3,378 research outputs found
Towards an Autonomous Walking Robot for Planetary Surfaces
In this paper, recent progress in the development of
the DLR Crawler - a six-legged, actively compliant walking
robot prototype - is presented. The robot implements
a walking layer with a simple tripod and a more complex
biologically inspired gait. Using a variety of proprioceptive
sensors, different reflexes for reactively crossing obstacles
within the walking height are realised. On top of
the walking layer, a navigation layer provides the ability
to autonomously navigate to a predefined goal point in
unknown rough terrain using a stereo camera. A model
of the environment is created, the terrain traversability is
estimated and an optimal path is planned. The difficulty
of the path can be influenced by behavioral parameters.
Motion commands are sent to the walking layer and the
gait pattern is switched according to the estimated terrain
difficulty. The interaction between walking layer and navigation
layer was tested in different experimental setups
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
Practical application of pseudospectral optimization to robot path planning
To obtain minimum time or minimum energy trajectories for robots it is necessary to employ planning methods which adequately consider the platformâs dynamic properties. A variety of sampling, graph-based or local receding-horizon optimisation methods have previously been proposed. These typically use simpliïŹed kino-dynamic models to avoid the signiïŹcant computational burden of solving this problem in a high dimensional state-space. In this paper we investigate solutions from the class of pseudospectral optimisation methods which have grown in favour amongst the optimal control community in recent years. These methods have high computational efficiency and rapid convergence properties. We present a practical application of such an approach to the robot path planning problem to provide a trajectory considering the robotâs dynamic properties. We extend the existing literature by augmenting the path constraints with sensed obstacles rather than predeïŹned analytical functions to enable real world application
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