This paper from 1991 introduces an approach for runtime type checking and type
coercion. It is relevant because this approach is being considered for addition
into Ruby 3.0.
This paper will be discussed at PWL Berlin.
This paper was celebrated at PWLMTL#2 by Peter Zion, who also used it as
a springboard to discuss GPU architecture in general, as well as his
implementation based on the paper in the Fabric Engine
framework (fabricengine.com).
Rather than artificially separate garbage collection from other classes
of memory management, I propose moving garbage collection topics under
the more general label of memory management ("storage management" or
"memory allocation" might be reasonable alternatives).
for adding these papers in the commit log.
The dynamic window approach to collision avoidance is an influential
paper for mobile robots. The method is based on a robot's dynamics
rather than higher-level representations of a robot and/or obstacles in
an environment.
The PRM and RRT algorithms are two seminal papers in robot motion
planning. The problem of motion planning scales exponentially with the
degrees of freedom a robot has and the degrees of freedom the obstacles
in an environment have. Thus, planning with high degrees of freedom leads to many problems
such as incompleteness and extremely slow speed. The PRM method was the first to
propose a sampling-based stratey to deal with motion planning and
created a practical methed for offline planning of robot manipulators.
The RRT method modified PRM by using a tree structure rather than a
graph so that non-holonomic and other constraints could be considered
when planning.
The Instantaneous Trajectory Generation method is relatively new, but
very important. It allows for extremely fast trajectory generation for
robots of high degrees of freedom (motion states generated within 1
millisecond). It has been used to implement robot sword fighting and
other activities that require fast reaction-based planning. The author
started a business based simply on the work and has shown the
algorithm's success in many robot applications.
Zeeshan Lakhani
Igor Wiedler
Dag
Jeremy Heiler
DarrenN
Michael L Perry
Sveinn Fannar
Markus Klink
Julian Squires
Fwiffo
Jason Brown
David Kua
Ines Sombra
Matthew Loseke
Sterling McLeod
Jason Ganetsky
Pamela Selle