12,305 research outputs found
A Vision-based Scheme for Kinematic Model Construction of Re-configurable Modular Robots
Re-configurable modular robotic (RMR) systems are advantageous for their
reconfigurability and versatility. A new modular robot can be built for a
specific task by using modules as building blocks. However, constructing a
kinematic model for a newly conceived robot requires significant work. Due to
the finite size of module-types, models of all module-types can be built
individually and stored in a database beforehand. With this priori knowledge,
the model construction process can be automated by detecting the modules and
their corresponding interconnections. Previous literature proposed theoretical
frameworks for constructing kinematic models of modular robots, assuming that
such information was known a priori. While well-devised mechanisms and built-in
sensors can be employed to detect these parameters automatically, they
significantly complicate the module design and thus are expensive. In this
paper, we propose a vision-based method to identify kinematic chains and
automatically construct robot models for modular robots. Each module is affixed
with augmented reality (AR) tags that are encoded with unique IDs. An image of
a modular robot is taken and the detected modules are recognized by querying a
database that maintains all module information. The poses of detected modules
are used to compute: (i) the connection between modules and (ii) joint angles
of joint-modules. Finally, the robot serial-link chain is identified and the
kinematic model constructed and visualized. Our experimental results validate
the effectiveness of our approach. While implementation with only our RMR is
shown, our method can be applied to other RMRs where self-identification is not
possible
Three new and remarkable species of mosses from China and the Philippines
Distichophyllum meizhii Tan & Lin and D. wanianum Tan & Lin (Hookeriaceae) collected from southwestern region of China are described as new to science. Also, Horikawaea redfearnii Tan & Lin is described as a new species based on collections from Hainan Island of China and Palawan Island of the Philippines. The sporophytic specimen of Horikawaea Nog. was collected for the first time and support a family placement in Pterobryaceae
Robot Introspection with Bayesian Nonparametric Vector Autoregressive Hidden Markov Models
Robot introspection, as opposed to anomaly detection typical in process
monitoring, helps a robot understand what it is doing at all times. A robot
should be able to identify its actions not only when failure or novelty occurs,
but also as it executes any number of sub-tasks. As robots continue their quest
of functioning in unstructured environments, it is imperative they understand
what is it that they are actually doing to render them more robust. This work
investigates the modeling ability of Bayesian nonparametric techniques on
Markov Switching Process to learn complex dynamics typical in robot contact
tasks. We study whether the Markov switching process, together with Bayesian
priors can outperform the modeling ability of its counterparts: an HMM with
Bayesian priors and without. The work was tested in a snap assembly task
characterized by high elastic forces. The task consists of an insertion subtask
with very complex dynamics. Our approach showed a stronger ability to
generalize and was able to better model the subtask with complex dynamics in a
computationally efficient way. The modeling technique is also used to learn a
growing library of robot skills, one that when integrated with low-level
control allows for robot online decision making.Comment: final version submitted to humanoids 201
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