Climbing Robotics
Climbing Robots
Overview
The overall goal of this program is to develop a fundamental
understanding of the problem-solving activity underlying climbing,
then to create new technologies based on this understanding that will
enable non-specific multi-limbed robots to free-climb natural,
unstructured, vertical terrain. Climbing is regarded by human
climbers to be a physical problem-solving activity in a highly
unstructured environment. Overall, climbing involves a tight
combination of fast but insightful reasoning, goal-directed sensing,
and reactive execution. Sophisticated planning is required to handle
hard constraints (e.g., equilibrium, torque limits, collision) on the
agent's motion, as well as softer ones (e.g., uncertainties, risk
level, energy consumption). Precise sensing (e.g., tactile, vision) is
used to search and detect potential holds in the unstructured rock
face, estimate the location and characteristics of contact points,
and anticipate or detect slip. Fine control is needed to maintain
balance through careful distribution of contact forces. A solution
to the climbing problem requires that these activities be fused into
a seamless process.
More Information
ARL Research on this project has focused in two main areas. The first
is that of motion planning, with research by Tim Bretl. He uses
probabilistic planning techniques to design a route for a robot to
climb. The planned route enables the robot to stay stable at all times while ascending the
wall using only friction in the endpoints.
The second area of research, by Teresa Miller, focuses on control techniques for a
climbing robot. It has been shown in experiments that simple PD
control of robotic joint angles is insufficient to deal with the many
system uncertainties. For that reason we are investigating types of
control which can generate torque trajectories that follow a planned
path while minimizing the likelihood of slip.
ARL Main Page.
For questions or comments contact: www@arl.stanford.edu
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