ProSeqqo: A generic solver for process planning and sequencing in industrial robotics

Zahorán, László; Kovács, András

doi:10.1016/j.rcim.2022.102387

Cited by 3 publications

(2 citation statements)

References 36 publications

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“…More recently, the RoboTSP algorithm [16] has emerged as another method that could compete with clustering in terms of speed. It was able to produce a high-quality solution in less than a minute for a complex drilling task involving 245 targets, with an average of 28.5 configurations per target.…”

Section: Existing Work For Robot Task Sequencing Problemmentioning

confidence: 99%

Optimizing Robotic Task Sequencing and Trajectory Planning on the Basis of Deep Reinforcement Learning

Dong,

Wan,

Zeng

et al. 2023

Biomimetics

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The robot task sequencing problem and trajectory planning problem are two important issues in the robotic optimization domain and are solved sequentially in two separate levels in traditional studies. This paradigm disregards the potential synergistic impact between the two problems, resulting in a local optimum solution. To address this problem, this paper formulates a co-optimization model that integrates the task sequencing problem and trajectory planning problem into a holistic problem, abbreviated as the robot TSTP problem. To solve the TSTP problem, we model the optimization process as a Markov decision process and propose a deep reinforcement learning (DRL)-based method to facilitate problem solving. To validate the proposed approach, multiple test cases are used to verify the feasibility of the TSTP model and the solving capability of the DRL method. The real-world experimental results demonstrate that the DRL method can achieve a 30.54% energy savings compared to the traditional evolution algorithm, and the computational time required by the proposed DRL method is much shorter than those of the evolutionary algorithms. In addition, when adopting the TSTP model, a 18.22% energy reduction can be achieved compared to using the sequential optimization model.

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Section: Existing Work For Robot Task Sequencing Problemmentioning

confidence: 99%

Optimizing Robotic Task Sequencing and Trajectory Planning on the Basis of Deep Reinforcement Learning

Dong,

Wan,

Zeng

et al. 2023

Biomimetics

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“…Given this setting, a robotic arm should visit all the bins for the target tasks, visiting only a single node within the bins [9]. Please refer to [10,11] for a detailed review on the RTSP.…”

Section: Scheduling Problems For Robotic Arms In Automated Systemsmentioning

confidence: 99%

A Scheduling Solution for Robotic Arm-Based Batching Systems with Multiple Conveyor Belts

Nielsen¹,

Sung

Yafrani³

et al. 2023

Algorithms

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In this study, we tackle a key scheduling problem in a robotic arm-based food processing system, where multiple conveyors—an infeed conveyor that feeds food items to robotic arms and two tray lane conveyors, on which trays to batch food items are placed—are implemented. The target scheduling problem is to determine what item on an infeed conveyor belt is picked up by which robotic arm at what position, and on which tray the picked up item will be placed. This problem involves critical constraints, such as sequence-dependent processing time and dynamic item and tray positions. Moreover, due to the speed of the infeed conveyor and latency in the information about entering items into the system, this scheduling problem must be solved in near real time. To address these challenges, we propose a scheduling solution that first decomposes the original scheduling problem into sub-problems, where a sub-problem formulated as a goal program schedules robotic arms only for a single tray. The performance of the proposed solution approach is then tested under a simulation environment, and from the experiments, the proposed approach produces acceptable performance.

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Multi-objective Operation Path Planning Method for Robotic Arm Based on Configuration Space

Zou,

Liu,

Chen

et al. 2023

2023 5th International Symposium on Robotics &Amp; Intelligent Manufacturing Technology (ISRIMT)

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ProSeqqo: A generic solver for process planning and sequencing in industrial robotics

Cited by 3 publications

References 36 publications

Optimizing Robotic Task Sequencing and Trajectory Planning on the Basis of Deep Reinforcement Learning

Optimizing Robotic Task Sequencing and Trajectory Planning on the Basis of Deep Reinforcement Learning

A Scheduling Solution for Robotic Arm-Based Batching Systems with Multiple Conveyor Belts

Multi-objective Operation Path Planning Method for Robotic Arm Based on Configuration Space

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