258 research outputs found
Path Planning Tolerant to Degraded Locomotion Conditions
Mobile robots, especially those driving outdoors and in unstructured terrain,
sometimes suffer from failures and errors in locomotion, like unevenly
pressurized or flat tires, loose axes or de-tracked tracks. Those are errors
that go unnoticed by the odometry of the robot. Other factors that influence
the locomotion performance of the robot, like the weight and distribution of
the payload, the terrain over which the robot is driving or the battery charge
could not be compensated for by the PID speed or position controller of the
robot, because of the physical limits of the system. Traditional planning
systems are oblivious to those problems and may thus plan unfeasible
trajectories. Also, the path following modules oblivious to those problems will
generate sub-optimal motion patterns, if they can get to the goal at all.
In this paper, we present an adaptive path planning algorithm that is
tolerant to such degraded locomotion conditions. We do this by constantly
observing the executed motions of the robot via simultaneously localization and
mapping (SLAM). From the executed path and the given motion commands, we
constantly on the fly collect and cluster motion primitives (MP), which are in
turn used for planning. Therefore the robot can automatically detect and adapt
to different locomotion conditions and reflect those in the planned paths
Search-based Motion Planning for Aggressive Flight in SE(3)
Quadrotors with large thrust-to-weight ratios are able to track aggressive
trajectories with sharp turns and high accelerations. In this work, we develop
a search-based trajectory planning approach that exploits the quadrotor
maneuverability to generate sequences of motion primitives in cluttered
environments. We model the quadrotor body as an ellipsoid and compute its
flight attitude along trajectories in order to check for collisions against
obstacles. The ellipsoid model allows the quadrotor to pass through gaps that
are smaller than its diameter with non-zero pitch or roll angles. Without any
prior information about the location of gaps and associated attitude
constraints, our algorithm is able to find a safe and optimal trajectory that
guides the robot to its goal as fast as possible. To accelerate planning, we
first perform a lower dimensional search and use it as a heuristic to guide the
generation of a final dynamically feasible trajectory. We analyze critical
discretization parameters of motion primitive planning and demonstrate the
feasibility of the generated trajectories in various simulations and real-world
experiments.Comment: 8 pages, submitted to RAL and ICRA 201
Planning Hybrid Driving-Stepping Locomotion on Multiple Levels of Abstraction
Navigating in search and rescue environments is challenging, since a variety
of terrains has to be considered. Hybrid driving-stepping locomotion, as
provided by our robot Momaro, is a promising approach. Similar to other
locomotion methods, it incorporates many degrees of freedom---offering high
flexibility but making planning computationally expensive for larger
environments.
We propose a navigation planning method, which unifies different levels of
representation in a single planner. In the vicinity of the robot, it provides
plans with a fine resolution and a high robot state dimensionality. With
increasing distance from the robot, plans become coarser and the robot state
dimensionality decreases. We compensate this loss of information by enriching
coarser representations with additional semantics. Experiments show that the
proposed planner provides plans for large, challenging scenarios in feasible
time.Comment: In Proceedings of IEEE International Conference on Robotics and
Automation (ICRA), Brisbane, Australia, May 201
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