Browsing by Author "Magnusson, Martin"

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Benchmarking the utility of maps of dynamics for human-aware motion planning
(2022-11-02) Swaminathan, Chittaranjan Srinivas; Kucner, Tomasz Piotr; Magnusson, Martin; Palmieri, Luigi; Molina, Sergi; Mannucci, Anna; Pecora, Federico; Lilienthal, Achim J.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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.
Optical far-field extinction of a single GaAs nanowire towards in situ size control of aerotaxy nanowire growth
(2020-01-09) Chen, Yang; Anttu, Nicklas; Sivakumar, Sudhakar; Gompou, Eleni; Magnusson, Martin
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
A substrate-free approach of semiconductor nanowire growth has been achieved by the aerotaxy technique previously. In this work, we propose an in situ method to monitor the size of nanowires through non-destructive optical-extinction measurements. Our work aims to build a theoretical look-up database of extinction spectra for a single nanowire of varying dimensions. We describe the origin of possible peaks in the spectra, for example due to nanowire-length dependent Fabry–Perot resonances and nanowire-diameter dependent TM and TE mode resonances. Furthermore, we show that the Au catalyst on top of the nanowire can be ignored in the simulations when the volume of the nanowire is an order of magnitude larger than that of the Au catalyst and the diameter is small compared to the incident wavelength. For the calculation of the extinction spectra, we use the finite element method, the discrete dipole approximation and the Mie theory. To compare with experimental measurements of randomly oriented nanowires, we perform an averaging over nanowire orientation for the modeled results. However, in the experiments, nanowires are accumulating on the quartz window of the measurement setup, which leads to increasing uncertainty in the comparison with the experimental extinction spectra. This uncertainty can be eliminated by considering both a sparse and a dense collection of nanowires on the quartz window in the optical simulations. Finally, we create a database of extinction spectra for a GaAs nanowire of varying diameters and lengths. This database can be used to estimate the diameter and the length of the nanowires by comparing the position of a peak and the peak-to-shoulder difference in the extinction spectrum. Possible tapering of nanowires can be monitored through the appearance of an additional peak at a wavelength of 700–800 nm.
Survey of maps of dynamics for mobile robots
(2023-09) Kucner, Tomasz Piotr; Magnusson, Martin; Mghames, Sariah; Palmieri, Luigi; Verdoja, Francesco; Swaminathan, Chittaranjan Srinivas; Krajník, Tomáš; Schaffernicht, Erik; Bellotto, Nicola; Hanheide, Marc; Lilienthal, Achim J
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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.
THÖR-MAGNI: A large-scale indoor motion capture recording of human movement and robot interaction
(2024) Schreiter, Tim; Rodrigues de Almeida, Tiago; Zhu, Yufei; Gutierrez Maestro, Eduardo; Morillo-Mendez, Lucas; Rudenko, Andrey; Palmieri, Luigi; Kucner, Tomasz P.; Magnusson, Martin; Lilienthal, Achim J.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
We present a new large dataset of indoor human and robot navigation and interaction, called THÖR-MAGNI, that is designed to facilitate research on social human navigation: for example, modeling and predicting human motion, analyzing goal-oriented interactions between humans and robots, and investigating visual attention in a social interaction context. THÖR-MAGNI was created to fill a gap in available datasets for human motion analysis and HRI. This gap is characterized by a lack of comprehensive inclusion of exogenous factors and essential target agent cues, which hinders the development of robust models capable of capturing the relationship between contextual cues and human behavior in different scenarios. Unlike existing datasets, THÖR-MAGNI includes a broader set of contextual features and offers multiple scenario variations to facilitate factor isolation. The dataset includes many social human–human and human–robot interaction scenarios, rich context annotations, and multi-modal data, such as walking trajectories, gaze-tracking data, and lidar and camera streams recorded from a mobile robot. We also provide a set of tools for visualization and processing of the recorded data. THÖR-MAGNI is, to the best of our knowledge, unique in the amount and diversity of sensor data collected in a contextualized and socially dynamic environment, capturing natural human–robot interactions.
Trajectory Prediction for Heterogeneous Agents A Performance Analysis on Small and Imbalanced Datasets
(2024-07-01) de Almeida, Tiago Rodrigues; Zhu, Yufei; Rudenko, Andrey; Kucner, Tomasz P.; Stork, Johannes A.; Magnusson, Martin; Lilienthal, Achim J.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Robots and other intelligent systems navigating in complex dynamic environments should predict future actions and intentions of surrounding agents to reach their goals efficiently and avoid collisions. The dynamics of those agents strongly depends on their tasks, roles, or observable labels. Class-conditioned motion prediction is thus an appealing way to reduce forecast uncertainty and get more accurate predictions for heterogeneous agents. However, this is hardly explored in the prior art, especially for mobile robots and in limited data applications. In this paper, we analyse different class-conditioned trajectory prediction methods on two datasets. We propose a set of conditional pattern-based and efficient deep learning-based baselines, and evaluate their performance on robotics and outdoors datasets (THÖR-MAGNI and Stanford Drone Dataset). Our experiments show that all methods improve accuracy in most of the settings when considering class labels. More importantly, we observe that there are significant differences when learning from imbalanced datasets, or in new environments where sufficient data is not available. In particular, we find that deep learning methods perform better on balanced datasets, but in applications with limited data, e.g., cold start of a robot in a new environment, or imbalanced classes, pattern-based methods may be preferable.