A distributed algorithm for balanced multi-robot task allocation

Gautam, Avinash; Thakur, Amitayush; Dhanania, Garima; Mohan, Sudeept

doi:10.1109/iciinfs.2016.8263014

Cited by 8 publications

(6 citation statements)

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“…We propose three evaluation indexes: the TC of the handling robot, the TTC of the handling robot, and the BU of the handling robot. We compare the proposed algorithm with the traditional auction algorithm, paper [10] and paper [13]. The experimental results show that although the proposed algorithm cannot effectively reduce the TTC of the handling robot, it can reduce the TC and improve the BU.…”

Section: Discussionmentioning

confidence: 99%

“…In section 3, we discuss the task allocation problem in the agricultural picking and transportation system, and describe in detail the distributed task allocation algorithm of multi-handling robots based on task similarity grouping. In section 4, the simulation experiment is carried out and compared with the traditional auction method, paper [10] and paper [13], and the experimental results and analysis are given. Finally, summarize this paper in section 5.…”

Section: B Motivation and Contributionsmentioning

confidence: 99%

“…To verify the effectiveness of the proposed method, we compared the proposed algorithm with the traditional auction algorithm, the balanced heuristic mechanism algorithm for MRTA [10] and the distributed algorithm for MRTA [13], and designed experiments in three cases. Case 1: The number of handling robots is set to 5, and the number of tasks is set to 79.…”

Section: Simulated Experiments and Analysismentioning

confidence: 99%

“…6 is obtained by running the balanced heuristic mechanism algorithm of MRTA [10], Gantt Fig. 7 is obtained by running the distributed algorithm of MRTA [13], and Gantt Fig. 8 is obtained by running the algorithm proposed in this paper.…”

Section: Simulated Experiments and Analysismentioning

confidence: 99%

“…As long as the central controller collapses, the whole multi-robot system will collapse, which is not suitable for scenes with large number of robots, large number of tasks, and complex working environment [11], [12]. Therefore, Avinash Gautam [13] proposed a distributed algorithm to balance the task allocation of multi-robots. The distributed genetic algorithm was used to calculate the travel salesman travel (TST) of all tasks.…”

Section: Introductionmentioning

confidence: 99%

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A distributed task assignment algorithm for multi-robots in plant factory

Wang

et al. 2022

Preprint

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This paper focuses on the task allocation problem relating to multi-handling robots in agricultural picking and transportation system. It focuses on how to effectively and equally allocate tasks to handling robots, while avoiding collisions. A distributed task allocation algorithm for multi-handling robots based on task similarity grouping is proposed. First, the central controller divides the tasks into similar groups. On the one hand, it divides the tasks into different areas and reduces the collision between the handling robots, and on the other hand, it ensures the balanced utilization of the handling robots. Then, the central controller transmits the task groups to the handling robots through wireless communication, and the task group bidding between the handling robots is carried out by using a novel auction algorithm to minimize the travel cost (TC) of the handling robots, minimize the total travel cost (TTC) of the handling robots, and maximize the balanced utilization (BU) of the handling robots.We propose three evaluation indexes: the TC of the handling robot, TTC of the handling robot, and BU rate of the handling robot. The experimental results show that the proposed algorithm can reduce the TC and improve the BU. Therefore, the newly proposed task allocation algorithm is important for the scheduling of multi-handling robots in agricultural picking and transportation systems.

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Section: Discussionmentioning

confidence: 99%

Section: B Motivation and Contributionsmentioning

confidence: 99%

Section: Simulated Experiments and Analysismentioning

confidence: 99%

Section: Simulated Experiments and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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