33,707 research outputs found
Footstep and Motion Planning in Semi-unstructured Environments Using Randomized Possibility Graphs
Traversing environments with arbitrary obstacles poses significant challenges
for bipedal robots. In some cases, whole body motions may be necessary to
maneuver around an obstacle, but most existing footstep planners can only
select from a discrete set of predetermined footstep actions; they are unable
to utilize the continuum of whole body motion that is truly available to the
robot platform. Existing motion planners that can utilize whole body motion
tend to struggle with the complexity of large-scale problems. We introduce a
planning method, called the "Randomized Possibility Graph", which uses
high-level approximations of constraint manifolds to rapidly explore the
"possibility" of actions, thereby allowing lower-level motion planners to be
utilized more efficiently. We demonstrate simulations of the method working in
a variety of semi-unstructured environments. In this context,
"semi-unstructured" means the walkable terrain is flat and even, but there are
arbitrary 3D obstacles throughout the environment which may need to be stepped
over or maneuvered around using whole body motions.Comment: Accepted by IEEE International Conference on Robotics and Automation
201
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Situational reasoning for road driving in an urban environment
Robot navigation in urban environments requires situational reasoning.
Given the complexity of the environment and the behavior specified by traffic
rules, it is necessary to recognize the current situation to impose the correct
traffic rules. In an attempt to manage the complexity of the situational reasoning
subsystem, this paper describes a finite state machine model to govern the situational
reasoning process. The logic state machine and its interaction with the
planning system are discussed. The approach was implemented on Alice, Team
Caltech’s entry into the 2007 DARPA Urban Challenge. Results from the qualifying
rounds are discussed. The approach is validated and the shortcomings of
the implementation are identified
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