Multi-Robot Task Sequencing &amp; Automatic Path Planning for Cycle Time Optimization: Application for Car Production Line

Touzani, Hicham; Hadj-Abdelkader, Hicham; Séguy, Nicolas; Bouchafa, Samia

doi:10.1109/lra.2021.3057011

Cited by 33 publications

(13 citation statements)

References 15 publications

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“…In order to verify the performance of the proposed collision-detection algorithm, some obstacle sequences and manipulator joint configurations are randomly extracted from the task space and the configuration space respectively. The collision state between the obstacles and the manipulator are detected by employing the simplified collision-detection algorithm and the standard collision-detection algorithm [26] respectively, on which a total of 100 such calculations are performed. The statistical data of the experiment are shown in Table 2, which further supports the above conclusion.…”

Section: Table 2 Time Consumption Comparison Among the Two Algorithms(s)mentioning

confidence: 99%

Research on the Dynamic Path Planning of Manipulators Based on a Grid-Local Probability Road Map Method

et al. 2021

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A Grid-Local Probability Road Map (PRM) method was proposed for the path planning of manipulators in dynamic environments. Based on the idea of boundary discretization, a double-grid model was built to obtain a mapping from dynamic obstacles to configuration space. The collision detection was simplified as a data indexing process to improve its efficiency. Times of collision detections were reduced by employing local programming strategies and the stratified sampling method. Moreover, the validity of sampling was increased. Taking the PUMA560 manipulator as a research object, the simulation experiments show that the time consumption of the proposed simplified collision-detection algorithm is about 14% of that of the standard one, and the stratified sampling is beneficial to the generation of probability maps compared with simple random sampling method. The simulation experiment of the static path planning shows that the proposed algorithm consumes an average of 10ms, which is superior to the comparison algorithm and has high efficiency and real-time performance. The simulation experiment of the dynamic path planning shows that the proposed algorithm consumes an average of 7ms per step, which is better than the comparison algorithm. The proposed algorithm can adjust the global path in real time to avoid obstacles as the environment changes. The algorithm mentioned has been proved to be efficient.INDEX TERMS Dynamic environment, double grid model, probability road map, path planning.

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Section: Table 2 Time Consumption Comparison Among the Two Algorithms(s)mentioning

confidence: 99%

Research on the Dynamic Path Planning of Manipulators Based on a Grid-Local Probability Road Map Method

et al. 2021

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“…[1,3] minimum gap (g min i,j ) 1 maximum gap (g max i,j ) [3,5] 15 Mbps, contains some fluctuations; the data rate of No-Scheduling changes heavily from 5 to 22 Mbps. From the results, we can verify that the proposed methods can be applied to flatten traffic communications.…”

Section: Numerical Investigationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Optimization for industrial robots has attracted significant attention in prior works [5]- [10]. In order to solve the multitask sequencing problem in an automotive assembly line, the authors in [5] develop a genetic algorithm-based approach that minimizes the production time and the duration of robots' motion. The study in [6] considers multi-robot scenarios with possible communication failure and proposes distributed coalition formation and task assignment methods.…”

Section: Introductionmentioning

confidence: 99%

“…The study in [10] designs a computation offloading method with the consideration of quality-of-service for cloud robotics. However, the majority of the above works do not consider the network resource, especially the bandwidth, when designing the allocation strategies [5]- [9]. Moreover, the research in [10] employs the bandwidth utilization as an energy indicator but ignores the network resource limitations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Minmax Utilization Algorithm for Network Traffic Scheduling of Industrial Robots

Yantong¹,

Friderikos²,

Andraos³

2021

Preprint

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Emerging 5G and beyond wireless industrial virtualized networks are expected to support a significant number of robotic manipulators. Depending on the processes involved, these industrial robots might result in significant volume of multi-modal traffic that will need to traverse the network all the way to the (public/private) edge cloud, where advanced processing, control and service orchestration will be taking place. In this paper, we perform the traffic engineering by capitalizing on the underlying pseudo-deterministic nature of the repetitive processes of robotic manipulators in an industrial environment and propose an integer linear programming (ILP) model to minimize the maximum aggregate traffic in the network. The task sequence and time gap requirements are also considered in the proposed model. To tackle the curse of dimensionality in ILP, we provide a random search algorithm with quadratic time complexity. Numerical investigations reveal that the proposed scheme can reduce the peak data rate up to 53.4% compared with the nominal case where robotic manipulators operate in an uncoordinated fashion, resulting in significant improvement in the utilization of the underlying network resources.

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Online Route Scheduling for a Team of Service Robots with MOEAs and mTSP Model

Alves,

Silva,

Souza

2022

Engineering Applications of Neural Networks

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Multi-Robot Task Sequencing & Automatic Path Planning for Cycle Time Optimization: Application for Car Production Line

Cited by 33 publications

References 15 publications

Research on the Dynamic Path Planning of Manipulators Based on a Grid-Local Probability Road Map Method

Research on the Dynamic Path Planning of Manipulators Based on a Grid-Local Probability Road Map Method

A Minmax Utilization Algorithm for Network Traffic Scheduling of Industrial Robots

Online Route Scheduling for a Team of Service Robots with MOEAs and mTSP Model

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