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Optimized stochastic policies for task allocation in swarms of robots
- IEEE Transactions on Robotics
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Cited by 32 (5 self)
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endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. This paper is posted at ScholarlyCommons.
Towards Energy Optimization: Emergent Task Allocation in a Swarm of Foraging Robots
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Get in touch: cooperative decision making based on robot-to-robot collisions
- AUTON AGENT MULTI-AGENT SYST
"... 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 ..."
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Cited by 16 (13 self)
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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.
G.: Self-organized aggregation triggers collective decision making in a group of cockroach-like robots
- Adaptive Behavior
, 2009
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The online version of this article can be found at:
Task partitioning in swarms of robots: reducing performance losses due to interference at shared resources
- Informatics in control, automation and robotics
, 2009
"... Abstract. The performance of large groups of robots is often limited by a com-monly shared resource. This effect, termed interference, can have a large impact on robotic swarms. This article studies the issue of interference in a swarm of robots working on a harvesting task. The environment of the r ..."
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Abstract. The performance of large groups of robots is often limited by a com-monly shared resource. This effect, termed interference, can have a large impact on robotic swarms. This article studies the issue of interference in a swarm of robots working on a harvesting task. The environment of the robots is spatially constrained, i.e., there is a commonly shared resource, the nest, which limits the group’s performance when used without any arbitration mechanism. The arti-cle studies the use of task partitioning for reducing concurrent accesses to the resource, and therefore limiting the impact of interference on the group’s per-formance. In our study, we spatially partition the environment into two subparts, thereby partitioning the corresponding harvesting task as well. We employ a sim-ple method to allocate individuals to the partitions. The approach is empirically studied both in an environment with a narrow nest area and an environment with-out this constraint. The results of the task partitioning strategy are analyzed and compared to the case in which task partitioning is not employed. 1
A Bio-Inspired Architecture for Division of Labour in SANETs
"... Abstract. Division of labour is one of the possible strategies to efficiently exploit the resources of autonomous systems. It is also a phenomenon often observed in animal systems. We show an architecture that implements division of labour in Sensor/Actuator Networks. The way the nodes take their de ..."
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Abstract. Division of labour is one of the possible strategies to efficiently exploit the resources of autonomous systems. It is also a phenomenon often observed in animal systems. We show an architecture that implements division of labour in Sensor/Actuator Networks. The way the nodes take their decisions is inspired by ants ’ foraging behaviour. The preliminary results show that the architecture and the bio-inspired mechanism successfully induce self-organised division of labour in the network. The experiments were run in simulation. We developed a new type of simulator for this purpose. Key features of our work are crosslayer design and exploitation of inter-node interactions. No explicit negotiation between the agents takes place. 1
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.
INTERFERENCE REDUCTION THROUGH TASK PARTITIONING IN A ROBOTIC SWARM or: “Don’t you step on my blue suede shoes!”
"... Interference reduction through task ..."
Theoretical Study of Ant-based Algorithms for Multi-Agent Patrolling
"... Abstract. This paper addresses the multi-agent patrolling problem, which consists for a set of autonomous agents to visit all the places of an unknown environment as regularly as possible. The proposed approach is based on the ant paradigm. Each agent can only mark and move according to its local pe ..."
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Cited by 9 (5 self)
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Abstract. This paper addresses the multi-agent patrolling problem, which consists for a set of autonomous agents to visit all the places of an unknown environment as regularly as possible. The proposed approach is based on the ant paradigm. Each agent can only mark and move according to its local perception of the environment. We study EVAW, a pheromone-based variant of the EVAP [3] and VAW [12]. The main novelty of the paper is the proof of some emergent spatial properties of the proposed algorithm. In particular we show that obtained cycles are necessarily of same length, which ensures an efficient spatial distribution of the agents. We also report some experimental results and discuss open questions concerning the proposed algorithm. 1
Evolution of Hierarchical Controllers for Multirobot Systems
"... Decentralized control for multirobot systems is difficult to design by hand because the behavioral rules for individual robots cannot, in general, be derived from a desired collective behavior. System designers have therefore resorted to evo-lutionary computation as a means to heuristically synthesi ..."
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Cited by 4 (2 self)
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Decentralized control for multirobot systems is difficult to design by hand because the behavioral rules for individual robots cannot, in general, be derived from a desired collective behavior. System designers have therefore resorted to evo-lutionary computation as a means to heuristically synthesize self-organized behaviors for robot collectives. Evolutionary computation is typically applied by putting the rules gov-erning the individual robots under evolutionary control and by assigning fitness scores based on collective performance. Scaling evolutionary approaches to complex tasks has, how-ever, proven challenging due to issues related to bootstrap-ping and premature convergence. In this paper, we show how hierarchical task decomposition and the combination of evolved and preprogrammed control can overcome these is-sues. We apply our approach to a complex multirobot task that requires a high degree of coordination and collective de-cision making, and we synthesize controllers capable of solv-ing the task.
