4 research outputs found
Cellular Automata Applications in Shortest Path Problem
Cellular Automata (CAs) are computational models that can capture the
essential features of systems in which global behavior emerges from the
collective effect of simple components, which interact locally. During the last
decades, CAs have been extensively used for mimicking several natural processes
and systems to find fine solutions in many complex hard to solve computer
science and engineering problems. Among them, the shortest path problem is one
of the most pronounced and highly studied problems that scientists have been
trying to tackle by using a plethora of methodologies and even unconventional
approaches. The proposed solutions are mainly justified by their ability to
provide a correct solution in a better time complexity than the renowned
Dijkstra's algorithm. Although there is a wide variety regarding the
algorithmic complexity of the algorithms suggested, spanning from simplistic
graph traversal algorithms to complex nature inspired and bio-mimicking
algorithms, in this chapter we focus on the successful application of CAs to
shortest path problem as found in various diverse disciplines like computer
science, swarm robotics, computer networks, decision science and biomimicking
of biological organisms' behaviour. In particular, an introduction on the first
CA-based algorithm tackling the shortest path problem is provided in detail.
After the short presentation of shortest path algorithms arriving from the
relaxization of the CAs principles, the application of the CA-based shortest
path definition on the coordinated motion of swarm robotics is also introduced.
Moreover, the CA based application of shortest path finding in computer
networks is presented in brief. Finally, a CA that models exactly the behavior
of a biological organism, namely the Physarum's behavior, finding the
minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From
software to wetware. Springer, 201
GRAB: A Dataset of Whole-Body Human Grasping of Objects
Training computers to understand, model, and synthesize human grasping
requires a rich dataset containing complex 3D object shapes, detailed contact
information, hand pose and shape, and the 3D body motion over time. While
"grasping" is commonly thought of as a single hand stably lifting an object, we
capture the motion of the entire body and adopt the generalized notion of
"whole-body grasps". Thus, we collect a new dataset, called GRAB (GRasping
Actions with Bodies), of whole-body grasps, containing full 3D shape and pose
sequences of 10 subjects interacting with 51 everyday objects of varying shape
and size. Given MoCap markers, we fit the full 3D body shape and pose,
including the articulated face and hands, as well as the 3D object pose. This
gives detailed 3D meshes over time, from which we compute contact between the
body and object. This is a unique dataset, that goes well beyond existing ones
for modeling and understanding how humans grasp and manipulate objects, how
their full body is involved, and how interaction varies with the task. We
illustrate the practical value of GRAB with an example application; we train
GrabNet, a conditional generative network, to predict 3D hand grasps for unseen
3D object shapes. The dataset and code are available for research purposes at
https://grab.is.tue.mpg.de.Comment: ECCV 202