4,080 research outputs found
Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study
Developing robot agnostic software frameworks involves synthesizing the
disparate fields of robotic theory and software engineering while
simultaneously accounting for a large variability in hardware designs and
control paradigms. As the capabilities of robotic software frameworks increase,
the setup difficulty and learning curve for new users also increase. If the
entry barriers for configuring and using the software on robots is too high,
even the most powerful of frameworks are useless. A growing need exists in
robotic software engineering to aid users in getting started with, and
customizing, the software framework as necessary for particular robotic
applications. In this paper a case study is presented for the best practices
found for lowering the barrier of entry in the MoveIt! framework, an
open-source tool for mobile manipulation in ROS, that allows users to 1)
quickly get basic motion planning functionality with minimal initial setup, 2)
automate its configuration and optimization, and 3) easily customize its
components. A graphical interface that assists the user in configuring MoveIt!
is the cornerstone of our approach, coupled with the use of an existing
standardized robot model for input, automatically generated robot-specific
configuration files, and a plugin-based architecture for extensibility. These
best practices are summarized into a set of barrier to entry design principles
applicable to other robotic software. The approaches for lowering the entry
barrier are evaluated by usage statistics, a user survey, and compared against
our design objectives for their effectiveness to users
Robot's hand and expansions in non-integer bases
We study a robot hand model in the framework of the theory of expansions in
non-integer bases. We investigate the reachable workspace and we study some
configurations enjoying form closure properties.Comment: 22 pages, 10 figure
Toward a computational theory for motion understanding: The expert animators model
Artificial intelligence researchers claim to understand some aspect of human intelligence when their model is able to emulate it. In the context of computer graphics, the ability to go from motion representation to convincing animation should accordingly be treated not simply as a trick for computer graphics programmers but as important epistemological and methodological goal. In this paper we investigate a unifying model for animating a group of articulated bodies such as humans and robots in a three-dimensional environment. The proposed model is considered in the framework of knowledge representation and processing, with special reference to motion knowledge. The model is meant to help setting the basis for a computational theory for motion understanding applied to articulated bodies
The kinematics of hyper-redundant robot locomotion
This paper considers the kinematics of hyper-redundant (or “serpentine”) robot locomotion over uneven solid terrain, and presents algorithms to implement a variety of “gaits”. The analysis and algorithms are based on a continuous backbone curve model which captures the robot's macroscopic geometry. Two classes of gaits, based on stationary waves and traveling waves of mechanism deformation, are introduced for hyper-redundant robots of both constant and variable length. We also illustrate how the locomotion algorithms can be used to plan the manipulation of objects which are grasped in a tentacle-like manner. Several of these gaits and the manipulation algorithm have been implemented on a 30 degree-of-freedom hyper-redundant robot. Experimental results are presented to demonstrate and validate these concepts and our modeling assumptions
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