25,045 research outputs found
SMUG Planner: A Safe Multi-Goal Planner for Mobile Robots in Challenging Environments
Robotic exploration or monitoring missions require mobile robots to
autonomously and safely navigate between multiple target locations in
potentially challenging environments. Currently, this type of multi-goal
mission often relies on humans designing a set of actions for the robot to
follow in the form of a path or waypoints. In this work, we consider the
multi-goal problem of visiting a set of pre-defined targets, each of which
could be visited from multiple potential locations. To increase autonomy in
these missions, we propose a safe multi-goal (SMUG) planner that generates an
optimal motion path to visit those targets. To increase safety and efficiency,
we propose a hierarchical state validity checking scheme, which leverages
robot-specific traversability learned in simulation. We use LazyPRM* with an
informed sampler to accelerate collision-free path generation. Our iterative
dynamic programming algorithm enables the planner to generate a path visiting
more than ten targets within seconds. Moreover, the proposed hierarchical state
validity checking scheme reduces the planning time by 30% compared to pure
volumetric collision checking and increases safety by avoiding high-risk
regions. We deploy the SMUG planner on the quadruped robot ANYmal and show its
capability to guide the robot in multi-goal missions fully autonomously on
rough terrain
Efficient Multi-Robot Motion Planning for Unlabeled Discs in Simple Polygons
We consider the following motion-planning problem: we are given unit
discs in a simple polygon with vertices, each at their own start position,
and we want to move the discs to a given set of target positions. Contrary
to the standard (labeled) version of the problem, each disc is allowed to be
moved to any target position, as long as in the end every target position is
occupied. We show that this unlabeled version of the problem can be solved in
time, assuming that the start and target positions are at
least some minimal distance from each other. This is in sharp contrast to the
standard (labeled) and more general multi-robot motion-planning problem for
discs moving in a simple polygon, which is known to be strongly NP-hard
Online, interactive user guidance for high-dimensional, constrained motion planning
We consider the problem of planning a collision-free path for a
high-dimensional robot. Specifically, we suggest a planning framework where a
motion-planning algorithm can obtain guidance from a user. In contrast to
existing approaches that try to speed up planning by incorporating experiences
or demonstrations ahead of planning, we suggest to seek user guidance only when
the planner identifies that it ceases to make significant progress towards the
goal. Guidance is provided in the form of an intermediate configuration
, which is used to bias the planner to go through . We
demonstrate our approach for the case where the planning algorithm is
Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our
approach allows to compute highly-constrained paths with little domain
knowledge. Without our approach, solving such problems requires
carefully-crafting domain-dependent heuristics
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