Enabling Flow Awareness for Mobile Robots in Partially Observable Environments

Kucner, Tomasz Piotr; Magnusson, Martin; Schaffernicht, Erik; Bennetts, Victor Hernandez; Lilienthal, Achim J.

doi:10.1109/lra.2017.2660060

Cited by 48 publications

(54 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the future, it is possible to replace the von Mises distribution with a Gaussian distribution or replace the Bernoulli likelihood in [20] with a Gaussian likelihood to accurately model such spatial density estimations. 4) CLiFF-Map Model: Circular Linear Flow Field map (CLiFF-map) [33] is a technique for encoding patterns of movement as a field of Gaussian mixtures. They can be combined with semi-wrapped Guassian mixture models (SWGMM) to model multi-modal motion.…”

Section: B Evaluation Methodsologymentioning

confidence: 99%

“…Kucner et al also improved their approach in [11] and proposed a continuous representation in [32]. To model speed and direction of people, [33] introduced an expectation-maximisation scheme based on the Independent von MisesGaussian distributions [34]. They also showed that the model of the movement of people could be used to achieve more efficient navigation of the robot through human crowds [35].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Time-varying Pedestrian Flow Models for Service Robots

Vintr¹,

Molina

Senanayake

et al. 2019

2019 European Conference on Mobile Robots (ECMR)

Self Cite

View full text Add to dashboard Cite

We present a human-centric spatio-temporal model for service robots operating in densely populated environments for long time periods. The method integrates observations of pedestrians performed by a mobile robot at different locations and times into a memory efficient model, that represents the spatial layout of natural pedestrian flows and how they change over time. To represent temporal variations of the observed flows, our method does not model the time in a linear fashion, but by several dimensions wrapped into themselves. This representation of time can capture long-term (i.e. days to weeks) periodic patterns of peoples' routines and habits. Knowledge of these patterns allows making long-term predictions of future human presence and walking directions, which can support mobile robot navigation in human-populated environments. Using datasets gathered by a robot for several weeks, we compare the model to state-of-the-art methods for pedestrian flow modelling.

show abstract

Section: B Evaluation Methodsologymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Time-varying Pedestrian Flow Models for Service Robots

Vintr¹,

Molina

Senanayake

et al. 2019

2019 European Conference on Mobile Robots (ECMR)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Unlike the work of Ramos et al [25], which is aimed primarily at modelling the spatial structure, and [22], which aims to make short-term predictions of the motion of people, our aim is to create universal, spatial-temporal models capable of long-term predictions of various phenomena. Inspired by the ability of the continuous models [25], [22] to represent spatio-temporal phenomena and the predictive power of spectral representations [5], we propose a novel method which allows to introduce the notion of time into state-of-the-art spatial models used in mobile robotics. Unlike our previous work [5], which treats environmental states as independent despite their spatial proximity and is applicable to binary states only, the proposed method can be applied to continuous, multi-dimensional representations, e.g.…”

Section: Related Workmentioning

confidence: 99%

Warped Hypertime Representations for Long-Term Autonomy of Mobile Robots

Krajník

Vintr

Molina

et al. 2019

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

This paper presents a novel method for introducing time into discrete and continuous spatial representations used in mobile robotics, by modelling long-term, pseudoperiodic variations caused by human activities. Unlike previous approaches, the proposed method does not treat time and space separately, and its continuous nature respects both the temporal and spatial continuity of the modeled phenomena. The method extends the given spatial model with a set of wrapped dimensions that represent the periodicities of observed changes. By performing clustering over this extended representation, we obtain a model that allows us to predict future states of both discrete and continuous spatial representations. We apply the proposed algorithm to several long-term datasets and show that the method enables a robot to predict future states of representations with different dimensions. The experiments further show that the method achieves more accurate predictions than the previous state of the art.

show abstract

“…The Circular Linear Flow Field map (CLiFF-map) [10] describes motion patterns as a field of Gaussian mixtures whose local elements are probability distribution of (instantaneous) velocities V V V = (θ, ρ), where θ ∈ [0, 2π) is the orientation and ρ ∈ R + the speed. This is a heterogeneous vector with one circular random variable (θ) and one linear (ρ).…”

Section: Cliff-map Modelmentioning

confidence: 99%

“…Unlike [11,14,12,9] we use a more powerful probabilistic representation than vector fields named Circular Linear Flow Field (CLiFF) map [10]. It associates a Gaussian mixture model to each location whose components encode multiple weighted flow directions.…”

Section: Introductionmentioning

confidence: 99%

Kinodynamic motion planning on Gaussian mixture fields

Palmieri

Kucner

Magnusson

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

Self Cite

View full text Add to dashboard Cite

We present a mobile robot motion planning approach under kinodynamic constraints that exploits learned perception priors in the form of continuous Gaussian mixture fields. Our Gaussian mixture fields are statistical multi-modal motion models of discrete objects or continuous media in the environment that encode e.g. the dynamics of air or pedestrian flows. We approach this task using a recently proposed circular linear flow field map based on semi-wrapped GMMs whose mixture components guide sampling and rewiring in an RRT* algorithm using a steer function for non-holonomic mobile robots. In our experiments with three alternative baselines, we show that this combination allows the planner to very efficiently generate high-quality solutions in terms of path smoothness, path length as well as natural yet minimum control effort motions through multi-modal representations of Gaussian mixture fields.

show abstract

Enabling Flow Awareness for Mobile Robots in Partially Observable Environments

Abstract: This is the accepted version of a paper published in IEEE Robotics and Automation Letters. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination.

Cited by 48 publications

References 21 publications

Time-varying Pedestrian Flow Models for Service Robots

Time-varying Pedestrian Flow Models for Service Robots

Warped Hypertime Representations for Long-Term Autonomy of Mobile Robots

Kinodynamic motion planning on Gaussian mixture fields

Contact Info

Product

Resources

About