234 research outputs found
Critically fast pick-and-place with suction cups
Fast robotics pick-and-place with suction cups is a crucial component in the
current development of automation in logistics (factory lines, e-commerce,
etc.). By "critically fast" we mean the fastest possible movement for
transporting an object such that it does not slip or fall from the suction cup.
The main difficulties are: (i) handling the contact between the suction cup and
the object, which fundamentally involves kinodynamic constraints; and (ii)
doing so at a low computational cost, typically a few hundreds of milliseconds.
To address these difficulties, we propose (a) a model for suction cup contacts,
(b) a procedure to identify the contact stability constraint based on that
model, and (c) a pipeline to parameterize, in a time-optimal manner, arbitrary
geometric paths under the identified contact stability constraint. We
experimentally validate the proposed pipeline on a physical robot system: the
cycle time for a typical pick-and-place task was less than 5 seconds, planning
and execution times included. The full pipeline is released as open-source for
the robotics community.Comment: 7 pages, 5 figure
Robotic manipulation of a rotating chain
This paper considers the problem of manipulating a uniformly rotating chain:
the chain is rotated at a constant angular speed around a fixed axis using a
robotic manipulator. Manipulation is quasi-static in the sense that transitions
are slow enough for the chain to be always in "rotational" equilibrium. The
curve traced by the chain in a rotating plane -- its shape function -- can be
determined by a simple force analysis, yet it possesses complex multi-solutions
behavior typical of non-linear systems. We prove that the configuration space
of the uniformly rotating chain is homeomorphic to a two-dimensional surface
embedded in . Using that representation, we devise a manipulation
strategy for transiting between different rotation modes in a stable and
controlled manner. We demonstrate the strategy on a physical robotic arm
manipulating a rotating chain. Finally, we discuss how the ideas developed here
might find fruitful applications in the study of other flexible objects, such
as elastic rods or concentric tubes.Comment: 12 pages, 9 figure
Time-Optimal Path Tracking via Reachability Analysis
Given a geometric path, the Time-Optimal Path Tracking problem consists in
finding the control strategy to traverse the path time-optimally while
regulating tracking errors. A simple yet effective approach to this problem is
to decompose the controller into two components: (i)~a path controller, which
modulates the parameterization of the desired path in an online manner,
yielding a reference trajectory; and (ii)~a tracking controller, which takes
the reference trajectory and outputs joint torques for tracking. However, there
is one major difficulty: the path controller might not find any feasible
reference trajectory that can be tracked by the tracking controller because of
torque bounds. In turn, this results in degraded tracking performances. Here,
we propose a new path controller that is guaranteed to find feasible reference
trajectories by accounting for possible future perturbations. The main
technical tool underlying the proposed controller is Reachability Analysis, a
new method for analyzing path parameterization problems. Simulations show that
the proposed controller outperforms existing methods.Comment: 6 pages, 3 figures, ICRA 201
A New Approach to Time-Optimal Path Parameterization based on Reachability Analysis
Time-Optimal Path Parameterization (TOPP) is a well-studied problem in
robotics and has a wide range of applications. There are two main families of
methods to address TOPP: Numerical Integration (NI) and Convex Optimization
(CO). NI-based methods are fast but difficult to implement and suffer from
robustness issues, while CO-based approaches are more robust but at the same
time significantly slower. Here we propose a new approach to TOPP based on
Reachability Analysis (RA). The key insight is to recursively compute reachable
and controllable sets at discretized positions on the path by solving small
Linear Programs (LPs). The resulting algorithm is faster than NI-based methods
and as robust as CO-based ones (100% success rate), as confirmed by extensive
numerical evaluations. Moreover, the proposed approach offers unique additional
benefits: Admissible Velocity Propagation and robustness to parametric
uncertainty can be derived from it in a simple and natural way.Comment: 15 pages, 9 figure
A Single-Query Manipulation Planner
In manipulation tasks, a robot interacts with movable object(s). The
configuration space in manipulation planning is thus the Cartesian product of
the configuration space of the robot with those of the movable objects. It is
the complex structure of such a "Composite Configuration Space" that makes
manipulation planning particularly challenging. Previous works approximate the
connectivity of the Composite Configuration Space by means of discretization or
by creating random roadmaps. Such approaches involve an extensive
pre-processing phase, which furthermore has to be re-done each time the
environment changes. In this paper, we propose a high-level Grasp-Placement
Table similar to that proposed by Tournassoud et al. (1987), but which does not
require any discretization or heavy pre-processing. The table captures the
potential connectivity of the Composite Configuration Space while being
specific only to the movable object: in particular, it does not require to be
re-computed when the environment changes. During the query phase, the table is
used to guide a tree-based planner that explores the space systematically. Our
simulations and experiments show that the proposed method enables improvements
in both running time and trajectory quality as compared to existing approaches.Comment: 8 pages, 7 figures, 1 tabl
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