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 gas expansion 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.

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

Abstract. The ability of robots to quickly and accurately localize their neighbors is extremely important in swarm robotics. Prior approaches generally rely either on global information provided by GPS, beacons, and landmarks, or complex local information provided by vision systems. In this paper we provide a new technique, based on trilateration. This system is fully distributed, inexpensive, scalable, and robust. In addition, the system provides a unified framework that merges localization with information exchange between robots. The usefulness of this framework is illustrated on a number of applications. 1 Goal of Our Work Our goal is to create a new “enabling technology ” for swarm robotics. Since the concept of “emergent behavior ” arises from the local interaction of robots with their nearby neighbors, it is often crucial that robots know the location of those neighbors. Because we do not want to impose a global coordinate system on the swarm, this means that each robot must have its own local coordinate system,

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

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This paper presents a novel chemical plume tracing algorithm executed by a distributed network of mobile sensing robots that measure the ambient fluid velocity and chemical concentration. The algorithm drives the robotic network to the source of the toxic plume, where measures can be taken to remove or extinguish the source emitter.

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.

Traditional approaches to designing multi-agent systems are offline, in simula-tion, and assume the presence of a global observer. Artificial Physics (AP) or physicomimetics (Spears and Gordon 1999) can be used to self-organize swarms of mobile robots into formations that move towards a goal. Using an offline ap-proach, we extend the AP framework to moving formations through obstacle fields. We provide important metrics of performance that allow us to (a) compare the utility of different generalized force laws in the artificial physics framework, (b) examine trade-offs between different metrics, and (c) provide a detailed method of comparison for future researchers in this area. In the online, real world, a global observer may be absent, performance feedback may be delayed or perturbed by noise, agents may only interact with their local neighbors, and only a subset of agents may experience any form of performance feed-back. Under these constraints, designing multi-agent systems is difficult. We present a novel approach called“Distributed Agent Evolution with Dynamic Adaptation to Local Unexpected Scenarios ” or DAEDALUS to address these issues, by mimicking

Abstract. The ability of robots to quickly and accurately localize their neighbors is extremely important for robotic teams. Prior approaches typically rely either on global information provided by GPS, beacons and landmarks, or on complex local information provided by vision systems. In this paper we describe our trilateration approach to multi-robot localization, which is fully distributed, inexpensive, and scalable [15]. Our prior research [14] focused on maintaining multi-robot formations indoors using trilateration. This paper pushes the limits of our trilateration technology by testing formations of robots in an outdoor setting at larger inter-robot distances and higher speeds. 1

Object-oriented programming has worked quite well – so far. What are the objects, how do they relate to each other? Once we clarified these questions we typically feel confident to design and implement even the most complex systems. However, objects can deceive us. They can lure us into a false sense of understanding. The metaphor of objects can go too far by making us try to create objects that are too much inspired by the real world. This is a serious problem, as a resulting system may be significantly more complex than it would have to be, or worse, will not work at all. We postulate the notion of an antiobject as a kind of object that appears to essentially do the opposite of what we generally think the object should be doing. As a Gedankenexperiment antiobjects allow us to literally think outside the proverbial box or, in this case outside the object. This article discusses two examples, a Pacman game and a soccer simulation where antiobjects are employed as part of a game AI called Collaborative Diffusion. In Collaborative-Diffusion based soccer the player and grass tile agents are antiobjects. Counter to the intuition of most programmers the grass tile agents, on top of which all the players are moving, are doing the vast majority of the computation, while the soccer player agents are doing almost no computation. This article illustrates that this role reversal is not only a different way to look at objects but, for instance, in the case with Collaborative Diffusion, is simple to implement, incremental in nature and more robust than traditional approaches.