Consideration of communication in human–machine interaction for cooperative trajectory planning

Interactive human-machine systems aim to significantly enhance performance and reduce human workload by leveraging the combined strengths of humans and automated systems. In the state of the art, human-machine cooperation (HMC) systems are modeled in various interaction layers, e.g., the decision layer, trajectory layer, and action layer. The literature usually focuses on the action layer, assuming that there is no need for a consensus at the decision or trajectory layers. Only few studies deal with the interaction at the trajectory layer. None of the previous work has systematically examined the structure of communication for interaction between humans and machines beyond the action layer. Therefore, this paper proposes a graph representation based on a multi-agent system theory for human-machine cooperation. For this purpose, a layer model for human-machine cooperation from the literature is converted into a graph representation. Using our novel graph representation, the existence of communication loops can be demonstrated, which are necessary for emancipated cooperation. In contrast, a leader-follower structure does not possess a closed loop in this graph representation. The choice of the communication loop for emancipated cooperation is ambiguous and can take place via various closed loops at higher layers of human-machine interactions, which open new possibilities for the design of emancipated cooperative control systems. In a simulation, it is shown that emancipated cooperation is possible via three variants of communication loops and that a consensus on a common trajectory is found in each case. The results indicate that taking into account cooperative strategies at the trajectory layer can enhance the performance and effectiveness of human-machine systems.

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Publication Details

Journal
Frontiers in Robotics and AI
Published
2025-05-06
DOI
https://doi.org/10.3389/frobt.2025.1568402
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
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article

Consideration of communication in human–machine interaction for cooperative trajectory planning

Bálint Varga, Sören Hohmann, Julian Schneider
Frontiers in Robotics and AI
Robot Manipulation and Learning
article

Consideration of communication in human–machine interaction for cooperative trajectory planning

Bálint Varga, Sören Hohmann, Julian Schneider
article en

Abstract

Interactive human-machine systems aim to significantly enhance performance and reduce human workload by leveraging the combined strengths of humans and automated systems. In the state of the art, human-machine cooperation (HMC) systems are modeled in various interaction layers, e.g., the decision layer, trajectory layer, and action layer. The literature usually focuses on the action layer, assuming that there is no need for a consensus at the decision or trajectory layers. Only few studies deal with the interaction at the trajectory layer. None of the previous work has systematically examined the structure of communication for interaction between humans and machines beyond the action layer. Therefore, this paper proposes a graph representation based on a multi-agent system theory for human-machine cooperation. For this purpose, a layer model for human-machine cooperation from the literature is converted into a graph representation. Using our novel graph representation, the existence of communication loops can be demonstrated, which are necessary for emancipated cooperation. In contrast, a leader-follower structure does not possess a closed loop in this graph representation. The choice of the communication loop for emancipated cooperation is ambiguous and can take place via various closed loops at higher layers of human-machine interactions, which open new possibilities for the design of emancipated cooperative control systems. In a simulation, it is shown that emancipated cooperation is possible via three variants of communication loops and that a consensus on a common trajectory is found in each case. The results indicate that taking into account cooperative strategies at the trajectory layer can enhance the performance and effectiveness of human-machine systems.

Frontiers in Robotics and AIVol. 12
Karlsruhe Institute of Technology (DE)
Openalex Percentile: Top 90%
Robot Manipulation and Learning
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Consideration of communication in human–machine interaction for cooperative trajectory planning — Bálint Varga, Sören Hohmann, et al. · Frontiers in Robotics and AI (2025) | TGRS Research Map | TGRS