Time-varying Pedestrian Flow Models for Service Robots

We present a human-centric spatiotemporal 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 for several weeks, we compare the model to state-of-the-art methods for pedestrian flow modelling

Towards Safer Robot Motion: Using a Qualitative Motion Model to Classify Human-Robot Spatial Interaction

For adoption of Autonomous Mobile Robots (AMR) across a breadth of industries, they must navigate around humans in a way which is safe and which humans perceive as safe, but without greatly compromising efficiency. This work aims to classify the Human-Robot Spatial Interaction (HRSI) situation of an interacting human and robot, to be applied in Human-Aware Navigation (HAN) to account for situational context. We develop qualitative probabilistic models of relative human and robot movements in various HRSI situations to classify situations, and explain our plan to develop per-situation probabilistic models of socially legible HRSI to predict human and robot movement. In future work we aim to use these predictions to generate qualitative constraints in the form of metric cost-maps for local robot motion planners, enforcing more efficient and socially legible trajectories which are both physically safe and perceived as safe

Non-Parametric Modeling of Spatio-Temporal Human Activity Based on Mobile Robot Observations

This work presents a non-parametric spatiotemporal model for mapping human activity by mobile autonomous robots in a long-term context. Based on Variational Gaussian Process Regression, the model incorporates prior information of spatial and temporal-periodic dependencies to create a continuous representation of human occurrences. The inhomogeneous data distribution resulting from movements of the robot is included in the model via a heteroscedastic likelihood function and can be accounted for as predictive uncertainty. Using a sparse formulation, data sets over multiple weeks and several hundred square meters can be used for model creation. The experimental evaluation, based on multi-week data sets, demonstrates that the proposed approach outperforms the state of the art both in terms of predictive quality and subsequent path planning.© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Robotic Exploration for Learning Human Motion Patterns

Understanding how people are likely to move is key to efficient and safe robot navigation in human environments. However, mobile robots can only observe a fraction of the environment at a time, while the activity patterns of people may also change at different times. This paper introduces a new methodology for mobile robot exploration to maximise the knowledge of human activity patterns by deciding where and when to collect observations. We introduce an exploration policy driven by the entropy levels in a spatio-temporal map of pedestrian flows, and compare multiple spatio-temporal exploration strategies including both informed and uninformed approaches. The evaluation is performed by simulating mobile robot exploration using real sensory data from three long-term pedestrian datasets. The results show that for certain scenarios the models built with proposed exploration system can better predict the flow patterns than uninformed strategies, allowing the robot to move in a more socially compliant way, and that the exploration ratio is a key factor when it comes to the model prediction accuracy

Survey of maps of dynamics for mobile robots

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

Exploration and Mapping of Spatio-Temporal Pedestrian Flow Patterns for Mobile Robots

Socially compliant robot navigation is one of the key aspects for long-term acceptance of mobile robots in human-populated environments. One of the current barriers for this acceptance is that many navigation methods are based only on reactive behaviours, which can lead to frequent re-plannings, causing an erratic or aggressive robot behaviour. Instead, giving the ability to model and predict in advance how the people are likely to behave, from a long-term perspective, is an important enabler for safe and efficient navigation. For example, a robot may use its knowledge of the expected human motion to go with the main direction of flow to minimise the possibility of collisions or trajectory re-plannings. In order to provide robots with knowledge of the expected activity patterns of people at different places and times,the first main contribution of this thesis is the introduction of a Spatio-Temporal Flow map (STeF-map). This is a time-dependent probabilistic map able to model and predict the flow patterns of people in the environment. The proposed representation models the likelihood of motion directions on a grid-based map by a set of harmonic functions, which efficiently capture long-term variations of crowd movements over time. The experimental evaluation shows that the proposed model enables a better human motion prediction than spatial-only approaches and an increased capacity for socially compliant robot navigation. Obtaining this knowledge from a mobile robot platform is, however, not a trivial task, as usually they can only observe a fraction of the environment at a time, while the activity patterns of people may also change at different times. Therefore, the second main contribution is the investigation of a new methodology for mobile robot exploration to maximise the knowledge of human activity patterns, by deciding where and when to collect observations based on an exploration policy driven by the entropy levels in a spatio-temporal map. The evaluation is performed by simulating mobile robot exploration using real sensory data from three long-term pedestrian datasets, and the results show that for certain scenarios, the proposed exploration system can learn STeF-maps more quickly and better predict the flow patterns than uninformed strategies