22,535 research outputs found
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
MORPH: A Reference Architecture for Configuration and Behaviour Self-Adaptation
An architectural approach to self-adaptive systems involves runtime change of
system configuration (i.e., the system's components, their bindings and
operational parameters) and behaviour update (i.e., component orchestration).
Thus, dynamic reconfiguration and discrete event control theory are at the
heart of architectural adaptation. Although controlling configuration and
behaviour at runtime has been discussed and applied to architectural
adaptation, architectures for self-adaptive systems often compound these two
aspects reducing the potential for adaptability. In this paper we propose a
reference architecture that allows for coordinated yet transparent and
independent adaptation of system configuration and behaviour
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
A biologically inspired meta-control navigation system for the Psikharpax rat robot
A biologically inspired navigation system for the mobile rat-like robot named Psikharpax is presented, allowing for self-localization and autonomous navigation in an initially unknown environment. The ability of parts of the model (e. g. the strategy selection mechanism) to reproduce rat behavioral data in various maze tasks has been validated before in simulations. But the capacity of the model to work on a real robot platform had not been tested. This paper presents our work on the implementation on the Psikharpax robot of two independent navigation strategies (a place-based planning strategy and a cue-guided taxon strategy) and a strategy selection meta-controller. We show how our robot can memorize which was the optimal strategy in each situation, by means of a reinforcement learning algorithm. Moreover, a context detector enables the controller to quickly adapt to changes in the environment-recognized as new contexts-and to restore previously acquired strategy preferences when a previously experienced context is recognized. This produces adaptivity closer to rat behavioral performance and constitutes a computational proposition of the role of the rat prefrontal cortex in strategy shifting. Moreover, such a brain-inspired meta-controller may provide an advancement for learning architectures in robotics
Towards Full Automated Drive in Urban Environments: A Demonstration in GoMentum Station, California
Each year, millions of motor vehicle traffic accidents all over the world
cause a large number of fatalities, injuries and significant material loss.
Automated Driving (AD) has potential to drastically reduce such accidents. In
this work, we focus on the technical challenges that arise from AD in urban
environments. We present the overall architecture of an AD system and describe
in detail the perception and planning modules. The AD system, built on a
modified Acura RLX, was demonstrated in a course in GoMentum Station in
California. We demonstrated autonomous handling of 4 scenarios: traffic lights,
cross-traffic at intersections, construction zones and pedestrians. The AD
vehicle displayed safe behavior and performed consistently in repeated
demonstrations with slight variations in conditions. Overall, we completed 44
runs, encompassing 110km of automated driving with only 3 cases where the
driver intervened the control of the vehicle, mostly due to error in GPS
positioning. Our demonstration showed that robust and consistent behavior in
urban scenarios is possible, yet more investigation is necessary for full scale
roll-out on public roads.Comment: Accepted to Intelligent Vehicles Conference (IV 2017
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