A probabilistic approach to workspace sharing for human–robot cooperation in assembly tasks

Pellegrinelli, Stefania; Moro, Federico L.; Pedrocchi, Nicola; Tosatti, Lorenzo Molinari; Tolio, Tullio

doi:10.1016/j.cirp.2016.04.035

Cited by 51 publications

(36 citation statements)

References 17 publications

(19 reference statements)

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“…While some characteristics are quantitative, most of the studies highlighted a small number of properties that define distinct classes of HRC instances:  Temporal and spatial relation of collaborating humans and robots (agents in a more generic sense)-while this shows wide variation in cases like teleoperation or assisted vehicle steering [13], it is assumed in industrial production that the agents (partially) share the same space, even though their activity over time does not necessarily overlap [7]. Close collaboration with physical contact [14]-e.g., common handling of large workpieces-does, naturally, require colocation and simultaneous operation [15].  Agent multiplicity can be covered to its full diversity by industrial HRC applications.…”

Section: Classification Of Human-robot Collaborationmentioning

confidence: 99%

Human–robot collaborative assembly in cyber-physical production: Classification framework and implementation

et al. 2017

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The production industry is moving towards the next generation of assembly, which is conducted based on safe and reliable robots working in the same workplace alongside with humans. Focusing on assembly tasks, this paper presents a review of human-robot collaboration research and its classification works. Aside from defining key terms and relations, the paper also proposes means of describing human-robot collaboration that can be relied on during detailed elaboration of solutions. A human-robot collaborative assembly system is developed with a novel and comprehensive structure, and a case study is presented to validate the proposed framework.Assembly, man-machine system, human-robot collaboration

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Section: Classification Of Human-robot Collaborationmentioning

confidence: 99%

Human–robot collaborative assembly in cyber-physical production: Classification framework and implementation

et al. 2017

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“…The implemented algorithms proved to be efficient on medium-sized real life mechanical assembly problems from the automotive industry. Future work will concentrate on richer process models (e.g., task durations depending on resource assignment) and on extension to human-robot cooperation [18]. …”

Section: Discussionmentioning

confidence: 99%

Decomposition approach to optimal feature-based assembly planning

2017

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The paper proposes a generic approach to assembly planning where individual tasks, with detailed technological content specified by features, must be combined into an optimal assembly plan subject to technological and geometric constraints. To cope with the complexity and variety of the constraints that refer to the overall assembly process, Benders decomposition is applied. The macro-level master problem looks for the optimal sequencing and resource assignment of the tasks, while sub-problem modules ensure plan feasibility on the micro-level from aspects of technology, fixturing, tooling, and collision. Constraints are also dynamically generated for the master problem. The approach is demonstrated in automotive assembly.

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“…The human and the robot actions are unsynchronised, i.e., the human and the robot have to work in the same workspace on different uncorrelated goals. Extending the applicability to a higher number of human possible tasks and to industrial robots, Pellegrinelli et al [195] introduced a framework for human-robot workspace sharing (unsynchronised tasks) based on hindsight optimisation [92]. The approach defines a distribution probability on human's goal on the basis of the movements of the human's hand.…”

Section: Robot Motion Planningmentioning

confidence: 99%

Symbiotic human-robot collaborative assembly

et al. 2019

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In human-robot collaborative assembly, robots are often required to dynamically change their pre-planned tasks to collaborate with human operators in a shared workspace. However, the robots used today are controlled by pre-generated rigid codes that cannot support effective human-robot collaboration. In response to this need, multi-modal yet symbiotic communication and control methods have been a focus in recent years. These methods include voice processing, gesture recognition, haptic interaction, and brainwave perception. Deep learning is used for classification, recognition and context awareness identification. Within this context, this keynote provides an overview of symbiotic human-robot collaborative assembly and highlights future research directions.

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A probabilistic approach to workspace sharing for human–robot cooperation in assembly tasks

Cited by 51 publications

References 17 publications

Human–robot collaborative assembly in cyber-physical production: Classification framework and implementation

Human–robot collaborative assembly in cyber-physical production: Classification framework and implementation

Decomposition approach to optimal feature-based assembly planning

Symbiotic human-robot collaborative assembly

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