Market-Based Framework for Mobile Surveillance Systems

Studies in Computational Intelligence

Elmogy

2015

333

193

Multi-robot systems (MRS) are a group of robots that are designed aiming to perform some collective behavior. By this collective behavior, some goals that are impossible for a single robot to achieve become feasible and attainable. There are several foreseen benefits of MRS compared to single robot systems such as the increased ability to resolve task complexity, increasing performance, reliability and simplicity in design. These benefits have attracted many researchers from academia and industry to investigate how to design and develop robust versatile MRS by solving a number of challenging problems such as complex task allocation, group formation, cooperative object detection and tracking, communication relaying and self-organization to name just a few. One of the most challenging problems of MRS is how to optimally assign a set of robots to a set of tasks in such a way that optimizes the overall system performance subject to a set of constraints. This problem is known as Multi-robot Task Allocation (MRTA) problem. MRTA is a complex problem especially when it comes to heterogeneous unreliable robots equipped with different capabilities that are required to perform various tasks with different requirements and constraints in an optimal way. This chapter provides a comprehensive review on challenging aspects of MRTA problem, recent approaches to tackle this problem and the future directions.

Section: Decentralized Approachesmentioning

confidence: 98%

Multi-robot Task Allocation: A Review of the State-of-the-Art

Studies in Computational Intelligence

Elmogy

2015

333

193

“…Auctions have mainly two general categories: simple auctions and combinatorial auctions. MRTA problem are solved using both categories to reach an optimal allocation and results were acceptable [33][34][35]. Depending on the how the winner determination strategy is implemented, market-based approach can be centralized or decentralized.…”

Section: Market-based Approachmentioning

confidence: 99%

A Comparative Study between Optimization and Market-Based Approaches to Multi-Robot Task Allocation

Badrel-din

Advances in Artificial Intelligence

2013

This paper presents a comparative study between optimization-based and market-based approaches used for solving the Multirobot task allocation (MRTA) problem that arises in the context of multirobot systems (MRS). The two proposed approaches are used to find the optimal allocation of a number of heterogeneous robots to a number of heterogeneous tasks. The two approaches were extensively tested over a number of test scenarios in order to test their capability of handling complex heavily constrained MRS applications that include extended number of tasks and robots. Finally, a comparative study is implemented between the two approaches and the results show that the optimization-based approach outperforms the market-based approach in terms of optimal allocation and computational time.

“…Auctions have mainly two general categories; simple auctions and combinatorial auctions. MRTA problem are solved using both categories to reach an optimal allocation and results were acceptable [14] [15] [16]. Depending on the how the winner determi nation strategy is implemented, market-based approach can be centralized or decentralized.…”

Section: B Objective Functionmentioning

confidence: 99%

Market-based approach to Multi-robot Task Allocation

2013 International Conference on Individual and Collective Behaviors in Robotics (ICBR)

2013

This paper presents a market-based approach used for solving the Multi-robot Task Allocation (MRTA) problem that arises in the context of Multi-robot Systems (MRS). The proposed approach is used to find the best allocation of a number of heterogeneous robots to a number of heterogeneous tasks. The approach was extensively tested over a number of test scenarios in order to test its capability of handling complex constrained MRS applications that included extended number of tasks and robots. Finally a comparative study is implemented between the pro posed market-based approach and two optimization-based approaches, the results show that the optimization-based approaches outperformed the market-based approach in terms of best allocation and computational time, however, in terms of capabilities matching the difference between both algorithms is very minimal.