A Unifying Framework for Human–Agent Collaborative Systems—Part II: Design Procedure and Application

Ding, Yongsheng; Xin, Bin; Chen, Jie; Yang, Qiliang; Fang, Hao

doi:10.1109/tcyb.2021.3086073

Cited by 6 publications

(2 citation statements)

References 51 publications

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“…HRC-oriented works usually focus on subproblems, such as scheduling human and robot actions [11], [12], [13], or cooperative planning at a symbolic level [14], [15], [16]. Few works address TAMP by explicitly modeling the human agent [17], [18]. For example, [19] and [20] proposed a hierarchical agent-based task planner, where complex tasks are decomposed into simple actions.…”

Section: A Related Workmentioning

confidence: 99%

Optimal Task and Motion Planning and Execution for Multiagent Systems in Dynamic Environments

Faroni

Umbrico

Beschi

et al. 2024

IEEE Trans. Cybern.

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Combining symbolic and geometric reasoning in multiagent systems is a challenging task that involves planning, scheduling, and synchronization problems. Existing works overlooked the variability of task duration and geometric feasibility intrinsic to these systems because of the interaction between agents and the environment. We propose a combined task and motion planning approach to optimize the sequencing, assignment, and execution of tasks under temporal and spatial variability. The framework relies on decoupling tasks and actions, where an action is one possible geometric realization of a symbolic task. At the task level, timeline-based planning deals with temporal constraints, duration variability, and synergic assignment of tasks. At the action level, online motion planning plans for the actual movements dealing with environmental changes. We demonstrate the approach's effectiveness in a collaborative manufacturing scenario, in which a robotic arm and a human worker shall assemble a mosaic in the shortest time possible. Compared with existing works, our approach applies to a broader range of applications and reduces the execution time of the process.

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Section: A Related Workmentioning

confidence: 99%

Optimal Task and Motion Planning and Execution for Multiagent Systems in Dynamic Environments

Faroni

Umbrico

Beschi

et al. 2024

IEEE Trans. Cybern.

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“…Currently, with the development of modern signal processing techniques and related theories and algorithms, non-collaborative communication is being used more and more widely in various fields [11][12][13]. In communication systems, different modulation methods are used for electronic communication signals to meet different user requirements and make full use of channel capacity [14].…”

Section: Introductionmentioning

confidence: 99%

Research on non-smooth signal identification algorithm for electronic communication in a two-way relay network

Liu,

Liu

2023

Applied Mathematics and Nonlinear Sciences

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To better allow accurate identification of non-smooth signals in two-way relay networks. In this paper, an algorithm based on the two-way relay network system model, the RLS algorithm, is proposed. The algorithm introduces the recursive least squares principle algorithm to the basic cost function of nonsmooth signal identification, optimizes the signal identification model by exponential weighting, and uses the time-varying variance matrix and the optimal identification data separation matrix to make real-time estimates of the new cost function for obtaining nonsmooth signals. And a new index, the PI index, is introduced to define whether the non-stationary signal is stable and reliable. Through two sets of comparison tests, we can learn that the RLS algorithm based on the two-way relay network system is better than other algorithms in the identification of non-stationary signals. It shows that the RLS algorithm can better identify and manage the information flow in a two-way relay network, make the non-stationary signal more stable, and further improve the convergence and convergence speed of the non-stationary signal to provide a better information interaction experience for both sides of electronic communication.

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Risk-Aware Task Sequencing for Human-Robot Collaboration

Bonini,

Cesta,

Cialdea Mayer

et al. 2024

Lecture Notes in Mechanical Engineering

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A Unifying Framework for Human–Agent Collaborative Systems—Part II: Design Procedure and Application

Cited by 6 publications

References 51 publications

Optimal Task and Motion Planning and Execution for Multiagent Systems in Dynamic Environments

Optimal Task and Motion Planning and Execution for Multiagent Systems in Dynamic Environments

Research on non-smooth signal identification algorithm for electronic communication in a two-way relay network

Risk-Aware Task Sequencing for Human-Robot Collaboration

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