Dataset Information

A Resilient and Effective Task Scheduling Approach for Industrial Human-Robot Collaboration.

ABSTRACT: Effective task scheduling in human-robot collaboration (HRC) scenarios is one of the great challenges of collaborative robotics. The shared workspace inside an industrial setting brings a lot of uncertainties that cannot be foreseen. A prior offline task scheduling strategy is ineffective in dealing with these uncertainties. In this paper, a novel online framework to achieve a resilient and reliable task schedule is presented. The framework can deal with deviations that occur during operation, different operator skills, error by the human or robot, and substitution of actors, while maintaining an efficient schedule by promoting parallel human-robot work. First, the collaborative job and the possible deviations are represented by AND/OR graphs. Subsequently, the proposed architecture chooses the most suitable path to improve the collaboration. If some failures occur, the AND/OR graph is adapted locally, allowing the collaboration to be completed. The framework is validated in an industrial assembly scenario with a Franka Emika Panda collaborative robot.

SUBMITTER: Pupa A

PROVIDER: S-EPMC9269759 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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A Resilient and Effective Task Scheduling Approach for Industrial Human-Robot Collaboration.

Pupa Andrea A Van Dijk Wietse W Brekelmans Christiaan C Secchi Cristian C

Sensors (Basel, Switzerland) 20220629 13

Effective task scheduling in human-robot collaboration (HRC) scenarios is one of the great challenges of collaborative robotics. The shared workspace inside an industrial setting brings a lot of uncertainties that cannot be foreseen. A prior offline task scheduling strategy is ineffective in dealing with these uncertainties. In this paper, a novel online framework to achieve a resilient and reliable task schedule is presented. The framework can deal with deviations that occur during operation, d ...[more]

PMID: 35808396

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Project description:Artificial intelligence (AI) provides considerable opportunities to assist human work. However, one crucial challenge of human-AI collaboration is that many AI algorithms operate in a black-box manner where the way how the AI makes predictions remains opaque. This makes it difficult for humans to validate a prediction made by AI against their own domain knowledge. For this reason, we hypothesize that augmenting humans with explainable AI improves task performance in human-AI collaboration. To test this hypothesis, we implement explainable AI in the form of visual heatmaps in inspection tasks conducted by domain experts. Visual heatmaps have the advantage that they are easy to understand and help to localize relevant parts of an image. We then compare participants that were either supported by (a) black-box AI or (b) explainable AI, where the latter supports them to follow AI predictions when the AI is accurate or overrule the AI when the AI predictions are wrong. We conducted two preregistered experiments with representative, real-world visual inspection tasks from manufacturing and medicine. The first experiment was conducted with factory workers from an electronics factory, who performed [Formula: see text] assessments of whether electronic products have defects. The second experiment was conducted with radiologists, who performed [Formula: see text] assessments of chest X-ray images to identify lung lesions. The results of our experiments with domain experts performing real-world tasks show that task performance improves when participants are supported by explainable AI with heatmaps instead of black-box AI. We find that explainable AI as a decision aid improved the task performance by 7.7 percentage points (95% confidence interval [CI]: 3.3% to 12.0%, [Formula: see text]) in the manufacturing experiment and by 4.7 percentage points (95% CI: 1.1% to 8.3%, [Formula: see text]) in the medical experiment compared to black-box AI. These gains represent a significant improvement in task performance.

Dataset Information

A Resilient and Effective Task Scheduling Approach for Industrial Human-Robot Collaboration.

Publications

A Resilient and Effective Task Scheduling Approach for Industrial Human-Robot Collaboration.

Similar Datasets

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