A taxonomy for task allocation problems with temporal and ordering constraints

Nunes, Everson Araújo; Manner, Marie D.; Mitiche, Hakim; Gini, Maria

doi:10.1016/j.robot.2016.10.008

Cited by 197 publications

(126 citation statements)

References 143 publications

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“…Online methods for multi-agent task assignment and scheduling algorithms have been developed for small-sized teams of agents, and highly flexible against execution uncertainty [24]. Multi-robot task allocation with temporal/ordering constraints has been studied in the context of integrating auction-based methods with Simple Temporal Problems [22]. These methods, however, do not account for conflicting spatial interactions, as needed, for example, in dual-arm manipulations.…”

Section: Related Workmentioning

confidence: 99%

A Constraint Programming Approach to Simultaneous Task Allocation and Motion Scheduling for Industrial Dual-Arm Manipulation Tasks

Behrens

Lange

Mansouri

2019

2019 International Conference on Robotics and Automation (ICRA)

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Modern lightweight dual-arm robots bring the physical capabilities to quickly take over tasks at typical industrial workplaces designed for workers. In times of mass-customization, low setup times including the instructing/specifying of new tasks are crucial to stay competitive. We propose a constraint programming approach to simultaneous task allocation and motion scheduling for such industrial manipulation and assembly tasks. The proposed approach covers dual-arm and even multi-arm robots as well as connected machines. The key concept are Ordered Visiting Constraints, a descriptive and extensible model to specify such tasks with their spatiotemporal requirements and task-specific combinatorial or ordering constraints. Our solver integrates such task models and robot motion models into constraint optimization problems and solves them efficiently using various heuristics to produce makespan-optimized robot programs. The proposed task model is robot independent and thus can easily be deployed to other robotic platforms. Flexibility and portability of our proposed model is validated through several experiments on different simulated robot platforms. We benchmarked our search strategy against a generalpurpose heuristic. For large manipulation tasks with 200 objects, our solver implemented using Google's Operations Research tools and ROS requires less than a minute to compute usable plans.

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Section: Related Workmentioning

confidence: 99%

A Constraint Programming Approach to Simultaneous Task Allocation and Motion Scheduling for Industrial Dual-Arm Manipulation Tasks

Behrens

Lange

Mansouri

2019

2019 International Conference on Robotics and Automation (ICRA)

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“…Definition 1.1 A robot is an autonomous entity that acts in an environment and is capable of performing some actions [41]. If an MRTA problem is taken into account, a robot is then typically modeled as a material point; i.e., the physical layer is omitted.…”

Section: Basic Definitionsmentioning

confidence: 99%

“…If the earliest start time is not provided, then the latest finish time is called the "deadline." A time window is closed if both times are given [41]. Definition 1.5 Synchronization constraints specify temporal restrictions on the tasks [41]; e.g., "tasks t 1 and t 2 must start at the same time."…”

Section: Basic Definitionsmentioning

confidence: 99%

“…The Multi-Robot Task Allocation problem (MRTA) is informally defined as follows: "given two sets of robots and tasks, the purpose is to allocate tasks to robots while optimizing some criteria (i.e., objective function) under several constraints" [30,36,49]. The problem is known to be N P-hard; solving it in an optimal way is a great challenge, especially when heterogeneous robots, complex tasks, and dynamic environments should be considered [41]. In order to understand the MRTA problem, the following sections will give some useful definitions.…”

Section: Introductionmentioning

confidence: 99%

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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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“…The actual performance of allocating settings often differs from the planned because of the design change. The deviations in the execution time and other uncertain events always lead the allocations to inaccuracy or infeasibility 26,31,32 . They negatively increase the variability in the system, which deteriorates the allocating performance in turn, and eventually, the design change upsets the system performance or lead to infeasibility.…”

Section: Contributionmentioning

confidence: 99%

A Robust Predictive–Reactive Allocating Approach, Considering Random Design Change in Complex Product Design Processes

Su¹,

Meng²,

Liu³

2018

IJCIS

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In the highly dynamic complex product design process, task allocations recovered by reactive allocating decisions are usually subject to design changes. In this paper, a robust predictive-reactive allocating approach considering possible disruption times is proposed, so that it can absorb the disruption in the executing process and utilize the limited capacity of resource more effectively. Four indexes (Makespan, stability, robustness, and compression cost) are used to measure the quality of the proposed method. To illustrate the novel allocating idea, we first assign tasks to resources with the objective of a trade-off between the overall execution time and the overall design cost, which can transform the problem into a non-identical parallel environment. Then, the probability distribution sequencing (PDS) method combining with inserting idle time (IIT) is proposed to generate an original-predictive allocation. A match-up time strategy is considered to match up with the initial allocation at some point in the future. The relationship between the minimum match-up time and the compression cost is analyzed to find the optimal matchup time. Our computational results show that the proposed sequencing method is better than the shortest processing time (SPT) which is a common sequencing way mentioned in the literature. The robust predictive-reactive allocating approach is sensitive to the design change, which is helpful to reduce the reallocating cost and keep the robustness and stability.

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A taxonomy for task allocation problems with temporal and ordering constraints

Cited by 197 publications

References 143 publications

A Constraint Programming Approach to Simultaneous Task Allocation and Motion Scheduling for Industrial Dual-Arm Manipulation Tasks

A Constraint Programming Approach to Simultaneous Task Allocation and Motion Scheduling for Industrial Dual-Arm Manipulation Tasks

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

A Robust Predictive–Reactive Allocating Approach, Considering Random Design Change in Complex Product Design Processes

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