206 research outputs found
Experience-Based Planning with Sparse Roadmap Spanners
We present an experienced-based planning framework called Thunder that learns
to reduce computation time required to solve high-dimensional planning problems
in varying environments. The approach is especially suited for large
configuration spaces that include many invariant constraints, such as those
found with whole body humanoid motion planning. Experiences are generated using
probabilistic sampling and stored in a sparse roadmap spanner (SPARS), which
provides asymptotically near-optimal coverage of the configuration space,
making storing, retrieving, and repairing past experiences very efficient with
respect to memory and time. The Thunder framework improves upon past
experience-based planners by storing experiences in a graph rather than in
individual paths, eliminating redundant information, providing more
opportunities for path reuse, and providing a theoretical limit to the size of
the experience graph. These properties also lead to improved handling of
dynamically changing environments, reasoning about optimal paths, and reducing
query resolution time. The approach is demonstrated on a 30 degrees of freedom
humanoid robot and compared with the Lightning framework, an experience-based
planner that uses individual paths to store past experiences. In environments
with variable obstacles and stability constraints, experiments show that
Thunder is on average an order of magnitude faster than Lightning and planning
from scratch. Thunder also uses 98.8% less memory to store its experiences
after 10,000 trials when compared to Lightning. Our framework is implemented
and freely available in the Open Motion Planning Library.Comment: Submitted to ICRA 201
RoboTSP - A Fast Solution to the Robotic Task Sequencing Problem
In many industrial robotics applications, such as spot-welding,
spray-painting or drilling, the robot is required to visit successively
multiple targets. The robot travel time among the targets is a significant
component of the overall execution time. This travel time is in turn greatly
affected by the order of visit of the targets, and by the robot configurations
used to reach each target. Therefore, it is crucial to optimize these two
elements, a problem known in the literature as the Robotic Task Sequencing
Problem (RTSP). Our contribution in this paper is two-fold. First, we propose a
fast, near-optimal, algorithm to solve RTSP. The key to our approach is to
exploit the classical distinction between task space and configuration space,
which, surprisingly, has been so far overlooked in the RTSP literature. Second,
we provide an open-source implementation of the above algorithm, which has been
carefully benchmarked to yield an efficient, ready-to-use, software solution.
We discuss the relationship between RTSP and other Traveling Salesman Problem
(TSP) variants, such as the Generalized Traveling Salesman Problem (GTSP), and
show experimentally that our method finds motion sequences of the same quality
but using several orders of magnitude less computation time than existing
approaches.Comment: 6 pages, 7 figures, 1 tabl
The Provable Virtue of Laziness in Motion Planning
The Lazy Shortest Path (LazySP) class consists of motion-planning algorithms
that only evaluate edges along shortest paths between the source and target.
These algorithms were designed to minimize the number of edge evaluations in
settings where edge evaluation dominates the running time of the algorithm; but
how close to optimal are LazySP algorithms in terms of this objective? Our main
result is an analytical upper bound, in a probabilistic model, on the number of
edge evaluations required by LazySP algorithms; a matching lower bound shows
that these algorithms are asymptotically optimal in the worst case
Bayesian Active Edge Evaluation on Expensive Graphs
Robots operate in environments with varying implicit structure. For instance,
a helicopter flying over terrain encounters a very different arrangement of
obstacles than a robotic arm manipulating objects on a cluttered table top.
State-of-the-art motion planning systems do not exploit this structure, thereby
expending valuable planning effort searching for implausible solutions. We are
interested in planning algorithms that actively infer the underlying structure
of the valid configuration space during planning in order to find solutions
with minimal effort. Consider the problem of evaluating edges on a graph to
quickly discover collision-free paths. Evaluating edges is expensive, both for
robots with complex geometries like robot arms, and for robots with limited
onboard computation like UAVs. Until now, this challenge has been addressed via
laziness i.e. deferring edge evaluation until absolutely necessary, with the
hope that edges turn out to be valid. However, all edges are not alike in value
- some have a lot of potentially good paths flowing through them, and some
others encode the likelihood of neighbouring edges being valid. This leads to
our key insight - instead of passive laziness, we can actively choose edges
that reduce the uncertainty about the validity of paths. We show that this is
equivalent to the Bayesian active learning paradigm of decision region
determination (DRD). However, the DRD problem is not only combinatorially hard,
but also requires explicit enumeration of all possible worlds. We propose a
novel framework that combines two DRD algorithms, DIRECT and BISECT, to
overcome both issues. We show that our approach outperforms several
state-of-the-art algorithms on a spectrum of planning problems for mobile
robots, manipulators and autonomous helicopters
Dynamic collision avoidance system for a manipulator based on RGB-D data
The new paradigms of Industry 4.0 demand the collabora-
tion between robot and humans. They could help and collaborate each
other without any additional safety unlike other manipulators. The robot
should have the ability of acquire the environment and plan (or re-plan)
on-the-
y the movement avoiding the obstacles and people. This paper
proposes a system that acquires the environment space, based on a kinect
sensor, performs the path planning of a UR5 manipulator for pick and
place tasks while avoiding the objects, based on the point cloud from
kinect. Results allow to validate the proposed system.Project ”TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020” is financed by the North Portugal Regional Operational. Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF). This work is also financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation -COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013.info:eu-repo/semantics/publishedVersio
- …