2,592 research outputs found
Approximating Smallest Containers for Packing Three-dimensional Convex Objects
We investigate the problem of computing a minimal-volume container for the
non-overlapping packing of a given set of three-dimensional convex objects.
Already the simplest versions of the problem are NP-hard so that we cannot
expect to find exact polynomial time algorithms. We give constant ratio
approximation algorithms for packing axis-parallel (rectangular) cuboids under
translation into an axis-parallel (rectangular) cuboid as container, for
cuboids under rigid motions into an axis-parallel cuboid or into an arbitrary
convex container, and for packing convex polyhedra under rigid motions into an
axis-parallel cuboid or arbitrary convex container. This work gives the first
approximability results for the computation of minimal volume containers for
the objects described
Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review
Industry 4.0 has become a crucial part in the majority of processes, components, and related modelling, as well as predictive tools that allow a more efficient, automated and sustainable approach to industry. The availability of large quantities of data, and the advances in IoT, AI, and data-driven frameworks, have led to an enhanced data gathering, assessment, and extraction of actionable information, resulting in a better decision-making process. Product picking and its subsequent packing is an important area, and has drawn increasing attention for the research community. However, depending of the context, some of the related approaches tend to be either highly mathematical, or applied to a specific context. This article aims to provide a survey on the main methods, techniques, and frameworks relevant to product packing and to highlight the main properties and features that should be further investigated to ensure a more efficient and optimised approach
SDF-Pack: Towards Compact Bin Packing with Signed-Distance-Field Minimization
Robotic bin packing is very challenging, especially when considering
practical needs such as object variety and packing compactness. This paper
presents SDF-Pack, a new approach based on signed distance field (SDF) to model
the geometric condition of objects in a container and compute the object
placement locations and packing orders for achieving a more compact bin
packing. Our method adopts a truncated SDF representation to localize the
computation, and based on it, we formulate the SDF minimization heuristic to
find optimized placements to compactly pack objects with the existing ones. To
further improve space utilization, if the packing sequence is controllable, our
method can suggest which object to be packed next. Experimental results on a
large variety of everyday objects show that our method can consistently achieve
higher packing compactness over 1,000 packing cases, enabling us to pack more
objects into the container, compared with the existing heuristics under various
packing settings
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