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Swaminathan, Chittaranjan SrinivasORCID iD iconorcid.org/0000-0002-9545-9871
Publications (10 of 18) Show all publications
Kucner, T. P., Magnusson, M., Mghames, S., Palmieri, L., Verdoja, F., Swaminathan, C. S., . . . Lilienthal, A. J. (2023). Survey of maps of dynamics for mobile robots. The international journal of robotics research, 42(11), 977-1006
Open this publication in new window or tab >>Survey of maps of dynamics for mobile robots
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2023 (English)In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 42, no 11, p. 977-1006Article in journal (Refereed) Published
Abstract [en]

Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area.

Place, publisher, year, edition, pages
Sage Publications, 2023
Keywords
mapping, maps of dynamics, localization and mapping, acceptability and trust, human-robot interaction, human-aware motion planning
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-107930 (URN)10.1177/02783649231190428 (DOI)001042374800001 ()2-s2.0-85166946627 (Scopus ID)
Funder
EU, Horizon 2020, 101017274
Note

Funding agencies:

Czech Ministry of Education by OP VVV CZ.02.1.01/0.0/0.0/16 019/0000765

Business Finland 9249/31/2021

 

Available from: 2023-08-30 Created: 2023-08-30 Last updated: 2024-01-03Bibliographically approved
Molina, S., Mannucci, A., Magnusson, M., Adolfsson, D., Andreasson, H., Hamad, M., . . . Lilienthal, A. J. (2023). The ILIAD Safety Stack: Human-Aware Infrastructure-Free Navigation of Industrial Mobile Robots. IEEE robotics & automation magazine
Open this publication in new window or tab >>The ILIAD Safety Stack: Human-Aware Infrastructure-Free Navigation of Industrial Mobile Robots
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2023 (English)In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223XArticle in journal (Refereed) Epub ahead of print
Abstract [en]

Current intralogistics services require keeping up with e-commerce demands, reducing delivery times and waste, and increasing overall flexibility. As a consequence, the use of automated guided vehicles (AGVs) and, more recently, autonomous mobile robots (AMRs) for logistics operations is steadily increasing.

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Robots, Safety, Navigation, Mobile robots, Human-robot interaction, Hidden Markov models, Trajectory
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-108145 (URN)10.1109/MRA.2023.3296983 (DOI)001051249900001 ()
Funder
EU, Horizon 2020, 732737
Available from: 2023-09-14 Created: 2023-09-14 Last updated: 2024-04-23Bibliographically approved
Swaminathan, C. S., Kucner, T. P., Magnusson, M., Palmieri, L., Molina, S., Mannucci, A., . . . Lilienthal, A. J. (2022). Benchmarking the utility of maps of dynamics for human-aware motion planning. Frontiers in Robotics and AI, 9, Article ID 916153.
Open this publication in new window or tab >>Benchmarking the utility of maps of dynamics for human-aware motion planning
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2022 (English)In: Frontiers in Robotics and AI, E-ISSN 2296-9144, Vol. 9, article id 916153Article in journal (Refereed) Published
Abstract [en]

Robots operating with humans in highly dynamic environments need not only react to moving persons and objects but also to anticipate and adhere to patterns of motion of dynamic agents in their environment. Currently, robotic systems use information about dynamics locally, through tracking and predicting motion within their direct perceptual range. This limits robots to reactive response to observed motion and to short-term predictions in their immediate vicinity. In this paper, we explore how maps of dynamics (MoDs) that provide information about motion patterns outside of the direct perceptual range of the robot can be used in motion planning to improve the behaviour of a robot in a dynamic environment. We formulate cost functions for four MoD representations to be used in any optimizing motion planning framework. Further, to evaluate the performance gain through using MoDs in motion planning, we design objective metrics, and we introduce a simulation framework for rapid benchmarking. We find that planners that utilize MoDs waste less time waiting for pedestrians, compared to planners that use geometric information alone. In particular, planners utilizing both intensity (proportion of observations at a grid cell where a dynamic entity was detected) and direction information have better task execution efficiency.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2022
Keywords
ATC, benchmarking, dynamic environments, human-aware motion planning, human-populated environments, maps of dynamics
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-102370 (URN)10.3389/frobt.2022.916153 (DOI)000885477300001 ()36405073 (PubMedID)2-s2.0-85142125253 (Scopus ID)
Funder
European Commission, 101017274
Available from: 2022-11-24 Created: 2022-11-24 Last updated: 2022-12-20Bibliographically approved
Rudenko, A., Kucner, T. P., Swaminathan, C. S., Chadalavada, R. T., Arras, K. O. & Lilienthal, A. (2020). Benchmarking Human Motion Prediction Methods. In: : . Paper presented at HRI 2020, Workshop on Test Methods and Metrics for Effective HRI in Real World Human-Robot Teams, Cambridge, UK,(Conference cancelled).
Open this publication in new window or tab >>Benchmarking Human Motion Prediction Methods
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2020 (English)Conference paper, Oral presentation only (Other academic)
Abstract [en]

In this extended abstract we present a novel dataset for benchmarking motion prediction algorithms. We describe our approach to data collection which generates diverse and accurate human motion in a controlled weakly-scripted setup. We also give insights for building a universal benchmark for motion prediction.

Keywords
human motion prediction, benchmarking, datasets
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-89169 (URN)
Conference
HRI 2020, Workshop on Test Methods and Metrics for Effective HRI in Real World Human-Robot Teams, Cambridge, UK,(Conference cancelled)
Projects
ILIAD
Available from: 2021-02-01 Created: 2021-02-01 Last updated: 2021-02-02Bibliographically approved
Kucner, T. P., Lilienthal, A., Magnusson, M., Palmieri, L. & Swaminathan, C. S. (2020). Closing Remarks. In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots: (pp. 143-151). Springer
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2020 (English)In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, p. 143-151Chapter in book (Refereed)
Abstract [en]

Dynamics is an inherent feature of reality. In spite of that, the domain of maps of dynamics has not received a lot of attention yet. In this book, we present solutions for building maps of dynamics and outline how to make use of them for motion planning. In this chapter, we present discuss related research question that as of yet remain to be answered, and derive possible future research directions. 

Place, publisher, year, edition, pages
Springer, 2020
Series
Cognitive Systems Monographs, ISSN 1867-4925 ; 40
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-81667 (URN)10.1007/978-3-030-41808-3_6 (DOI)2-s2.0-85083964746 (Scopus ID)978-3-030-41807-6 (ISBN)978-3-030-41808-3 (ISBN)
Available from: 2020-05-13 Created: 2020-05-13 Last updated: 2020-05-13Bibliographically approved
Kucner, T. P., Lilienthal, A., Magnusson, M., Palmieri, L. & Swaminathan, C. S. (2020). Introduction. In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots: (pp. 1-13). Springer
Open this publication in new window or tab >>Introduction
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2020 (English)In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, p. 1-13Chapter in book (Refereed)
Abstract [en]

Change and motion are inherent features of reality. The ability to recognise patterns governing changes has allowed humans to thrive in a dynamic reality. Similarly, dynamics awareness can also improve the performance of robots. Dynamics awareness is an umbrella term covering a broad spectrum of concepts. In this chapter, we present the key aspects of dynamics awareness. We introduce two motivating examples presenting the challenges for robots operating in a dynamic environment. We discuss the benefits of using spatial models of dynamics and analyse the challenges of building such models.

Place, publisher, year, edition, pages
Springer, 2020
Series
Cognitive Systems Monographs, ISSN 1867-4925 ; 40
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-81665 (URN)10.1007/978-3-030-41808-3_1 (DOI)2-s2.0-85083992773 (Scopus ID)978-3-030-41807-6 (ISBN)978-3-030-41808-3 (ISBN)
Available from: 2020-05-13 Created: 2020-05-13 Last updated: 2020-05-13Bibliographically approved
Kucner, T. P., Lilienthal, A., Magnusson, M., Palmieri, L. & Swaminathan, C. S. (2020). Maps of Dynamics. In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots: (pp. 15-32). Springer
Open this publication in new window or tab >>Maps of Dynamics
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2020 (English)In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, p. 15-32Chapter in book (Refereed)
Abstract [en]

The task of building maps of dynamics is the key focus of this book, as well as how to use them for motion planning. In this chapter, we present a categorisation and overview of different types of maps of dynamics. Furthermore, we give an overview of approaches to motion planning in dynamic environments, with a focus on motion planning over maps of dynamics. 

Place, publisher, year, edition, pages
Springer, 2020
Series
Cognitive Systems Monographs, ISSN 1867-4925
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-81670 (URN)10.1007/978-3-030-41808-3_2 (DOI)2-s2.0-85083956964 (Scopus ID)978-3-030-41807-6 (ISBN)978-3-030-41808-3 (ISBN)
Available from: 2020-05-13 Created: 2020-05-13 Last updated: 2020-05-13Bibliographically approved
Kucner, T. P., Lilienthal, A., Magnusson, M., Palmieri, L. & Swaminathan, C. S. (2020). Modelling Motion Patterns with Circular-Linear Flow Field Maps. In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots: (pp. 65-113). Springer
Open this publication in new window or tab >>Modelling Motion Patterns with Circular-Linear Flow Field Maps
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2020 (English)In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, p. 65-113Chapter in book (Refereed)
Abstract [en]

The shared feature of the flow of discrete objects and continuous media is that they both can be represented as velocity vectors encapsulating direction and speed of motion. In this chapter, we present a method for modelling the flow of discrete objects and continuous media as continuous Gaussian mixture fields. The proposed model associates to each part of the environment a Gaussian mixture model describing the local motion patterns. We also present a learning method, designed to build the model from a set of sparse, noisy and incomplete observations. 

Place, publisher, year, edition, pages
Springer, 2020
Series
Cognitive Systems Monographs, ISSN 1867-4925 ; 40
National Category
Fluid Mechanics and Acoustics
Identifiers
urn:nbn:se:oru:diva-81664 (URN)10.1007/978-3-030-41808-3_4 (DOI)2-s2.0-85084011370 (Scopus ID)978-3-030-41807-6 (ISBN)978-3-030-41808-3 (ISBN)
Available from: 2020-05-12 Created: 2020-05-12 Last updated: 2020-05-12Bibliographically approved
Kucner, T. P., Lilienthal, A., Magnusson, M., Palmieri, L. & Swaminathan, C. S. (2020). Modelling Motion Patterns with Conditional Transition Map. In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots: (pp. 33-64). Springer
Open this publication in new window or tab >>Modelling Motion Patterns with Conditional Transition Map
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2020 (English)In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, p. 33-64Chapter in book (Refereed)
Abstract [en]

The key idea of modelling flow of discrete objects is to capture the way they move through the environment. One method to capture the flow is to observe changes in occupancy caused by the motion of discrete objects. In this chapter, we present a method to model and learn occupancy shifts caused by an object moving through the environment. The key idea is observe temporal changes changes in the occupancy of adjacent cells, and based on the temporal offset infer the direction of the occupancy flow.

Place, publisher, year, edition, pages
Springer, 2020
Series
Cognitive Systems Monographs, ISSN 1867-4925 ; 40
National Category
Oceanography, Hydrology and Water Resources
Identifiers
urn:nbn:se:oru:diva-81669 (URN)10.1007/978-3-030-41808-3_3 (DOI)2-s2.0-85083960053 (Scopus ID)978-3-030-41807-6 (ISBN)978-3-030-41808-3 (ISBN)
Available from: 2020-05-13 Created: 2020-05-13 Last updated: 2020-05-13Bibliographically approved
Kucner, T. P., Lilienthal, A., Magnusson, M., Palmieri, L. & Swaminathan, C. S. (2020). Motion Planning Using MoDs. In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots: (pp. 115-141). Springer
Open this publication in new window or tab >>Motion Planning Using MoDs
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2020 (English)In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, p. 115-141Chapter in book (Refereed)
Abstract [en]

Maps of dynamics can be beneficial for motion planning. Information about motion patterns in the environment can lead to finding flow-aware paths, allowing robots to align better to the expected motion: either of other agents in the environment or the flow of air or another medium. The key idea of flow-aware motion planning is to include adherence to the flow represented in the MoD into the motion planning algorithm’s sub-units (i.e. cost function, sampling mechanism), thereby biasing the motion planner into obeying local and implicit traffic rules. 

Place, publisher, year, edition, pages
Springer, 2020
Series
Cognitive Systems Monographs, ISSN 1867-4925 ; 40
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-81668 (URN)10.1007/978-3-030-41808-3_5 (DOI)2-s2.0-85083963960 (Scopus ID)978-3-030-41807-6 (ISBN)978-3-030-41808-3 (ISBN)
Available from: 2020-05-13 Created: 2020-05-13 Last updated: 2020-05-13Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0002-9545-9871

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