Sensing, Modelling and Using Spatial Motion Patterns for Dynamics-Aware Mobile Robots

Mobile robots are increasingly being deployed outside of labs and in real-world applications. This makes it ever more important to explicitly account for the dynamics present in real-world environments over longer timescales. The dynamic nature of the world faced by robots notably includes flows and activity patterns of people, vehicles, and other robots, as well as dynamic and semi-static objects; but also flows of air and water currents, e.g., in applications of air and surface vessels. Constructing, and making use of, representations that explicitly model dynamics can facilitate both safer and more efficient robots, and also better understanding and predictions of flows and other spatiotemporal patterns.

There are several open research questions related to dynamics awareness for mobile robots. These involve, among many other things, studying representations of dynamics in terms of efficiency, accuracy, expressiveness, and usability; exploring how these representations can be used to facilitate dynamics-aware planning and human–robot spatial interaction; active perception and other measures for improving and maintaining the learned representations during long-term operation; as well as using robots, possibly integrated into stationary sensor networks, for building models of flow for, e.g., ventilation monitoring and surveillance.

The aim of this Special Issue is to bring out and highlight contributions in modeling, exploring, and exploiting information about the spatiotemporal patterns that govern dynamics, especially with applications in mobile robotics.

Therefore, prospective authors are invited to submit original research contributions or survey articles for review and publication in the Sensors open access journal. Topics of interest include (but are not limited to) the following:

• Maps of dynamics and flow;
• Perception of spatial dynamics;
• Dynamics-aware localization;
• Dynamics-aware motion planning;
• Dynamics-aware coordination;
• Dynamics-aware reasoning;
• Planning for dynamics perception;
• Active perception and exploration for dynamics;
• Dynamics-awareness for human–robot spatial interaction;
• Dynamics-awareness for motion prediction;
• Surveillance and crowd analysis.

Dr. Martin Magnusson
Dr. Tomasz Piotr Kucner
Prof. Dr. Achim J. Lilienthal
Guest Editors

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