48,240 research outputs found
Dynamic coordinated control laws in multiple agent models
We present an active control scheme of a kinetic model of swarming. It has
been shown previously that the global control scheme for the model, presented
in \cite{JK04}, gives rise to spontaneous collective organization of agents
into a unified coherent swarm, via a long-range attractive and short-range
repulsive potential. We extend these results by presenting control laws whereby
a single swarm is broken into independently functioning subswarm clusters. The
transition between one coordinated swarm and multiple clustered subswarms is
managed simply with a homotopy parameter. Additionally, we present as an
alternate formulation, a local control law for the same model, which implements
dynamic barrier avoidance behavior, and in which swarm coherence emerges
spontaneously.Comment: 20 pages, 6 figure
Flexible human-robot cooperation models for assisted shop-floor tasks
The Industry 4.0 paradigm emphasizes the crucial benefits that collaborative
robots, i.e., robots able to work alongside and together with humans, could
bring to the whole production process. In this context, an enabling technology
yet unreached is the design of flexible robots able to deal at all levels with
humans' intrinsic variability, which is not only a necessary element for a
comfortable working experience for the person but also a precious capability
for efficiently dealing with unexpected events. In this paper, a sensing,
representation, planning and control architecture for flexible human-robot
cooperation, referred to as FlexHRC, is proposed. FlexHRC relies on wearable
sensors for human action recognition, AND/OR graphs for the representation of
and reasoning upon cooperation models, and a Task Priority framework to
decouple action planning from robot motion planning and control.Comment: Submitted to Mechatronics (Elsevier
Motion planning and assembly for microassembly workstation
In general, mechatronics systems have no standard
operating system that could be used for planning and
control when these complex devices are running. The
goal of this paper is to formulate a work platform that can
be used as a method for obtaining precision in the
manipulation of micro-entities using micro-scale
manipulation tools for microsystem applications. This
paper provide groundwork for motion planning and
assembly of the Micro-Assembly Workstation (MAW)
manipulation system. To demonstrate the feasibility of the
idea, the paper implements some of the motion planning
algorithms; it investigates the performance of the
conventional Euclidean distance algorithm (EDA),
artificial potential fields’ algorithm, and A* algorithm
when implemented on a virtual space
A Top-Down Approach to Managing Variability in Robotics Algorithms
One of the defining features of the field of robotics is its breadth and
heterogeneity. Unfortunately, despite the availability of several robotics
middleware services, robotics software still fails to smoothly handle at least
two kinds of variability: algorithmic variability and lower-level variability.
The consequence is that implementations of algorithms are hard to understand
and impacted by changes to lower-level details such as the choice or
configuration of sensors or actuators. Moreover, when several algorithms or
algorithmic variants are available it is difficult to compare and combine them.
In order to alleviate these problems we propose a top-down approach to
express and implement robotics algorithms and families of algorithms so that
they are both less dependent on lower-level details and easier to understand
and combine. This approach goes top-down from the algorithms and shields them
from lower-level details by introducing very high level abstractions atop the
intermediate abstractions of robotics middleware. This approach is illustrated
on 7 variants of the Bug family that were implemented using both laser and
infra-red sensors.Comment: 6 pages, 5 figures, Presented at DSLRob 2013 (arXiv:cs/1312.5952
A model of ant route navigation driven by scene familiarity
In this paper we propose a model of visually guided route navigation in ants that captures the known properties of real behaviour whilst retaining mechanistic simplicity and thus biological plausibility. For an ant, the coupling of movement and viewing direction means that a familiar view specifies a familiar direction of movement. Since the views experienced along a habitual route will be more familiar, route navigation can be re-cast as a search for familiar views. This search can be performed with a simple scanning routine, a behaviour that ants have been observed to perform. We test this proposed route navigation strategy in simulation, by learning a series of routes through visually cluttered environments consisting of objects that are only distinguishable as silhouettes against the sky. In the first instance we determine view familiarity by exhaustive comparison with the set of views experienced during training. In further experiments we train an artificial neural network to perform familiarity discrimination using the training views. Our results indicate that, not only is the approach successful, but also that the routes that are learnt show many of the characteristics of the routes of desert ants. As such, we believe the model represents the only detailed and complete model of insect route guidance to date. What is more, the model provides a general demonstration that visually guided routes can be produced with parsimonious mechanisms that do not specify when or what to learn, nor separate routes into sequences of waypoints
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