Decentral task allocation for industrial AGV-systems with resource constraints

Ryck, M. De; Pissoort, Davy; Holvoet, Tom; Demeester, Eric

doi:10.1016/j.jmsy.2021.03.008

Cited by 34 publications

(17 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A very slight difference (1–2%) in the simulated and real experimentation was observed in terms of communication overhead for each task -robot ratio in the system, as can be seen in Figs 15 and 16 .The robot could not proceed further in case of resource failure Ryck at al [ 40 ], and the task needed to be re-announced and reallocated, whereas, in the proposed approach, a request was made only for the failed resources and only the robots having the required resource responded which reduced the communication burden on the entire system. Furthermore, in the task allocation proposed in Dai et al[ 38 ] reduced communication burden; however it still had 16.7% more communication burden on the system as compared to the proposed approach, as shown in Fig 16 .…”

Section: Resultsmentioning

confidence: 99%

“…The time required by the benchmark techniques of task allocation Ryck at al [ 40 ], Dai et al [ 39 ], Lee et al [ 38 ] with the same objective value achievement is shown in Fig 11 .…”

Section: Resultsmentioning

confidence: 99%

“…Also, in Dai et al [ 38 ] when a robot was partially damaged during task execution, the task was declared undone and needed to be reallocated to another robot the same applied to Ryck et al [ 40 ], Dai et al [ 39 ]. Contrary to this in the proposed approach, when the robot was partially damaged, it could request for only the failed resource without re-announcing the entire task, provided the deficiency was not less than the AIV ( i ).…”

Section: Resultsmentioning

confidence: 99%

“…The results are validated with benchmark Lee et al [ 38 ], the task allocation with Deep Q-Learning incorporation Dai et al [ 39 ], and Decentralized Task Allocation Ryck et al [ 40 ]. We aim to minimize the total cost of executing tasks to complete more tasks with limited resources and task completion despite faults in robot’s capabilities, also decreasing the system’s communication burden.…”

Section: Experimentationmentioning

confidence: 99%

See 3 more Smart Citations

Task allocation in multi-robot system using resource sharing with dynamic threshold approach

et al. 2022

View full text Add to dashboard Cite

Task allocation is a fundamental requirement for multi-robot systems working in dynamic environments. An efficient task allocation algorithm allows the robots to adjust their behavior in response to environmental changes such as fault occurrences, or other robots’ actions to increase overall system performance. To address these challenges, this paper presents a Task Allocation technique based on a threshold level which is an accumulative value aggregated by a centralized unit using the Task-Robot ratio and the number of the available resource in the system. The threshold level serves as a reference for task acceptance and the task acceptance occurs despite resource shortage. The deficient resources for the accepted task are acquired through an auction process using objective minimization. Despite resource shortage, task acceptance occurs. The threshold approach and the objective minimization in the auction process reduce the overall completion time and increase the system’s resource utilization up to 96%, which is demonstrated theoretically and validated through simulations and real experimentation.

show abstract

Section: Resultsmentioning

confidence: 99%

“…The time required by the benchmark techniques of task allocation Ryck at al [ 40 ], Dai et al [ 39 ], Lee et al [ 38 ] with the same objective value achievement is shown in Fig 11 .…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Experimentationmentioning

confidence: 99%

See 2 more Smart Citations

Task allocation in multi-robot system using resource sharing with dynamic threshold approach

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Multi-robot task scheduling deals with allocating the tasks to robots in the system. We reviewed the multi-robot task allocation literature in our previous work [4,5]. Multi-robot routing, on the other hand, deals with finding the most optimal path for each robot in the system in order to execute all assigned tasks resulting from the task scheduling problem, and this without congestions, deadlocks, or live-locks.…”

Section: Literature Reviewmentioning

confidence: 99%