2,636 research outputs found
Automatic Differentiation of Rigid Body Dynamics for Optimal Control and Estimation
Many algorithms for control, optimization and estimation in robotics depend
on derivatives of the underlying system dynamics, e.g. to compute
linearizations, sensitivities or gradient directions. However, we show that
when dealing with Rigid Body Dynamics, these derivatives are difficult to
derive analytically and to implement efficiently. To overcome this issue, we
extend the modelling tool `RobCoGen' to be compatible with Automatic
Differentiation. Additionally, we propose how to automatically obtain the
derivatives and generate highly efficient source code. We highlight the
flexibility and performance of the approach in two application examples. First,
we show a Trajectory Optimization example for the quadrupedal robot HyQ, which
employs auto-differentiation on the dynamics including a contact model. Second,
we present a hardware experiment in which a 6 DoF robotic arm avoids a randomly
moving obstacle in a go-to task by fast, dynamic replanning
Automated sequence and motion planning for robotic spatial extrusion of 3D trusses
While robotic spatial extrusion has demonstrated a new and efficient means to
fabricate 3D truss structures in architectural scale, a major challenge remains
in automatically planning extrusion sequence and robotic motion for trusses
with unconstrained topologies. This paper presents the first attempt in the
field to rigorously formulate the extrusion sequence and motion planning (SAMP)
problem, using a CSP encoding. Furthermore, this research proposes a new
hierarchical planning framework to solve the extrusion SAMP problems that
usually have a long planning horizon and 3D configuration complexity. By
decoupling sequence and motion planning, the planning framework is able to
efficiently solve the extrusion sequence, end-effector poses, joint
configurations, and transition trajectories for spatial trusses with
nonstandard topologies. This paper also presents the first detailed computation
data to reveal the runtime bottleneck on solving SAMP problems, which provides
insight and comparing baseline for future algorithmic development. Together
with the algorithmic results, this paper also presents an open-source and
modularized software implementation called Choreo that is machine-agnostic. To
demonstrate the power of this algorithmic framework, three case studies,
including real fabrication and simulation results, are presented.Comment: 24 pages, 16 figure
TriP: A Python package for the kinematic modeling of serial-parallel hybrid robots
can be classified according to their mechanical structure. Serial mechanisms like robotic
arms are mechanisms where each moving part (called a link) is connected to only the one
before and the one after it. They are often used when a large workspace is required, meaning
the robot needs a long reach. In parallel mechanisms, the links of the robot form loops causing
them to be structurally stronger and stiffer.
If both a large workspace and structural strength are required, hybrids that contain both serial
and parallel mechanisms are used. While hybrid mechanisms combine the mechanical advantages
of both parallel and serial mechanisms, they also combine their modeling disadvantages:
Finding an explicit solution for either forward or inverse kinematics is often impossible.
Using numerical approaches instead leads to complicated constrained optimization
problems for both forward and inverse kinematics.
While serial mechanisms are very well supported by current robotic frameworks, parallel
mechanisms and hybrid mechanisms especially are often not supported at all.
TriP is a python package designed to close this gap using a modular modeling framework. It allows the modeling of arbitrary hybrid mechanisms and is capable of calculating forward and inverse kinematics
Graphic simualtion test bed for robotics applications in a workstation environment
Graphical simulation is a cost-effective solution for developing and testing robots and their control systems. The availability of various high-performance workstations makes these systems feasible. Simulation offers preliminary testing of systems before their actual realizations, and it provides a framework for developing new control and planning algorithms. On the other hand, these simulation systems have to have the capability of incorporating various knowledge-based system components, e.g., task planners, representation formalisms, etc. They also should have an appropriate user interface, which makes possible the creation and control of simulation models. ROBOSIM was developed jointly by MSFC and Vanderbilt University, first in a VAX environment. Recently, the system has been ported to an HP-9000 workstation equipped with an SRX graphics accelerator. The user interface of the system now contains a menu- and icon-based facility, as well as the original ROBOSIM language. The system is also coupled to a symbolic computing system based on Common Lisp, where knowledge-based functionalities are implemented. The knowledge-based layer uses various representation and reasoning facilities for programming and testing the control systems of robots
Robot pain: a speculative review of its functions
Given the scarce bibliography dealing explicitly with robot pain, this chapter has enriched its review with related research works about robot behaviours and capacities in which pain could play a role. It is shown that all such roles ¿ranging from punishment to intrinsic motivation and planning knowledge¿ can be formulated within the unified framework of reinforcement learning.Peer ReviewedPostprint (author's final draft
Development of Artificial Intelligent Techniques for Manipulator Position Control
Inspired by works in soft computing this research applies the constituents of soft
computing to act as the "brain" that controls the positioning process of a robot
manipulator's tool. This work combines three methods in artificial intelligence: fuzzy
rules, neural networks, and genetic algorithm to form the soft computing plant
uniquely planned for a six degree-of-freedom serial manipulator. The forward
kinematics of the manipulator is made as the feedforward control plant while the soft
computing plant replaces the inverse kinematics in the feedback loop. Fine
manipulator positioning is first achieved from the learning stage, and later execution
through forward kinematics after the soft computing plant proposes inputs and the
iterations. It is shown experimentally that the technique proposed is capable of
producing results with very low errors. Experiment A for example resulted the
position errors onpx: 0.004%;py: 0.006%; andpz: 0.002%
Cable-Driven Robots with Wireless Control Capability for Pedagogical Illustration in Science
Science teaching in secondary schools is often abstract for students. Even if
some experiments can be conducted in classrooms, mainly for chemistry or some
physics fields, mathematics is not an experimental science. Teachers have to
convince students that theorems have practical implications. We present
teachers an original and easy-to-use pedagogical tool: a cable-driven robot
with a Web-based remote control interface. The robot implements several
scientific concepts such as 3D-geometry and kinematics. The remote control
enables the teacher to move freely in the classroom.Comment: CAR - 8th National Conference on "Control Architecure of Robots"
(2013
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