A heuristics based multi-robot task allocation

Khuntia, Arpita; Choudhury, B. B.; Biswal, Bibhuti Bhusan; Dash, Kshitish K.

doi:10.1109/raics.2011.6069344

Cited by 10 publications

(3 citation statements)

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“…Also, it shows that the level 1 (0.01) for   and level 3 (150) for G n being chosen as the best level for the different robot to task ratios. The optimal levels obtained matches with the work done by khuntia 26 , et al for multirobot task allocation.…”

Section: Optimal Level Selection For Ga Parameterssupporting

confidence: 84%

Multi-objective Optimisation of Multi-robot Task Allocation with Precedence Constraints

et al. 2018

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Efficacy of the multi-robot systems depends on proper sequencing and optimal allocation of robots to the tasks. Focuses on deciding the optimal allocation of set-of-robots to a set-of-tasks with precedence constraints considering multiple objectives. Taguchi’s design of experiments based parameter tuned genetic algorithm (GA) is developed for generalised task allocation of single-task robots to multi-robot tasks. The developed methodology is tested for 16 scenarios by varying the number of robots and number of tasks. The scenarios were tested in a simulated environment with a maximum of 20 robots and 40 multi-robot foraging tasks. The tradeoff between performance measures for the allocations obtained through GA for different task levels was used to decide the optimal number of robots. It is evident that the tradeoffs occur at 20 per cent of performance measures and the optimal number of robot varies between 10 and 15 for almost all the task levels. This method shows good convergence and found that the precedence constraints affect the optimal number of robots required for a particular task level.

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Section: Optimal Level Selection For Ga Parameterssupporting

confidence: 84%

Multi-objective Optimisation of Multi-robot Task Allocation with Precedence Constraints

et al. 2018

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“…For a general discussion of the advantages and disadvantages of these categories see [2] and [3]. An example traditional method is the use of genetic or memetic algorithms to evolve a problem solution (see [8], [9], [15], and [16]). The main advantage of these methods is the ability to solve higher dimensioned problems considerably faster than some other search methods [17], but optimality is not guaranteed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In addition, simple, uninformed search-based methods cannot deal with the large solution space. Much research effort has therefore been directed toward designing search-based methods that incorporate some sort of heuristic, for example Tabu-search [7], genetic algorithms [8], [9], simulated annealing [10], and artificial neural networks [11]. These algorithms are generally referred to as traditional approaches.…”

Section: Introductionmentioning

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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