Anumber of recent studies concern algorithms for distributed control and coordination in systems of autonomous mobile robots. The common theoretical model adopted in these studies assumes that the positional input of the robots is obtained by perfectly accurate visual sensors, that robot movements are accurate, and that internal calculations performed by the robots on (real) coordinates are perfectly accurate as well. The current paper concentrates on the e ect of weakening this rather strong set of assumptions, and replacing it with the more realistic assumption that the robot sensors, movement and internal calculations may have slight inaccuracies. Speci cally, the paper concentrates on the ability of robot systems with inaccurate sensors, movements and calculations to carry out the task of convergence. The paper presents several impossibility theorems, limiting the inaccuracy allowing convergence, and prohibiting a general algorithm for gathering, namely, meeting at a point, in a nite number of steps. The main positive result is an algorithm for convergence under bounded measurement, movement and calculation errors.

Over the past few years, the focus of robotic design has been moving from a scenario where a few specialized (and expensive) units were used to solve a variety of tasks, to a scenario where many general purpose (and cheap) units are used to achieve some common goal. Consequently, part of the focus has been to understand better how to coordinate and control a set of such “simpler ” mobile units efficiently. Studies can be found in different disciplines, from engineering to artificial life: a shared feature of the majority of these works has been the design of algorithms based on heuristics, with no main concern on their correctness and termination. Few researchers have focused on trying to model formally an environment constituted by mobile units, analyzing which kind of capabilities they must have in order to achieve their goals; in other words, to study the problem from a computational point of view. In this paper we do a direct comparison between two models, ATOM and CORDA, introduced in two studies leading in this direction. First their main features are described, and then the main differences are highlighted, showing the relationship between the class of problems solvable in the two models.

We demonstrate the ability of a swarm of autonomous micro-robots to perform collective decision making in a dynamic environment. This decision making is an emergent property of decentralized self-organization, which results from executing a very simple bio-inspired algorithm. This algorithm allows the robotic swarm to choose from several distinct light sources in the environment and to aggregate in the area with the highest illuminance. Interestingly, these decisions are formed by the collective, although no information is exchanged by the robots. The only communicative act is the detection of robot-to-robot encounters. We studied the performance of the robotic swarm under four environmental conditions and investigated the dynamics of the aggregation behaviour as well as the flexibility and the robustness of the solutions. In summary, we can report that the tested robotic swarm showed two main characteristic features of swarm systems: it behaved flexible and the achieved solutions were very robust. This was achieved with limited individual sensor abilities and with low computational effort on each single robot in the swarm.

From an engineering point of view, the problem of coordinating a set of autonomous, mobile robots for the purpose of cooperatively performing a task has been studied extensively over the past decade. In contrast, in this paper we aim at an understanding of the fundamental algorithmic limitations on what a set of autonomous mobile robots can or cannot achieve. We therefore study a hard task for a set of weak robots. The task is for the robots in the plane to form any arbitrary pattern that is given in advance. This task is fundamental in the sense that if the robots can form any pattern, they can agree on their respective roles in a subsequent, coordinated action. The robots are weak in several aspects. They are anonymous; they cannot explicitly communicate with each other, but only observe the positions of the others; they cannot remember the past; they operate in a very strong form of asynchronicity. We show that the tasks that such a system of robots can perform depend strongly on their common agreement about their environment, i.e., the readings of their environment sensors. If the robots have no common agreement about their environment, they cannot form an arbitrary pattern. If each robot has a compass needle that indicates North (the robot world is a flat surface, and compass needles are parallel), then any odd number of robots can form an arbitrary pattern, but an even number cannot (in the worst case). If each robot has two independent compass needles, say North and East, then any set of robots can form any pattern.

Summary. The distributed coordination and control of a team of autonomous mobile robots is a problem widely studied in a variety of fields, such as engineering, artificial intelligence, artificial life, robotics. Generally, in these areas, the problem is studied mostly from an empirical point of view. Recently, a significant research effort has been and continues to be spent on understanding the fundamental algorithmic limitations on what a set of autonomous mobile robots can achieve. In particular, the focus is to identify the minimal robot capabilities (sensorial, motorial, computational) that allow a problem to be solvable and a task to be performed. In this paper we describe the current investigations on the interplay between robots capabilities, computability, and algorithmic solutions of coordination problems by autonomous mobile robots. 1

We consider an automated agent that needs to coordinate with a human partner when communication between them is not possible or is undesirable (tactic coordination games). Specifically, we examine situations where an agent and human attempt to coordinate their choices among several alternatives with equivalent utilities. We use machine learning algorithms to help the agent predict human choices in these tactic coordination domains. Learning to classify general human choices, however, is very difficult. Nevertheless, humans are often able to coordinate with one another in communication-free games, by using focal points, “prominent ” solutions to coordination problems. We integrate focal points into the machine learning process, by transforming raw domain data into a new hypothesis space. This results in classifiers with an improved classification rate and shorter training time. Integration of focal points into learning algorithms also results in agents that are more robust to changes in the environment. 1

It is generally accepted that an agent needs to build models of other agents in its environment. The content of these models ranges from simple entries, such as agent capabilities, to more complex entries, such as agent intentions, goals, desires, etc. There is the problem that the information stored in these models may not be accurate in terms of matching the actual property of the agent being modelled. When this happens the agent storing the models is said to be deluded about the agent being modelled. This paper discusses our synthesis of ideas on the issue of agent delusion and presents results of some of the work we have carried out in trying to overcome delusion within agent models and in preventing the spread of delusions to other agents ’ models when agents communicate/gossip with each other.

In this paper we present a self-stabilizing algorithm for the intruder problem. The problem can be formulated as follows: an enemy unit, or intruder, is trying to sneak through a field patrolled by an arbitrary number of friendly autonomous (i.e. robotic) units. These units must reach the intruder and block it by surrounding it.

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This paper studies self-organized flocking in a swarm of mobile robots. We present Kobot, a mobile robot platform developed specifically for swarm robotic studies, briefly describing its sensing and communication abilities. In particular, we describe a scalable method that allows the robots to sense the orientations of their neighbors using a digital compass and wireless communication. Then we propose a behavior for a swarm of robots that creates self-organized flocking by using heading alignment and proximal control. The flocking behavior is observed to operate in three phases: alignment, advance, and avoidance. We evaluate four variants of this behavior by setting its parameters to extreme values and analyze the performance of flocking using a number of metrics, such as order and entropy. Our results show that, the flocking behavior obtained under appropriate parameter values, is quite robust and generates successful selforganized flocking in constraint environments.