Decentralized cooperative auction for multiple agent task allocation using synchronized random number generators

Palmer, William D.; Kirschenbaum, Marc; Murton, J.; Zając, Katarzyna; Kovacina, Michael A.; Vaidyanathan, Ravi

doi:10.1109/iros.2003.1248933

Cited by 20 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is a complex goal, since the robots operate in a dynamic environment and the perceptions can be noisy. In order to deal with the coordination problem in a real scenario, we adopted a solution based on a greedy algorithm [12,15] and the Prey-Predator game formalization (see Sect. 3).…”

Section: Dynamic Task Assignmentmentioning

confidence: 99%

Multi-robot Surveillance Through a Distributed Sensor Network

Pennisi

Previtali

Gennari

et al. 2015

Cooperative Robots and Sensor Networks 2015

View full text Add to dashboard Cite

Automatic surveillance of public areas, such as airports, train stations, and shopping malls, requires the capacity of detecting and recognizing possible abnormal situations in populated environments. In this book chapter, an architecture for intelligent surveillance in indoor public spaces, based on an integration of interactive and non-interactive heterogeneous sensors, is described. As a difference with respect to traditional, passive and pure vision-based systems, the proposed approach relies on a distributed sensor network combining RFID tags, multiple mobile robots, and fixed RGBD cameras. The presence and the position of people in the scene is detected by suitably combining data coming from the sensor nodes, including those mounted on board of the mobile robots that are in charge of patrolling the environment. The robots can adapt their behavior according to the current situation, on the basis of a Prey-Predator scheme, and can coordinate their actions to fulfill the required tasks. Experimental results have been carried out both on real and on simulated data to show the effectiveness of the proposed approach.

show abstract

Section: Dynamic Task Assignmentmentioning

confidence: 99%

Multi-robot Surveillance Through a Distributed Sensor Network

Pennisi

Previtali

Gennari

et al. 2015

Cooperative Robots and Sensor Networks 2015

View full text Add to dashboard Cite

show abstract

“…The capabilities for this task are C t = {c 2 , c 4 , c 6 , c 8 }. From (1), the agent sets that respectively has the capability c 2 , c 4 , c 6 , c 8 a 2 , a 4 , a 6 , a 9 , a 10 , a 11 …”

Section: Case Studymentioning

confidence: 99%

Task Allocation Adaptable to Network Topology among Cooperative

Xiao

Zhang

2007

7th IEEE International Conference on Computer and Information Technology (CIT 2007)

View full text Add to dashboard Cite

In multi-agent system, the underlying networks are dynamic and the network topologies may change in operation; individual agents are not identical and their capability levels are not equal. The cooperation of agents should adapt to the dynamics and heterogeneity in order to improve the performance and efficiency of the overall system. In this paper, we present two means based on brute-force search and heuristic search respectively to achieve an efficient task allocation adaptable to current network topology among cooperative heterogeneous agents. These two algorithms are validated in the following case studies and performance analysis.

show abstract

“…The main goal of task allocation is maximizing the overall performance of the system and completing the task as soon as possible [5]. There are some existing studies on agent task allocation, divided into centrally controlled and distributed allocation [6]. A well-known example of distributed task allocation is the Contract Net Protocol [7].…”

Section: Introductionmentioning

confidence: 99%

Ant Colony Optimization for task allocation in Multi-Agent Systems

Wang

et al. 2013

China Commun.

View full text Add to dashboard Cite

Task allocation is a key issue of agent cooperation mechanism in Multi-Agent Systems. The important features of an agent system such as the latency of the network infrastructure, dynamic topology, and node heterogeneity impose new challenges on the task allocation in Multi-Agent environments. Based on the traditional parallel computing task allocation method and Ant Colony Optimization (ACO), a novel task allocation method named Collection Path Ant Colony Optimization (CPACO) is proposed to achieve global optimization and reduce processing time. The existing problems of ACO are analyzed; CPACO overcomes such problems by modifying the heuristic function and the update strategy in the Ant-Cycle Model and establishing a threedimensional path pheromone storage space. The experimental results show that CPACO consumed only 10.3% of the time taken by the Global Search Algorithm and exhibited better performance than the Forward Optimal Heuristic Algorithm. Based on the traditional parallel computing task allocation method and Ant Colony Optimization, Collection Path Ant Colony Optimization is proposed to achieve global optimization and reduce processing time. CPACO overcomes the existing problems of ACO by modifying the heuristic function and the update strategy in the Ant-Cycle Model and establishing a threedimensional path pheromone storage space.

show abstract

Decentralized cooperative auction for multiple agent task allocation using synchronized random number generators

Cited by 20 publications

References 5 publications

Multi-robot Surveillance Through a Distributed Sensor Network

Multi-robot Surveillance Through a Distributed Sensor Network

Task Allocation Adaptable to Network Topology among Cooperative

Ant Colony Optimization for task allocation in Multi-Agent Systems

Contact Info

Product

Resources

About