bots IN BRIEF
16 SERVO 07.2015
LEAP OF FAITH
In a leap for robot development, the MIT
researchers who built a
robotic cheetah have
now trained it to see and
jump over hurdles as it
runs — making this the
first four-legged robot to
run and jump over
To get a running jump, the robot plans out its path much
like a human runner: As it detects an approaching obstacle, it
estimates that object’s height and distance. The robot gauges
the best position from which to jump, and adjusts its stride
to land just short of the obstacle before exerting enough
force to push up and over. Based on the obstacle’s height, the
robot then applies a certain amount of force to land safely
before resuming its initial pace.
In experiments on a treadmill and an indoor track, the
cheetah robot successfully cleared obstacles up to 18 inches
tall — more than half of the robot’s own height — while
maintaining an average running speed of five miles per hour.
“A running jump is a truly dynamic behavior,” said
Sangbae Kim, an assistant professor of mechanical
engineering at MIT in a recent interview with Jennifer Chu
from the MIT News Office. “You have to manage balance and
energy, and be able to handle impact after landing. Our robot
is specifically designed for those highly dynamic behaviors.”
Kim and his colleagues — including research scientist
Hae won Park and postdoc Patrick Wensing — planned to
demonstrate their cheetah’s running jump at the DARPA
Robotics Challenge in June and present a paper detailing the
autonomous system in July at the conference, Robotics:
Science and Systems.
Last September, the group demonstrated that the
robotic cheetah was able to run untethered — a feat that
Kim notes the robot performed “blind,” without the use of
cameras or other vision systems.
Now, the robot can “see,” with the use of onboard
LIDAR — a visual system that uses reflections from a laser
to map terrain. The team developed a three-part algorithm to
plan out the robot’s path based on LIDAR data. Both the
vision and path-planning system are onboard the robot, giving
it complete autonomous control.
The algorithm’s first component enables the robot to
detect an obstacle and estimate its size and distance. The
researchers devised a formula to simplify a visual scene,
representing the ground as a straight line and any obstacles
as deviations from that line. With this formula, the robot can
estimate an obstacle’s height and distance from itself.
Once the robot has detected an obstacle, the second
component of the algorithm kicks in, allowing the robot to
adjust its approach while nearing the obstacle. Based on the
obstacle’s distance, the algorithm predicts the best position
from which to jump in order to safely clear it, then
backtracks from there to space out the robot’s remaining
strides, speeding up or slowing down in order to reach the
optimal jumping-off point.
This “approach adjustment algorithm” runs on-the-fly,
optimizing the robot’s stride with every step. The
optimization process takes about 100 milliseconds to
complete — about half the time of a single stride.
When the robot reaches the jumping-off point, the third
component of the algorithm takes over to determine its
jumping trajectory. Based on an obstacle’s height and the
robot’s speed, the researchers came up with a formula to
determine the amount of force the robot’s electric motors
should exert to safely launch the robot over the obstacle.
The formula essentially cranks up the force applied in the
robot’s normal bounding gait, which Kim notes is essentially
“sequential executions of small jumps.” To see it in action, go