chiara Gabellieri, Marco Tognon, Dario Sanalitro, Lucia Pallottino, Antonio Franchi
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Cooperative manipulation is a basic skill in groups of humans, animals and in many robotic applications. Besides being an interesting challenge, communication-less approaches have been applied to groups of robots in order to achieve higher scalability and simpler hardware and software design. We present a generic model and control law for robots cooperatively manipulating an object, for both ground and floating systems. The control method exploits a leader–follower scheme and is based only on implicit communication (i.e., the sensing of contact forces). The control objective mainly consists of steering the object manipulated by the swarm of robots to a desired position and orientation in a cooperative way. For a system with just one leader, we present analytical results on the equilibrium configurations and their stability that are then validated by numerical simulations. The role of object internal forces (induced by the robots through contact forces) is discussed in terms of convergence of the object position and orientation to the desired values. We also present a discussion on additional properties of the controlled system that were investigated using thorough numerical analysis, namely the robustness of the system when the object is subject to external disturbances in non-ideal conditions, and how the number of leaders in the swarm can affect the aforementioned convergence and robustness.
期刊介绍:
Swarm Intelligence is the principal peer-reviewed publication dedicated to reporting on research
and developments in the multidisciplinary field of swarm intelligence. The journal publishes
original research articles and occasional review articles on theoretical, experimental and/or
practical aspects of swarm intelligence. All articles are published both in print and in electronic
form. There are no page charges for publication. Swarm Intelligence is published quarterly.
The field of swarm intelligence deals with systems composed of many individuals that coordinate
using decentralized control and self-organization. In particular, it focuses on the collective
behaviors that result from the local interactions of the individuals with each other and with their
environment. It is a fast-growing field that encompasses the efforts of researchers in multiple
disciplines, ranging from ethology and social science to operations research and computer
engineering.
Swarm Intelligence will report on advances in the understanding and utilization of swarm
intelligence systems, that is, systems that are based on the principles of swarm intelligence. The
following subjects are of particular interest to the journal:
• modeling and analysis of collective biological systems such as social insect colonies, flocking
vertebrates, and human crowds as well as any other swarm intelligence systems;
• application of biological swarm intelligence models to real-world problems such as distributed
computing, data clustering, graph partitioning, optimization and decision making;
• theoretical and empirical research in ant colony optimization, particle swarm optimization,
swarm robotics, and other swarm intelligence algorithms.