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Swarm robotics: a review from the swarm engineering perspective
- SWARM INTELL
, 2013
"... Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of ..."
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Cited by 42 (27 self)
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Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature from the point of view of swarm engineering: we focus mainly on ideas and concepts that contribute to the advancement of swarm robotics as an engineering field and that could be relevant to tackle real-world applications. Swarm engineering is an emerging discipline that aims at defining systematic and well founded procedures for modeling, designing, realizing, verifying, validating, operating, and maintaining a swarm robotics system. We propose two taxonomies: in the first taxonomy, we classify works that deal with design and analysis methods; in the second taxonomy, we classify works according to the collective behavior studied. We conclude with a discussion of the current limits of swarm robotics as an engineering discipline and with suggestions for future research directions.
R.: Robust and self-repairing formation control for swarms of mobile agents
- In: Proceedins of 20th National Conference on Artificial Intelligence (AAAI
, 2005
"... We describe a decentralized algorithm for coordinating a swarm of identically-programmed mobile agents to spatially self-aggregate into arbitrary shapes using only local interactions. Our approach, called SHAPEBUGS, generates a consensus coordinate system by agents continually performing local trila ..."
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Cited by 20 (0 self)
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We describe a decentralized algorithm for coordinating a swarm of identically-programmed mobile agents to spatially self-aggregate into arbitrary shapes using only local interactions. Our approach, called SHAPEBUGS, generates a consensus coordinate system by agents continually performing local trilaterations, and achieves shape formation by simultaneously allowing agents to disperse within the defined 2D shape using a Contained Gas Model. This approach has several novel features (1) agents can easily aggregate into arbitrary user-specified shapes, using a formation process that is independent of the number of agents (2) the system automatically adapts to influx and death of agents, as well as accidental displacement. We show that the consensus coordinate system is robust and provides reasonable accuracy in the face of significant sensor and movement error.
Exerting Human Control Over Decentralized Robot Swarms
"... Abstract—Robot swarms are capable of performing tasks with robustness and flexibility using only local interactions between the agents. Such a system can lead to emergent behavior that is often desirable, but difficult to control and manipulate postdesign. These properties make the real-time control ..."
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Cited by 19 (0 self)
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Abstract—Robot swarms are capable of performing tasks with robustness and flexibility using only local interactions between the agents. Such a system can lead to emergent behavior that is often desirable, but difficult to control and manipulate postdesign. These properties make the real-time control of swarms by a human operator challenging—a problem that has not been adequately addressed in the literature. In this paper we present preliminary work on two possible forms of control: top-down control of global swarm characteristics and bottom-up control by influencing a subset of the swarm members. We present learning methods to address each of these. The first method uses instance-based learning to produce a generalized model from a sampling of the parameter space and global characteristics for specific situations. The second method uses evolutionary learning to learn placement and parameterization of virtual agents that can influence the robots in the swarm. Finally we show how these methods generalize and can be used by a human operator to dynamically control a swarm in real time. I.
Agent-based chemical plume tracing using fluid dynamics
- In Lecture Notes in Artificial Intelligence
, 2004
"... Abstract. This paper presents a rigorous evaluation of a novel, distributed chemical plume tracing algorithm. The algorithm is a combination of the best aspects of the two most popular predecessors for this task. Furthermore, it is based on solid, formal principles from the field of fluid mechanics. ..."
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Cited by 18 (8 self)
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Abstract. This paper presents a rigorous evaluation of a novel, distributed chemical plume tracing algorithm. The algorithm is a combination of the best aspects of the two most popular predecessors for this task. Furthermore, it is based on solid, formal principles from the field of fluid mechanics. The algorithm is applied by a network of mobile sensing agents (e.g., robots or micro-air vehicles) that sense the ambient fluid velocity and chemical concentration, and calculate derivatives. The algorithm drives the robotic network to the source of the toxic plume, where measures can be taken to disable the source emitter. This work is part of a much larger effort in research and development of a physics-based approach to developing networks of mobile sensing agents for monitoring, tracking, reporting and responding to hazardous conditions. 1
A distributed feedback mechanism to regulate wall construction by a robotic swarm
- ADAPTIVE BEHAVIOR
, 2006
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An overview of physicomimetics
- Lecture Notes in Computer Science – State of the Art Series
, 2005
"... Abstract. This paper provides an overview of our framework, called physicomimetics, for the distributed control of swarms of robots. We focus on robotic behaviors that are similar to those shown by solids, liquids, and gases. Solid formations are useful for distributed sensing tasks, while liquids a ..."
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Cited by 14 (4 self)
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Abstract. This paper provides an overview of our framework, called physicomimetics, for the distributed control of swarms of robots. We focus on robotic behaviors that are similar to those shown by solids, liquids, and gases. Solid formations are useful for distributed sensing tasks, while liquids are for obstacle avoidance tasks. Gases are handy for coverage tasks, such as surveillance and sweeping. Theoretical analyses are provided that allow us to reliably control these behaviors. Finally, our implementation on seven robots is summarized. 1
Robotic simulation of gases for a surveillance task
- In Proceedings of the 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems
, 2005
"... Proceedings of IROS’05. Abstract — The task addressed here requires a swarm of mobile robots to monitor a long corridor, i.e., by sweeping through it while avoiding large obstacles such as buildings. In the case of limited sensors and communication, maintaining spatial coverage – especially after pa ..."
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Cited by 12 (1 self)
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Proceedings of IROS’05. Abstract — The task addressed here requires a swarm of mobile robots to monitor a long corridor, i.e., by sweeping through it while avoiding large obstacles such as buildings. In the case of limited sensors and communication, maintaining spatial coverage – especially after passing the obstacles – is a challenging problem. Note that the main objective of this task is coverage. There are two primary methods for agents to achieve coverage: by uniformly increasing the inter-agent distances, and by moving the swarm as a whole. This paper presents a physics-based solution to the task that is based on a kinetic theory approach; our solution achieves both forms of coverage. Furthermore, the paper describes how we transition from our original algorithm to an algorithm utilizing mostly local sensor information, the latter being more realistic for modeling robots. To determine how well our kinetic theory approach performs against a popular alternative controller, experimental comparisons are presented. Index Terms — swarms, robotics, coverage, surveillance. I.
Two Formal Fluids Models for Multiagent Sweeping and Obstacle Avoidance
- Lecture Notes in Artificial Intelligence
, 2005
"... The task addressed here is a dynamic search through a bounded region, while avoiding multiple large obstacles, such as buildings. In the case of limited sensors and communication, maintaining spatial coverage -- especially after passing the obstacles -- is a challenging problem. Here, we investigate ..."
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Cited by 12 (1 self)
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The task addressed here is a dynamic search through a bounded region, while avoiding multiple large obstacles, such as buildings. In the case of limited sensors and communication, maintaining spatial coverage -- especially after passing the obstacles -- is a challenging problem. Here, we investigate two physics-based approaches to solving this task with multiple mobile robots, one based on artificial forces and the other based on the kinetic theory of gases. The desired behavior is achieved with both methods, and a comparison is made between them. Because both approaches are physics-based, formal assurances can be made about the multi-agent behavior.
Analysis and Implementation of Distributed algorithms for multi-robot Systems
, 2008
"... Distributed algorithms for multi-robot systems rely on network communications to share information. However, the motion of the robots changes the network topology, which affects the information presented to the algorithm. For an algorithm to produce accurate output, robots need to communicate rapidl ..."
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Cited by 11 (3 self)
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Distributed algorithms for multi-robot systems rely on network communications to share information. However, the motion of the robots changes the network topology, which affects the information presented to the algorithm. For an algorithm to produce accurate output, robots need to communicate rapidly enough to keep the network topology correlated to their physical configuration. Infrequent communications will cause most multirobot distributed algorithms to produce less accurate results, and cause some algorithms to stop working altogether. The central theme of this work is that algorithm accuracy, communications bandwidth, and physical robot speed are related. This thesis has three main contributions: First, I develop a prototypical multi-robot application and computational model, propose a set of complexity metrics to evaluate distributed algorithm performance on multi-robot systems, and introduce the idea of the robot speed ratio, a dimensionless measure of robot speed relative to message speed in networks that rely on multi-hop communication. The robot speed ratio captures key relationships
