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and over obstacles. By introducing a two-phase plan-
ner we are able to use two different cost functions, one
to find a low-risk path to the goal and another suited to
achieve safe robot configurations along the path. We
introduced a roughness quantification which, based
on filtered height differences, provides an estimate
of the risk the robot is exposed to. The presented
cost functions include costs of risk adjusted execu-
tion times and safety parameters, like the stability of
the system and an traction estimate. Using these met-
rics we provided several experiments which prove the
validity of our measures.
As stated in the experiment section, the algorithm
works on a subset of the robot state space X. Im-
proving the state space representation and enhancing
the search method to facilitate a more comprehensive
search for robot configurations is at the core of our fu-
ture work. Also, we look at the possibilities to over-
come extreme obstacles up to about 40cm in height
as this is the limit given by the manufacturer. This
might require new metrics and a more sophisticated
controller to cope with arising challenges.
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