Auction and Swarm Multi-Robot Task Allocation Algorithms in Real Time Scenarios

Oliver, Gabriel; Guerrero, José

doi:10.5772/13281

Cited by 10 publications

(8 citation statements)

References 22 publications

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“…Consensus steps use additional computations, which can lead robots to take a long time to converge to a coherent allocation -i.e., slowing the convergence of these approaches [2]. Auction-based algorithms [5,29,42,53] are typically distributed methods that have been adopted to solve MRTA problems. These algorithms are efficient and produce suboptimal solutions [19].…”

Section: Related Workmentioning

confidence: 99%

Towards a Distributed Solution to the Multi-Robot Task Allocation Problem with Energetic and Spatiotemporal Constraints

Zitouni

Harous²,

Maamri

2019

csci

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The multi-robot task allocation problem consists of two distinct sets: a set of tasks (requiring resources) and a set of robots (offering resources); then tasks are allocated to robots; while optimizing a certain objective function, subject to some constraints: e.g. allocate the maximum number of tasks, minimize the distances travelled by robots, etc. In this paper, we propose some objective functions, and our main contribution is the introduction of energetic constraints on multi-robot task allocation problems. In addition, we propose an allocation method (based on parallel distributed guided genetic algorithms) and compare it to two state-of-the-art algorithms. Performed simulations and obtained results show the effectiveness and scalability of our solution, even with a large number of robots and tasks.

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Section: Related Workmentioning

confidence: 99%

Towards a Distributed Solution to the Multi-Robot Task Allocation Problem with Energetic and Spatiotemporal Constraints

Zitouni

Harous²,

Maamri

2019

csci

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“…Auction-based algorithms (see [26]- [28]), which are a subset of market-based methods, have also been applied to ST-SR-TA task-allocation problems. In these algorithms, each agent bids on a task based on information from its own SA, and the highest bidder wins the task assignment.…”

Section: Literature Reviewmentioning

confidence: 99%

Reliable, Distributed Scheduling and Rescheduling for Time-Critical, Multiagent Systems

Whitbrook

Meng

Chung

2018

IEEE Trans. Automat. Sci. Eng.

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This paper addresses two main problems with many heuristic task allocation approaches-solution trapping in local minima and static structure. The existing distributed task allocation algorithm known as performance impact (PI) is used as the vehicle for developing solutions to these problems as it has been shown to outperform the state-of-the-art consensusbased bundle algorithm for time-critical problems with tight deadlines, but is both static and suboptimal with a tendency toward trapping in local minima. This paper describes two additional modules that are easily integrated with PI. The first extends the algorithm to permit dynamic online rescheduling in real time, and the second boosts performance by introducing an additional soft-max action-selection procedure that increases the algorithm's exploratory properties. This paper demonstrates the effectiveness of the dynamic rescheduling module and shows that the average time taken to perform tasks can be reduced by up to 9% when the soft-max module is used. In addition, the solution of some problems that baseline PI cannot handle is enabled by the second module. These developments represent a significant advance in the state of the art for multiagent, time-critical task assignment. Note to Practitioners-This work was motivated by the limitations of current agent-to-task allocation algorithms that do not use a central server for communication. In previously published work, the current state-of-the-art consensus-based bundle algorithm has demonstrated poor performance when applied to model task allocation problems with critical time limits, often failing to assign all of the tasks, especially when the deadlines are tight. The performance impact (PI) algorithm has a much better success rate with these model problems but would be flawed when applied to real missions because it has no mechanism for online replanning when new information becomes available. In addition, it is somewhat restricted in the way it searches for a problem solution, meaning that more efficient plans are often available but are not discovered. This paper tackles both of these shortcomings. The PI algorithm is extended to include a module that permits rescheduling when necessary, and a further module is introduced that widens the scope of the solution search. A third module that is able to offer robust plans, even for large-scaled missions involving many agents and tasks, has also been developed, although it is not discussed here. Implementation

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“…Popular heuristic methods include Tabu-search [18], genetic algorithms [19], and auction-based techniques [20]. In general, heuristic systems are less complex and demonstrate relatively fast execution times, although the trade-off is that they often provide sub-optimal solutions.…”

Section: Summary Of Approachesmentioning

confidence: 99%

Addressing robustness in time-critical, distributed, task allocation algorithms

Whitbrook

Meng²,

Chung³

2018

Appl Intell

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The aim of this work is to produce and test a robustness module (ROB-M) that can be generally applied to distributed, multi-agent task allocation algorithms, as robust versions of these are scarce and not well-documented in the literature. ROB-M is developed using the Performance Impact (PI) algorithm, as this has previously shown good results in deterministic trials. Different candidate versions of the module are thus bolted on to the PI algorithm and tested using two different task allocation problems under simulated uncertain conditions, and results are compared with baseline PI. It is shown that the baseline does not handle uncertainty well; the task-allocation success rate tends to decrease linearly as degree of uncertainty increases. However, when PI is run with one of the candidate robustness modules, the failure rate becomes very low for both problems, even under high simulated uncertainty, and so its architecture is adopted for ROB-M and also applied to MIT's baseline Consensus Based Bundle Algorithm (CBBA) to demonstrate its flexibility. Strong evidence is provided to show that ROB-M can work effectively with CBBA to improve performance under simulated uncertain conditions, as long as the deterministic versions of the problems can be solved with baseline CBBA. Furthermore, the use of ROB-M does not appear to increase mean task completion time in either algorithm, and only 100 Monte Carlo samples are required compared to 10,000 in MIT's robust version of the CBBA algorithm. PI with ROB-M is also tested directly against MIT's robust algorithm and demonstrates clear superiority in terms of mean numbers of solved tasks.

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Auction and Swarm Multi-Robot Task Allocation Algorithms in Real Time Scenarios

Cited by 10 publications

References 22 publications

Towards a Distributed Solution to the Multi-Robot Task Allocation Problem with Energetic and Spatiotemporal Constraints

Towards a Distributed Solution to the Multi-Robot Task Allocation Problem with Energetic and Spatiotemporal Constraints

Reliable, Distributed Scheduling and Rescheduling for Time-Critical, Multiagent Systems

Addressing robustness in time-critical, distributed, task allocation algorithms

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