40,382 research outputs found
An Aggregated Optimization Model for Multi-Head SMD Placements
In this article we propose an aggregate optimization approach by formulating the multi-head SMD placement optimization problem into a mixed integer program (MIP) with the variables based on batches of components. This MIP is tractable and effective in balancing workload among placement heads, minimizing the number of nozzle exchanges, and improving handling class. The handling class which specifies the traveling speed of the robot arm, to the best of our knowledge, has been for the first time incorporated in an optimization model. While the MIP produces an optimal planning for batches of components, a new sequencing heuristics is developed in order to determine the final sequence of component placements based on the outputs of the MIP. This two-stage approach guarantees a good feasible solution to the multi-head SMD placement optimization problem. The computational performance is examined using real industrial data.Multi-head surface mounting device;Component placement;Variable placement speed
Planning with SiMBA: Motion Planning under Uncertainty for Temporal Goals using Simplified Belief Guides
This paper presents a new multi-layered algorithm for motion planning under
motion and sensing uncertainties for Linear Temporal Logic specifications. We
propose a technique to guide a sampling-based search tree in the combined task
and belief space using trajectories from a simplified model of the system, to
make the problem computationally tractable. Our method eliminates the need to
construct fine and accurate finite abstractions. We prove correctness and
probabilistic completeness of our algorithm, and illustrate the benefits of our
approach on several case studies. Our results show that guidance with a
simplified belief space model allows for significant speed-up in planning for
complex specifications.Comment: 8 pages, to appear in the IEEE International Conference on Robotics
and Automation (ICRA), 202
Parameterized Complexity Results for Plan Reuse
Planning is a notoriously difficult computational problem of high worst-case
complexity. Researchers have been investing significant efforts to develop
heuristics or restrictions to make planning practically feasible. Case-based
planning is a heuristic approach where one tries to reuse previous experience
when solving similar problems in order to avoid some of the planning effort.
Plan reuse may offer an interesting alternative to plan generation in some
settings.
We provide theoretical results that identify situations in which plan reuse
is provably tractable. We perform our analysis in the framework of
parameterized complexity, which supports a rigorous worst-case complexity
analysis that takes structural properties of the input into account in terms of
parameters. A central notion of parameterized complexity is fixed-parameter
tractability which extends the classical notion of polynomial-time tractability
by utilizing the effect of structural properties of the problem input.
We draw a detailed map of the parameterized complexity landscape of several
variants of problems that arise in the context of case-based planning. In
particular, we consider the problem of reusing an existing plan, imposing
various restrictions in terms of parameters, such as the number of steps that
can be added to the existing plan to turn it into a solution of the planning
instance at hand.Comment: Proceedings of AAAI 2013, pp. 224-231, AAAI Press, 201
Progressive Learning for Physics-informed Neural Motion Planning
Motion planning (MP) is one of the core robotics problems requiring fast
methods for finding a collision-free robot motion path connecting the given
start and goal states. Neural motion planners (NMPs) demonstrate fast
computational speed in finding path solutions but require a huge amount of
expert trajectories for learning, thus adding a significant training
computational load. In contrast, recent advancements have also led to a
physics-informed NMP approach that directly solves the Eikonal equation for
motion planning and does not require expert demonstrations for learning.
However, experiments show that the physics-informed NMP approach performs
poorly in complex environments and lacks scalability in multiple scenarios and
high-dimensional real robot settings. To overcome these limitations, this paper
presents a novel and tractable Eikonal equation formulation and introduces a
new progressive learning strategy to train neural networks without expert data
in complex, cluttered, multiple high-dimensional robot motion planning
scenarios. The results demonstrate that our method outperforms state-of-the-art
traditional MP, data-driven NMP, and physics-informed NMP methods by a
significant margin in terms of computational planning speed, path quality, and
success rates. We also show that our approach scales to multiple complex,
cluttered scenarios and the real robot set up in a narrow passage environment.
The proposed method's videos and code implementations are available at
https://github.com/ruiqini/P-NTFields.Comment: Accepted to Robotics: Science and Systems (RSS) 202
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EASe : integrating search with learned episodes
Weak methods are insufficient to solve complex problems. Constrained weak methods, like hill-climbing, search too little of the problem space. Unconstrained weak methods, like breadth-first search, are intractable. Fortunately, through the integration of multiple weak methods more powerful problem solvers can be created. We demonstrate that augmenting a weak constrained search method with episodes provides a tractable method for solving a large class of problems. We demonstrate that these episodes can be generated using an unconstrained weak method while solving simple problems from a domain. We provide an analytical model of our approach and empirical results from the logic synthesis domain of VLSI design as well as the classic tile-sliding domain
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