Considering inter-task resource constraints in task allocation

Zhang, Yu; Parker, Lynne E.

doi:10.1007/s10458-012-9196-7

Cited by 54 publications

(29 citation statements)

References 20 publications

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“…A second point is related to the number of agents simultaneously completing tasks. In Chen andSun (2011), Zhang andParker (2013), Balakirsky et al (2007) several agents are required, while in our work only one agent is completing each task. Another consideration is that our system is fully heterogeneous, i.e.…”

Section: Comparative Analysismentioning

confidence: 99%

Solving the task variant allocation problem in distributed robotics

et al. 2018

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We consider the problem of assigning software processes (or tasks) to hardware processors in distributed robotics environments. We introduce the notion of a task variant, which supports the adaptation of software to specific hardware configurations. Task variants facilitate the trade-off of functional quality versus the requisite capacity and type of target execution processors. We formalise the problem of assigning task variants to processors as a mathematical model that incorporates typical constraints found in robotics applications; the model is a constrained form of a multi-objective, multi-dimensional, multiple-choice knapsack problem. We propose and evaluate three different solution methods to the problem: constraint programming, a constructive greedy heuristic and a local search metaheuristic. Furthermore, we demonstrate the use of task variants in a real instance of a distributed interactive multi-agent navigation system, showing that our best solution method (constraint programming) improves the system's quality of service, as compared to the local search metaheuristic, the greedy heuristic and a randomised solution, by an average of 16, 31 and 56% respectively.

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Section: Comparative Analysismentioning

confidence: 99%

Solving the task variant allocation problem in distributed robotics

et al. 2018

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“…Computer performance may also be restricted in some multirobot task allocation problems. Therefore, dispatching methods have often been studied in multirobot task allocation (e.g., methods that maximize the sum of efficiency [9,[25][26][27][28][29][30][31][32][33]). These methods entail searching for the best combination of robots and tasks that maximize the sum of efficiency.…”

Section: Related Workmentioning

confidence: 99%

Allocating Multiple Types of Tasks to Heterogeneous Agents Based on the Theory of Comparative Advantage

Morisawa

Hayashi

Mizuuchi

2018

Journal of Robotics

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We present a method to allocate multiple tasks with uncertainty to heterogeneous robots using the theory of comparative advantage: an economic theory that maximizes the benefit of specialization. In real applications, robots often must execute various tasks with uncertainty and future multirobot system will have to work effectively with people as a team. As an example, it may be necessary to explore an unknown environment while executing a main task with people, such as carrying, rescue, military, or construction. The proposed task allocation method is expected to reduce the total makespan (total length of task-execution time) compared with conventional methods in robotic exploration missions. We expect that our method is also effective in terms of calculation time compared with the time-extended allocation method (based on the solution of job-shop scheduling problems). We simulated carrying tasks and exploratory tasks, which include uncertainty conditions such as unknown work environments (2 tasks and 2 robots, multiple tasks and 2 robots, 2 robots and multiple tasks, and multiple tasks and multiple robots). In addition, we compared our method with full searching and methods that maximize the sum of efficiency in these simulations by several conditions: first, 2 tasks (carrying and exploring) in the four uncertain conditions (later time, new objects appearing, disobedient robots, and shorter carrying time) and second, many types of tasks to many types of robots in the three uncertain conditions (unknown carrying time, new objects appearing, and some reasonable agents). The proposed method is also effective in three terms: the task-execution time with an increasing number of objects, uncertain increase in the number of tasks during task execution, and uncertainty agents who are disobedient to allocation orders compared to full searching and methods that maximize the sum of efficiency. Additionally, we performed two real-world experiments with uncertainty.

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“…This is related to the issue of task dependency and is not specifically considered in this work. See [16] for some discussions on this issue. Table II shows the robots required for each object and the time required to accomplish the move.…”

Section: A An Object Collection Scenariomentioning

confidence: 99%

“…Furthermore, task dependencies (e.g., precedence orders) are not considered. However, note that the algorithms proposed in this paper can be extended when task dependencies are present, for example, using similar approaches as in [10], [16]. The solution bounds in these situations need to be re-established.…”

Section: Introductionmentioning

confidence: 99%

Multi-robot task scheduling

Zhang

Parker

2013

2013 IEEE International Conference on Robotics and Automation

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Abstract-The scheduling problem has been studied extensively in the literature. Many algorithms have been developed to operate with different types of processors and tasks. In the robotics domain, when considering each robot as a processor, some of these algorithms can be directly adapted. However, most of the existing algorithms can only handle single-robot tasks, or multi-robot tasks that can be divided into singlerobot tasks. As the task requirements may only be partially known, and the available (heterogeneous) robots can dynamically change, robots may be required to cooperate tightly to share different capabilities (i.e., sensors and motors). In such cases, considering scheduling for individual robots is no longer sufficient, since the robots need to work at the coalition level. Although there exist a few algorithms that also support these more complex cases, they do not represent efficient solutions that can be adapted by various multi-robot systems in a convenient manner. In this paper, we propose heuristics to address the multi-robot task scheduling problem at the coalition level, which hides the details of robot specifications, thus allowing these heuristics to be incorporated straightforwardly. These heuristics are easy to implement and efficient enough to run in real time. We provide formal analyses and simulation results to demonstrate and compare their performances.

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Considering inter-task resource constraints in task allocation

Cited by 54 publications

References 20 publications

Solving the task variant allocation problem in distributed robotics

Solving the task variant allocation problem in distributed robotics

Allocating Multiple Types of Tasks to Heterogeneous Agents Based on the Theory of Comparative Advantage

Multi-robot task scheduling

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