Tadele B. Tuli

selected publications

1. SORO

   Telepresence Mobile Robots Design and Control for Social Interaction

   Tuli, Tadele Belay, Terefe, Tesfaye Olana, and Rashid, Md Mamun Ur

   International Journal of Social Robotics Jul 2020

   Abstract Bibtex DOI

   Human–robot interaction has extended its application horizon to simplify how human beings interact with each other through a remotely controlled telepresence robot. The fast growth of communication technologies such as 4G and 5G has elevated the potential to establish stable audio–video-data transmission. However, human–robot physical interactions are still challenging regarding maneuverability, controllability, stability, drive layout, and autonomy. Hence, this paper presents a systematic design and control approach based on the customer’s needs and expectations of telepresence mobile robots for social interactions. A system model and controller design are developed using the Lagrangian method and linear quadratic regulator, respectively, for different scenarios such as flat surface, inclined surface, and yaw (steering). The robot system is capable of traveling uphill (30\{^{}circ }\\∘) and has a variable height (600–1200 mm). The robot is advantageous in developing countries to fill the skill gaps as well as for sharing knowledge and expertise using a virtual and mobile physical presence.

   @article{tuli_telepresence_2020,
     abbr = {SORO},
     title = {Telepresence {Mobile} {Robots} {Design} and {Control} for {Social} {Interaction}},
     issn = {1875-4805},
     url = {https://doi.org/10.1007/s12369-020-00676-3},
     doi = {10.1007/s12369-020-00676-3},
     language = {en},
     urldate = {2020-09-14},
     journal = {International Journal of Social Robotics},
     author = {Tuli, Tadele Belay and Terefe, Tesfaye Olana and Rashid, Md Mamun Ur},
     month = jul,
     year = {2020},
     bibtex_show = true,
     selected = true
   }

2. CIRP CMS

   Identifying human intention during assembly operations using wearable motion capturing systems including eye focus

   Manns, Martin, Tuli, Tadele Belay, and Schreiber, Florian

   Procedia CIRP Jan 2021

   Abstract Bibtex DOI

   Simulating human motion behavior in assembly operations helps to create efficient collaboration plans for humans and robots. However, identifying human intention may require high quality human motion capture data in order to discriminate micro-actions and human attention. In this regard, a human motion capture setup that combines various systems such as joint body, finger, and eye trackers is proposed in combination with a methodology of identifying the intention of human operators as well as for predicting sequences of activities. The approach may lead to safer human-robot collaboration.

   @article{manns_identifying_2021,
     series = {54th {CIRP} {CMS} 2021 - {Towards} {Digitalized} {Manufacturing} 4.0},
     title = {Identifying human intention during assembly operations using wearable motion capturing systems including eye focus},
     volume = {104},
     issn = {2212-8271},
     url = {https://www.sciencedirect.com/science/article/pii/S2212827121010532},
     doi = {10.1016/j.procir.2021.11.155},
     language = {en},
     urldate = {2021-11-29},
     journal = {Procedia CIRP},
     author = {Manns, Martin and Tuli, Tadele Belay and Schreiber, Florian},
     month = jan,
     year = {2021},
     keywords = {human-robot collaboration, Operator 4.0, Eye tracker, human motion capture},
     pages = {924--929},
     bibtex_show = true,
     abbr = {CIRP CMS},
     selected = true
   }

3. IEEE ETFA

   Knowledge-Based Digital Twin for Predicting Interactions in Human-Robot Collaboration

   Tuli, Tadele Belay, Kohl, Linus, Chala, Sisay Adugna, Manns, Martin, and Ansari, Fazel

   In 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ) Sep 2021

   Abstract Bibtex DOI

   Semantic representation of motions in a human-robot collaborative environment is essential for agile design and development of digital twins (DT) towards ensuring efficient collaboration between humans and robots in hybrid work systems, e.g., in assembly operations. Dividing activities into actions helps to further conceptualize motion models for predicting what a human intends to do in a hybrid work system. However, it is not straightforward to identify human intentions in collaborative operations for robots to understand and collaborate. This paper presents a concept for semantic representation of human actions and intention prediction using a flexible task ontology interface in the semantic data hub stored in a domain knowledge base. This semantic data hub enables the construction of a DT with corresponding reasoning and simulation algorithms. Furthermore, a knowledge-based DT concept is used to analyze and verify the presented use-case of Human-Robot Collaboration in assembly operations. The preliminary evaluation showed a promising reduction of time for assembly tasks, which identifies the potential to i) improve efficiency reflected by reducing costs and errors and ultimately ii) assist human workers in improving decision making. Thus the contribution of the current work involves a marriage of machine learning, robotics, and ontology engineering into DT to improve human-robot interaction and productivity in a collaborative production environment.

   @inproceedings{tuli_knowledge-based_2021,
     title = {Knowledge-{Based} {Digital} {Twin} for {Predicting} {Interactions} in {Human}-{Robot} {Collaboration}},
     doi = {10.1109/ETFA45728.2021.9613342},
     booktitle = {2021 26th {IEEE} {International} {Conference} on {Emerging} {Technologies} and {Factory} {Automation} ({ETFA} )},
     author = {Tuli, Tadele Belay and Kohl, Linus and Chala, Sisay Adugna and Manns, Martin and Ansari, Fazel},
     month = sep,
     year = {2021},
     keywords = {Collaboration, digital twin, Digital twin, human action models, human-robot interaction, Knowledge based systems, machine learning, Ontologies, ontology, Predictive models, Productivity, Semantics},
     pages = {1--8},
     abbr = {IEEE ETFA},
     bibtex_show = true,
     selected = true,
     file = {2021130209.pdf}
   }

4. CIRP CARV Best paper award

   Understanding Shared Autonomy of Collaborative Humans Using Motion Capture System for Simulating Team Assembly

   Tuli, Tadele Belay, Manns, Martin, and Jonek, Michael

   In Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems 2022

   Abstract Bibtex PDF DOI

   In virtual production planning, simulating human motions helps to improve process planning and interaction efficiency. However, simulating multiple humans sharing tasks in a shared workplace requires understanding how human workers interact and share autonomy. In this regard, an Inertial Measurement Unit based motion capture is employed for understanding shifting roles and learning effects. Parameters such as total time, distance, and acceleration variances in repetition are considered for modeling collaborative motion interactions. The results distinguish motion patterns versus the undertaken interactions. This work may serve as an initial input to model interaction schemes and recognize human actions behavior during team assembly. Furthermore, the concept can be extended toward a human-robot shared autonomy.

   @inproceedings{tuli_understanding_2022,
     abbr = {CIRP CARV},
     award = {Best paper award},
     address = {Cham},
     series = {Lecture {Notes} in {Mechanical} {Engineering}},
     title = {Understanding {Shared} {Autonomy} of {Collaborative} {Humans} {Using} {Motion} {Capture} {System} for {Simulating} {Team} {Assembly}},
     isbn = {978-3-030-90700-6},
     doi = {10.1007/978-3-030-90700-6_59},
     language = {en},
     booktitle = {Towards {Sustainable} {Customization}: {Bridging} {Smart} {Products} and {Manufacturing} {Systems}},
     publisher = {Springer International Publishing},
     author = {Tuli, Tadele Belay and Manns, Martin and Jonek, Michael},
     editor = {Andersen, Ann-Louise and Andersen, Rasmus and Brunoe, Thomas Ditlev and Larsen, Maria Stoettrup Schioenning and Nielsen, Kjeld and Napoleone, Alessia and Kjeldgaard, Stefan},
     year = {2022},
     keywords = {Human motion capture, Shared autonomy, Team interaction, Manual assembly, Role shifting},
     pages = {527--534},
     bibtex_show = true,
     selected = true,
     pdf = {shared_autonomy_final.pdf}
   }

5. CIRP CMS

   Latent Space Based Collaborative Motion Modeling from Motion Capture Data for Human Robot Collaboration (accepted paper, Switzerland)

   Tuli, Tadele Belay, Henkel, Martin, and Manns, Martin

   In 2022

   Bibtex

   @inproceedings{tuli_latentSpace_cms_2022,
     abbr = {CIRP CMS},
     series = {55th CIRP CMS 2022 - Leading Manufacturing Systems Transformation},
     title = {Latent Space Based Collaborative Motion Modeling from Motion Capture Data for Human Robot Collaboration (accepted paper, Switzerland)},
     author = {Tuli, Tadele Belay and Henkel, Martin and Manns, Martin},
     year = {2022},
     journal = {Procedia CIRP},
     bibtex_show = true,
     selected = true
   }

6. CPSL

   Industrial Human Activity Prediction and Detection Using Sequential Memory Networks (accepted paper, Canada)

   Tuli, Tadele Belay, Patel, Valay Mukesh, and Manns, Martin

   In CPSL 2022 - Conference On Production Systems And Logistics) 2022

   Bibtex

   @inproceedings{tuli_HARNets_2022,
     abbr = {CPSL},
     author = {Tuli, Tadele Belay and Patel, Valay Mukesh and Manns, Martin},
     booktitle = {CPSL 2022 - Conference On Production Systems And Logistics)},
     title = {Industrial Human Activity Prediction and Detection Using Sequential Memory Networks  (accepted paper, Canada)},
     year = {2022},
     bibtex_show = true,
     selected = true
   }