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51
Collaborative exploration of unknown environments with teams of mobile robots
- In dagstuhl
, 2002
"... Abstract. In this paper we consider the problem of exploring an unknown environment by a team of robots. As in single-robot exploration the goal is to minimize the overall exploration time. The key problem to be solved in the context of multiple robots is to choose appropriate target points for the ..."
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Cited by 27 (4 self)
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Abstract. In this paper we consider the problem of exploring an unknown environment by a team of robots. As in single-robot exploration the goal is to minimize the overall exploration time. The key problem to be solved in the context of multiple robots is to choose appropriate target points for the individual robots so that they simultaneously explore different regions of the environment. We present an approach for the coordination of multiple robots which, in contrast to previous approaches, simultaneously takes into account the cost of reaching a target point and its utility. The utility of a target point is given by the size of the unexplored area that a robot can cover with its sensors upon reaching that location. Whenever a target point is assigned to a specific robot, the utility of the unexplored area visible from this target position is reduced for the other robots. This way, a team of multiple robots assigns different target points to the individual robots. The technique has been implemented and tested extensively in real-world experiments and simulation runs. The results given in this paper demonstrate that our coordination technique significantly reduces the exploration time compared to previous approaches. 1
Algorithms for building annular structures with minimalist robots inspired by brood sorting in ant colonies. Autonomous Robots
, 2004
"... This study shows that a task as complicated as multi-object ‘ant-like annular sorting ’ can be accomplished with ‘minimalist ’ solutions employing simple mechanisms and minimal hardware. It provides an alternative to ‘patch sorting ’ for multi-object sorting. Three different mechanisms, based on hyp ..."
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Cited by 24 (1 self)
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This study shows that a task as complicated as multi-object ‘ant-like annular sorting ’ can be accomplished with ‘minimalist ’ solutions employing simple mechanisms and minimal hardware. It provides an alternative to ‘patch sorting ’ for multi-object sorting. Three different mechanisms, based on hypotheses about the behaviour of Leptothorax ants are investigated and comparisons are made. Mechanism I employs a simple clustering algorithm, with objects of different sizes. The mechanism explores the idea that it is the size difference of the object that promotes segregation. Mechanism II is an extension to our earlier two-object segregation mechanism. We test the ability of this mechanism to segregate an increased number of object types. Mechanism III uses a combined leaky integrator, which allows a greater segregation of object types while retaining the compactness of the structure. Its performance is improved by optimizing the mechanism’s parameters using a genetic algorithm. We compare the three mechanisms in terms of sorting performance. Comparisons between the results of these sorting mechanisms and the behaviour of ants should facilitate further insights into both biological and robotic research and make a contribution to the further development of swarm robotics. Index terms – collective behaviour transition, genetic algorithm, ant, sorting, swarm intelligence, swarm robotics 1
Task Allocation via Self-Organizing Swarm Coalitions in Distributed Mobile Sensor Network
- In: Proceedings of the American Association of Artificial Intelligence
, 2004
"... This paper presents a task allocation scheme via self-organizing swarm coalitions for distributed mobile sensor network coverage. Our approach uses the concepts of ant behavior to self-regulate the regional distributions of sensors in proportion to that of the moving targets to be tracked in a non-s ..."
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Cited by 22 (5 self)
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This paper presents a task allocation scheme via self-organizing swarm coalitions for distributed mobile sensor network coverage. Our approach uses the concepts of ant behavior to self-regulate the regional distributions of sensors in proportion to that of the moving targets to be tracked in a non-stationary environment. As a result, the adverse effects of task interference between robots are minimized and sensor network coverage is improved. Quantitative comparisons with other tracking strategies such as static sensor placement, potential fields, and auction-based negotiation show that our approach can provide better coverage and greater flexibility to respond to environmental changes.
Multi-Agent Role Allocation: Issues, Approaches, and Multiple Perspectives
- AUTON AGENT MULTI-AGENT SYST
"... In cooperative multi-agent systems, roles are used as a design concept when creating large systems, they are known to facilitate specialization of agents, and they can help to reduce interference in multi-robot domains. The types of tasks that the agents are asked to solve and the communicative capa ..."
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Cited by 11 (0 self)
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In cooperative multi-agent systems, roles are used as a design concept when creating large systems, they are known to facilitate specialization of agents, and they can help to reduce interference in multi-robot domains. The types of tasks that the agents are asked to solve and the communicative capabilities of the agents significantly affect the way roles are used in cooperative multi-agent systems. Along with a discussion of these issues about roles in multi-agent systems, this article compares computational models of the role allocation problem, presents the notion of explicitly versus implicitly defined roles, gives a survey of the methods used to approach role allocation problems, and concludes with a list of open research questions related to roles in multi-agent systems.
A Generalised Approach to Position Selection for Simulated Soccer Agents
- Proc. of the 1st European RoboCup Workshop, 28 May-2
, 2001
"... Position selection is a key task that must be carried out by a soccer-playing agent, but is often overlooked in favour of the more active tasks such as ball control. This paper examines the position selection implemented by the Essex Wizards team in the RoboCup Simulator league in recent competition ..."
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Cited by 9 (6 self)
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Position selection is a key task that must be carried out by a soccer-playing agent, but is often overlooked in favour of the more active tasks such as ball control. This paper examines the position selection implemented by the Essex Wizards team in the RoboCup Simulator league in recent competitions. The initial approach using task specific behaviours is firstly reviewed. The new approach is then addressed based on modular decomposition for flexibility. Implementation results are given to show the applicability.
Adaptive Mobile Charging Stations for Multi-Robot Systems
"... We consider systems of mobile robots that execute a transportation task and periodically recharge from a docking station. The location of the docking station has a considerable effect on task performance. In nonstationary tasks the optimal dock location may vary over the length of the task. In multi ..."
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Cited by 8 (2 self)
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We consider systems of mobile robots that execute a transportation task and periodically recharge from a docking station. The location of the docking station has a considerable effect on task performance. In nonstationary tasks the optimal dock location may vary over the length of the task. In multiple-robot systems, spatial interference between charging and working robots can make it difficult to find an optimal dock location, even in static tasks. We propose a new approach whereby the dock is itself an autonomous robot that attempts to incrementally improve its location. We show simulation results from a simple local controller that adapts to nonstationary tasks and spatial interference, and thus improves overall task performance compared to a static dock.
Efficient Exploration of Unknown Indoor Environments using a Team of Mobile Robots
"... Whenever multiple robots have to solve a common task, they need to coordinate their actions to carry out the task efficiently and to avoid interferences between individual robots. This is especially the case when considering the problem of exploring an unknown environment with a team of mobile robot ..."
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Cited by 6 (2 self)
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Whenever multiple robots have to solve a common task, they need to coordinate their actions to carry out the task efficiently and to avoid interferences between individual robots. This is especially the case when considering the problem of exploring an unknown environment with a team of mobile robots. To achieve efficient terrain coverage with the sensors of the robots, one first needs to identify unknown areas in the environment. Second, one has to assign target locations to the individual robots so that they gather new and relevant information about the environment with their sensors. This assignment should lead to a distribution of the robots over the environment in a way that they avoid redundant work and do not interfere with each other by, for example, blocking their paths. In this paper, we address the problem of efficiently coordinating a large team of mobile robots. To better distribute the robots over the environment and to avoid redundant work, we take into account the type of place a potential target is located in (e.g., a corridor or a room). This knowledge allows us to improve the distribution of robots over the environment compared to approaches lacking this capability. To autonomously determine the type of a place, we apply a classifier learned
Real-time motion planning of multiple mobile manipulators with a common task objective in shared work environments
- In IEEE International Conference on Robotics and Automation
, 2007
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Expert Assessment of Stigmergy: A Report for the Department of National Defence
"... This report describes the current state of research in the area known as Swarm Intelligence. Swarm Intelligence relies upon stigmergic principles in order to solve complex problems using only simple agents. Swarm Intelligence has been receiving increasing attention over the last 10 years as a resul ..."
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Cited by 4 (0 self)
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This report describes the current state of research in the area known as Swarm Intelligence. Swarm Intelligence relies upon stigmergic principles in order to solve complex problems using only simple agents. Swarm Intelligence has been receiving increasing attention over the last 10 years as a result of the acknowledgement of the success of social insect systems in solving complex problems without the need for central control or global information. In swarmbased problem solving, a solution emerges as a result of the collective action of the members of the swarm, often using principles of communication known as stigmergy. The individual behaviours of swarm members do not indicate the nature of the emergent collective behaviour and the solution process is generally very robust to the loss of individual swarm members. This report describes the general principles for swarm-based problem solving, the way in which stigmergy is employed, and presents a number of high level algorithms that have proven utility in solving hard optimization and control problems. Useful tools for the modelling and investigation of swarm-based systems are then briefly described. Applications in the areas of combinatorial optimization, distributed manufacturing, collective robotics, and routing in networks (including mobile ad hoc networks) are then reviewed. Military and security applications are then described, specifically highlighting the groups that have been or continue to be active in swarm research. The final section of the document identifies areas of future research of potential military interest. A substantial bibliography is provided in
Cooperative search algorithm for distributed autonomous robots
- in Proceedings of IEEE International Conference on Intelligent Robotics and Systems
, 2004
"... Abstract—This paper presents a cooperative random search algorithm for distributed independent autonomous robots. Our focus is to develop a distributed algorithm for a team of simple robots searching for targets in an unknown environment. The search algorithm consists of five simple behavioral rules ..."
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Cited by 3 (0 self)
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Abstract—This paper presents a cooperative random search algorithm for distributed independent autonomous robots. Our focus is to develop a distributed algorithm for a team of simple robots searching for targets in an unknown environment. The search algorithm consists of five simple behavioral rules for each robot. It is implemented in both simulation and physical robots. The results we obtained demonstrated that the rule set is effective and robust to dynamic changes in the environment layout. Keywords-cooperation; distributed control; multi-robot systems; search; swarm I. II.
