A mixed capability-based and optimization methodology for human-robot task allocation and scheduling

Andrea, Monguzzi,; Badawi, Mahmoud; Zanchettin, Andrea Maria; Rocco, Paolo

doi:10.1109/ro-man53752.2022.9900823

Cited by 5 publications

(1 citation statement)

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“…Both allocation and scheduling are considered in [19]. The authors approached the allocation problem based on agent capabilities, considering a single human and single robot duo in an offline fashion.…”

Section: Related Workmentioning

confidence: 99%

Learning and planning for optimal synergistic human-robot coordination in manufacturing contexts

Sandrini,

Faroni,

Pedrocchi

2023

Preprint

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Modern hybrid robot cells leverage heterogeneous agents to provide agile production solutions. Effective agent coordination is crucial to avoid inefficiencies and potential hazards for human operators working among robots. This paper proposes a new human-aware task allocation and scheduling model based on Mixed Integer Non-Linear Programming (MINLP) to optimize efficiency and safety during task planning, scheduling, and allocation. The approach introduces a synergy index that encodes the coupling effects between pairs of tasks executed in parallel by the agents. These terms are learned from previous process executions by means of a Bayesian linear estimation. The task planning model is enhanced by the knowledge of synergy terms to adapt the nominal duration of the plan to consider the effect of the operator’s presence. Simulations and experimental results demonstrate the effectiveness of the proposed method in obtaining a proactive human-aware solution starting from the task planning level. The proposed model reduces process execution time and achieves solutions with less agent interference, more considerable human-robot distance, and, thus, safer for agents.

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Section: Related Workmentioning

confidence: 99%