16,850 research outputs found
Evolution of Swarm Robotics Systems with Novelty Search
Novelty search is a recent artificial evolution technique that challenges
traditional evolutionary approaches. In novelty search, solutions are rewarded
based on their novelty, rather than their quality with respect to a predefined
objective. The lack of a predefined objective precludes premature convergence
caused by a deceptive fitness function. In this paper, we apply novelty search
combined with NEAT to the evolution of neural controllers for homogeneous
swarms of robots. Our empirical study is conducted in simulation, and we use a
common swarm robotics task - aggregation, and a more challenging task - sharing
of an energy recharging station. Our results show that novelty search is
unaffected by deception, is notably effective in bootstrapping the evolution,
can find solutions with lower complexity than fitness-based evolution, and can
find a broad diversity of solutions for the same task. Even in non-deceptive
setups, novelty search achieves solution qualities similar to those obtained in
traditional fitness-based evolution. Our study also encompasses variants of
novelty search that work in concert with fitness-based evolution to combine the
exploratory character of novelty search with the exploitatory character of
objective-based evolution. We show that these variants can further improve the
performance of novelty search. Overall, our study shows that novelty search is
a promising alternative for the evolution of controllers for robotic swarms.Comment: To appear in Swarm Intelligence (2013), ANTS Special Issue. The final
publication will be available at link.springer.co
Dexterous manipulation of unknown objects using virtual contact points
The manipulation of unknown objects is a problem of special interest in robotics since it is not always possible to have exact models of the objects with which the robot interacts. This paper presents a simple strategy to manipulate unknown objects using a robotic hand equipped with tactile sensors. The hand configurations that allow the rotation of an unknown object are computed using only tactile and kinematic information, obtained during the manipulation process and reasoning about the desired and real positions of the fingertips during the manipulation. This is done taking into account that the desired positions of the fingertips are not physically reachable since they are located in the interior of the manipulated object and therefore they are virtual positions with associated virtual contact points. The proposed approach was satisfactorily validated using three fingers of an anthropomorphic robotic hand (Allegro Hand), with the original fingertips replaced by tactile sensors (WTS-FT). In the experimental validation, several everyday objects with different shapes were successfully manipulated, rotating them without the need of knowing their shape or any other physical property.Peer ReviewedPostprint (author's final draft
Minimax Iterative Dynamic Game: Application to Nonlinear Robot Control Tasks
Multistage decision policies provide useful control strategies in
high-dimensional state spaces, particularly in complex control tasks. However,
they exhibit weak performance guarantees in the presence of disturbance, model
mismatch, or model uncertainties. This brittleness limits their use in
high-risk scenarios. We present how to quantify the sensitivity of such
policies in order to inform of their robustness capacity. We also propose a
minimax iterative dynamic game framework for designing robust policies in the
presence of disturbance/uncertainties. We test the quantification hypothesis on
a carefully designed deep neural network policy; we then pose a minimax
iterative dynamic game (iDG) framework for improving policy robustness in the
presence of adversarial disturbances. We evaluate our iDG framework on a
mecanum-wheeled robot, whose goal is to find a ocally robust optimal multistage
policy that achieve a given goal-reaching task. The algorithm is simple and
adaptable for designing meta-learning/deep policies that are robust against
disturbances, model mismatch, or model uncertainties, up to a disturbance
bound. Videos of the results are on the author's website,
http://ecs.utdallas.edu/~opo140030/iros18/iros2018.html, while the codes for
reproducing our experiments are on github,
https://github.com/lakehanne/youbot/tree/rilqg. A self-contained environment
for reproducing our results is on docker,
https://hub.docker.com/r/lakehanne/youbotbuntu14/Comment: 2018 International Conference on Intelligent Robots and System
Dynamics of the Orthoglide parallel robot
Recursive matrix relations for kinematics and dynamics of the Orthoglide
parallel robot having three concurrent prismatic actuators are established in
this paper. These are arranged according to the Cartesian coordinate system
with fixed orientation, which means that the actuating directions are normal to
each other. Three identical legs connecting to the moving platform are located
on three planes being perpendicular to each other too. Knowing the position and
the translation motion of the platform, we develop the inverse kinematics
problem and determine the position, velocity and acceleration of each element
of the robot. Further, the principle of virtual work is used in the inverse
dynamic problem. Some matrix equations offer iterative expressions and graphs
for the input forces and the powers of the three actuators
Real-Time Online Re-Planning for Grasping Under Clutter and Uncertainty
We consider the problem of grasping in clutter. While there have been motion
planners developed to address this problem in recent years, these planners are
mostly tailored for open-loop execution. Open-loop execution in this domain,
however, is likely to fail, since it is not possible to model the dynamics of
the multi-body multi-contact physical system with enough accuracy, neither is
it reasonable to expect robots to know the exact physical properties of
objects, such as frictional, inertial, and geometrical. Therefore, we propose
an online re-planning approach for grasping through clutter. The main challenge
is the long planning times this domain requires, which makes fast re-planning
and fluent execution difficult to realize. In order to address this, we propose
an easily parallelizable stochastic trajectory optimization based algorithm
that generates a sequence of optimal controls. We show that by running this
optimizer only for a small number of iterations, it is possible to perform real
time re-planning cycles to achieve reactive manipulation under clutter and
uncertainty.Comment: Published as a conference paper in IEEE Humanoids 201
Empowering and assisting natural human mobility: The simbiosis walker
This paper presents the complete development of the Simbiosis Smart Walker. The device is equipped with a set of sensor subsystems to acquire user-machine interaction forces and the temporal evolution of user's feet during gait. The authors present an adaptive filtering technique used for the identification and separation of different components found on the human-machine interaction forces. This technique allowed isolating the components related with the navigational commands and developing a Fuzzy logic controller to guide the device. The Smart Walker was clinically validated at the Spinal Cord Injury Hospital of Toledo - Spain, presenting great acceptability by spinal chord injury patients and clinical staf
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