Decentralized multi-robot allocation of tasks with temporal and precedence constraints

Nunes, Everson Araújo; McIntire, Mitchell; Gini, Maria

doi:10.1080/01691864.2017.1396922

Cited by 48 publications

(70 citation statements)

References 16 publications

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“…Auction-based approaches to task allocation have been proposed for tasks with precedence [17], with temporal [8,20] constraints, and for dynamic environments [25] with single robot tasks. Environments that contain multi-robot tasks, with and without constraints, are less well investigated than their single-robot counterparts [12].…”

Section: Related Workmentioning

confidence: 99%

Evaluating Multi-Robot Teamwork in Parameterised Environments

Schneider

Sklar

Parsons

2016

Towards Autonomous Robotic Systems

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Abstract. The work presented here investigates the impact of certain environmental parameters on the performance of a multi-robot team conducting exploration tasks. Experiments were conducted with physical robots and simulated robots, and a diverse set of metrics was computed. The experiments were structured to highlight several factors: (a) single-robot versus multi-robot tasks; (b) independent versus dependent (or "constrained") tasks; and (c) static versus dynamic task allocation modes. Four different task allocation mechanisms were compared, in two different exploration scenarios, with two different starting configurations for the robot team. The results highlight the distinctions between parameterised environments (characterised by the factors above, the robots' starting positions and the exploration scenario) and the effectiveness of each task allocation mechanism, illustrating that some mechanisms perform statistically better in particular environment parameterisations.

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Section: Related Workmentioning

confidence: 99%

Evaluating Multi-Robot Teamwork in Parameterised Environments

Schneider

Sklar

Parsons

2016

Towards Autonomous Robotic Systems

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“…Since generating good-quality schedules quickly is especially important, we evaluate the method's performance on the basis of schedule efficiency and computation time. We also compare our method with an optimized greedy scheduler, as in [18], that uses the same scheduling framework (nominal scheduling and leftover task rescheduling) and leftover optimization as our method.…”

Section: Implementation and Resultsmentioning

confidence: 99%

“…We address the trade-off between quality and computation time for large-scale problems by employing a market-based method as a schedule refinement technique on a smaller scale problem defined by the leftover tasks, instead of using it to directly solve the full scheduling problem. In particular, our method exploits a schedule placement technique similar to the one used in [18], where optimal placement of the task within the schedule helped inform bids and increased solution optimality.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Scheduling Algorithm for Multi-Robot Task Allocation in Assembling Aircraft Structures

Tereshchuk

Stewart

Bykov

et al. 2019

IEEE Robot. Autom. Lett.

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Efficient utilization of cooperating robots in the assembly of aircraft structures relies on balancing the workload of the robots and ensuring collision-free scheduling. We cast this problem as that of allocating a large number of repetitive assembly tasks, such as drilling holes and installing fasteners, among multiple robots. Such task allocation is often formulated as a Traveling Salesman Problem (TSP), which is NPhard, implying that computing an exactly optimal solution is computationally prohibitive for real-world applications. The problem complexity is further exacerbated by intermittent robot failures necessitating real-time task reallocation. In this letter, we present an efficient method that exploits workpart geometry and problem structure to initially generate balanced and conflict-free robot schedules under nominal conditions. Subsequently, we deal with the failures by allowing the robots to first complete their nominal schedules and then employing a market-based optimizer to allocate the leftover tasks. Results show an improvement of 11.5% in schedule efficiency as compared to an optimized greedy multi-agent scheduler on a four robot system, which is especially promising for aircraft assembly processes that take many hours to complete. Moreover, the computation times are similar and small, typically hundreds of milliseconds.

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“…In general, however, the problem of task allocation is a complex one [34] and, even though some solutions already exist [35,36], it is a topic that requires further research. An interesting solution is proposed in [37], in which a decentralized auction-based system is used.…”

Section: Discussionmentioning

confidence: 99%

A method for real-time dynamic fleet mission planning for autonomous mining

Wahde

Bellone

Torabi

2019

Auton Agent Multi-Agent Syst

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This paper introduces a method for dynamic fleet mission planning for autonomous mining (in loop-free maps), in which a dynamic fleet mission is defined as a sequence of static fleet missions, each generated using a modified genetic algorithm. For the case of static fleet mission planning (where each vehicle completes just one mission), the proposed method is able to reliably generate, within a short optimization time, feasible fleet missions with short total duration and as few stops as possible. For the dynamic case, in simulations involving a realistic mine map, the proposed method is able to generate efficient dynamic plans such that the number of completed missions per vehicle is only slightly reduced as the number of vehicles is increased, demonstrating the favorable scaling properties of the method as well as its applicability in real-world cases.

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Decentralized multi-robot allocation of tasks with temporal and precedence constraints

Cited by 48 publications

References 16 publications

Evaluating Multi-Robot Teamwork in Parameterised Environments

Evaluating Multi-Robot Teamwork in Parameterised Environments

An Efficient Scheduling Algorithm for Multi-Robot Task Allocation in Assembling Aircraft Structures

A method for real-time dynamic fleet mission planning for autonomous mining

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