9,392 research outputs found
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
Route Swarm: Wireless Network Optimization through Mobility
In this paper, we demonstrate a novel hybrid architecture for coordinating
networked robots in sensing and information routing applications. The proposed
INformation and Sensing driven PhysIcally REconfigurable robotic network
(INSPIRE), consists of a Physical Control Plane (PCP) which commands agent
position, and an Information Control Plane (ICP) which regulates information
flow towards communication/sensing objectives. We describe an instantiation
where a mobile robotic network is dynamically reconfigured to ensure high
quality routes between static wireless nodes, which act as source/destination
pairs for information flow. The ICP commands the robots towards evenly
distributed inter-flow allocations, with intra-flow configurations that
maximize route quality. The PCP then guides the robots via potential-based
control to reconfigure according to ICP commands. This formulation, deemed
Route Swarm, decouples information flow and physical control, generating a
feedback between routing and sensing needs and robotic configuration. We
demonstrate our propositions through simulation under a realistic wireless
network regime.Comment: 9 pages, 4 figures, submitted to the IEEE International Conference on
Intelligent Robots and Systems (IROS) 201
Kinematically optimal hyper-redundant manipulator configurations
“Hyper-redundant” robots have a very large or infinite degree of kinematic redundancy. This paper develops new methods for determining “optimal” hyper-redundant manipulator configurations based on a continuum formulation of kinematics. This formulation uses a backbone curve model to capture the robot's essential macroscopic geometric features. The calculus of variations is used to develop differential equations, whose solution is the optimal backbone curve shape. We show that this approach is computationally efficient on a single processor, and generates solutions in O(1) time for an N degree-of-freedom manipulator when implemented in parallel on O(N) processors. For this reason, it is better suited to hyper-redundant robots than other redundancy resolution methods. Furthermore, this approach is useful for many hyper-redundant mechanical morphologies which are not handled by known methods
Effects of NHC-Backbone Substitution on Efficiency in Ruthenium-Based Olefin Metathesis
series of ruthenium olefin metathesis catalysts bearing N-heterocyclic carbene (NHC) ligands with varying degrees of backbone and N-aryl substitution have been prepared. These complexes show greater resistance to decomposition through C−H activation of the N-aryl group, resulting in increased catalyst lifetimes. This work has utilized robotic technology to examine the activity and stability of each catalyst in metathesis, providing insights into the relationship between ligand architecture and enhanced efficiency. The development of this robotic methodology has also shown that, under optimized conditions, catalyst loadings as low as 25 ppm can lead to 100% conversion in the ring-closing metathesis of diethyl diallylmalonate
The RGB-D Triathlon: Towards Agile Visual Toolboxes for Robots
Deep networks have brought significant advances in robot perception, enabling
to improve the capabilities of robots in several visual tasks, ranging from
object detection and recognition to pose estimation, semantic scene
segmentation and many others. Still, most approaches typically address visual
tasks in isolation, resulting in overspecialized models which achieve strong
performances in specific applications but work poorly in other (often related)
tasks. This is clearly sub-optimal for a robot which is often required to
perform simultaneously multiple visual recognition tasks in order to properly
act and interact with the environment. This problem is exacerbated by the
limited computational and memory resources typically available onboard to a
robotic platform. The problem of learning flexible models which can handle
multiple tasks in a lightweight manner has recently gained attention in the
computer vision community and benchmarks supporting this research have been
proposed. In this work we study this problem in the robot vision context,
proposing a new benchmark, the RGB-D Triathlon, and evaluating state of the art
algorithms in this novel challenging scenario. We also define a new evaluation
protocol, better suited to the robot vision setting. Results shed light on the
strengths and weaknesses of existing approaches and on open issues, suggesting
directions for future research.Comment: This work has been submitted to IROS/RAL 201
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