7,691 research outputs found
Application of multiobjective genetic programming to the design of robot failure recognition systems
We present an evolutionary approach using multiobjective genetic programming (MOGP) to derive optimal feature extraction preprocessing stages for robot failure detection. This data-driven machine learning method is compared both with conventional (nonevolutionary) classifiers and a set of domain-dependent feature extraction methods. We conclude MOGP is an effective and practical design method for failure recognition systems with enhanced recognition accuracy over conventional classifiers, independent of domain knowledge
Technical Report: A Receding Horizon Algorithm for Informative Path Planning with Temporal Logic Constraints
This technical report is an extended version of the paper 'A Receding Horizon
Algorithm for Informative Path Planning with Temporal Logic Constraints'
accepted to the 2013 IEEE International Conference on Robotics and Automation
(ICRA). This paper considers the problem of finding the most informative path
for a sensing robot under temporal logic constraints, a richer set of
constraints than have previously been considered in information gathering. An
algorithm for informative path planning is presented that leverages tools from
information theory and formal control synthesis, and is proven to give a path
that satisfies the given temporal logic constraints. The algorithm uses a
receding horizon approach in order to provide a reactive, on-line solution
while mitigating computational complexity. Statistics compiled from multiple
simulation studies indicate that this algorithm performs better than a baseline
exhaustive search approach.Comment: Extended version of paper accepted to 2013 IEEE International
Conference on Robotics and Automation (ICRA
Distributed Control of Microscopic Robots in Biomedical Applications
Current developments in molecular electronics, motors and chemical sensors
could enable constructing large numbers of devices able to sense, compute and
act in micron-scale environments. Such microscopic machines, of sizes
comparable to bacteria, could simultaneously monitor entire populations of
cells individually in vivo. This paper reviews plausible capabilities for
microscopic robots and the physical constraints due to operation in fluids at
low Reynolds number, diffusion-limited sensing and thermal noise from Brownian
motion. Simple distributed controls are then presented in the context of
prototypical biomedical tasks, which require control decisions on millisecond
time scales. The resulting behaviors illustrate trade-offs among speed,
accuracy and resource use. A specific example is monitoring for patterns of
chemicals in a flowing fluid released at chemically distinctive sites.
Information collected from a large number of such devices allows estimating
properties of cell-sized chemical sources in a macroscopic volume. The
microscopic devices moving with the fluid flow in small blood vessels can
detect chemicals released by tissues in response to localized injury or
infection. We find the devices can readily discriminate a single cell-sized
chemical source from the background chemical concentration, providing
high-resolution sensing in both time and space. By contrast, such a source
would be difficult to distinguish from background when diluted throughout the
blood volume as obtained with a blood sample
Unmanned Ground Vehicles for Smart Farms
Forecasts of world population increases in the coming decades demand new production processes that are more efficient, safer, and less destructive to the environment. Industries are working to fulfill this mission by developing the smart factory concept. The agriculture world should follow industry leadership and develop approaches to implement the smart farm concept. One of the most vital elements that must be configured to meet the requirements of the new smart farms is the unmanned ground vehicles (UGV). Thus, this chapter focuses on the characteristics that the UGVs must have to function efficiently in this type of future farm. Two main approaches are discussed: automating conventional vehicles and developing specifically designed mobile platforms. The latter includes both wheeled and wheel-legged robots and an analysis of their adaptability to terrain and crops
Healthcare Robotics
Robots have the potential to be a game changer in healthcare: improving
health and well-being, filling care gaps, supporting care givers, and aiding
health care workers. However, before robots are able to be widely deployed, it
is crucial that both the research and industrial communities work together to
establish a strong evidence-base for healthcare robotics, and surmount likely
adoption barriers. This article presents a broad contextualization of robots in
healthcare by identifying key stakeholders, care settings, and tasks; reviewing
recent advances in healthcare robotics; and outlining major challenges and
opportunities to their adoption.Comment: 8 pages, Communications of the ACM, 201
Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional Archive of Phenotypic Elites Algorithm
The recently introduced Multi-dimensional Archive of Phenotypic Elites
(MAP-Elites) is an evolutionary algorithm capable of producing a large archive
of diverse, high-performing solutions in a single run. It works by discretizing
a continuous feature space into unique regions according to the desired
discretization per dimension. While simple, this algorithm has a main drawback:
it cannot scale to high-dimensional feature spaces since the number of regions
increase exponentially with the number of dimensions. In this paper, we address
this limitation by introducing a simple extension of MAP-Elites that has a
constant, pre-defined number of regions irrespective of the dimensionality of
the feature space. Our main insight is that methods from computational geometry
could partition a high-dimensional space into well-spread geometric regions. In
particular, our algorithm uses a centroidal Voronoi tessellation (CVT) to
divide the feature space into a desired number of regions; it then places every
generated individual in its closest region, replacing a less fit one if the
region is already occupied. We demonstrate the effectiveness of the new
"CVT-MAP-Elites" algorithm in high-dimensional feature spaces through
comparisons against MAP-Elites in maze navigation and hexapod locomotion tasks
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