62,007 research outputs found
Comparative Study on Agile software development methodologies
Today-s business environment is very much dynamic, and organisations are
constantly changing their software requirements to adjust with new environment.
They also demand for fast delivery of software products as well as for
accepting changing requirements. In this aspect, traditional plan-driven
developments fail to meet up these requirements. Though traditional software
development methodologies, such as life cycle-based structured and object
oriented approaches, continue to dominate the systems development few decades
and much research has done in traditional methodologies, Agile software
development brings its own set of novel challenges that must be addressed to
satisfy the customer through early and continuous delivery of the valuable
software. It is a set of software development methods based on iterative and
incremental development process, where requirements and development evolve
through collaboration between self-organizing, cross-functional teams that
allows rapid delivery of high quality software to meet customer needs and also
accommodate changes in the requirements. In this paper, we significantly
identify and describe the major factors, that Agile development approach
improves software development process to meet the rapid changing business
environments. We also provide a brief comparison of agile development
methodologies with traditional systems development methodologies, and discuss
current state of adopting agile methodologies. We speculate that from the need
to satisfy the customer through early and continuous delivery of the valuable
software, Agile software development is emerged as an alternative to
traditional plan-based software development methods. The purpose of this paper,
is to provide an in-depth understanding, the major benefits of agile
development approach to software development industry, as well as provide a
comparison study report of ASDM over TSDM.Comment: 25 pages, 25 images, 86 references used, with authors biographie
Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs
In this paper, we present Batch Informed Trees (BIT*), a planning algorithm
based on unifying graph- and sampling-based planning techniques. By recognizing
that a set of samples describes an implicit random geometric graph (RGG), we
are able to combine the efficient ordered nature of graph-based techniques,
such as A*, with the anytime scalability of sampling-based algorithms, such as
Rapidly-exploring Random Trees (RRT).
BIT* uses a heuristic to efficiently search a series of increasingly dense
implicit RGGs while reusing previous information. It can be viewed as an
extension of incremental graph-search techniques, such as Lifelong Planning A*
(LPA*), to continuous problem domains as well as a generalization of existing
sampling-based optimal planners. It is shown that it is probabilistically
complete and asymptotically optimal.
We demonstrate the utility of BIT* on simulated random worlds in
and and manipulation problems on CMU's HERB, a
14-DOF two-armed robot. On these problems, BIT* finds better solutions faster
than RRT, RRT*, Informed RRT*, and Fast Marching Trees (FMT*) with faster
anytime convergence towards the optimum, especially in high dimensions.Comment: 8 Pages. 6 Figures. Video available at
http://www.youtube.com/watch?v=TQIoCC48gp
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