Researchers at MIT have developed a robotic cheetah – a first of its kind robot that has been trained to see and jump over hurdles as it runs.
Much like how humans plan their path on the go, the robotic cheetah also plans out its path and when it detects an approaching obstacle, it estimates that object’s height and distance. The robot then 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.
Internal experiments have shown that the robot successfully clears obstacles up to 18 inches tall — more than half of the robot’s own height — while maintaining an average running speed of 5 miles per hour.
“A running jump is a truly dynamic behavior,” says Sangbae Kim, an assistant professor of mechanical engineering at MIT. “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.”
The robotic cheetah has been fitted with LIDAR — a visual system that uses reflections from a laser to map terrain. Based on the data available fro LIDAR, the researchers developed a three-part algorithm using which the robot plans out its path. Thanks to both vision and path-planning system onboard the robot, it has complete autonomous control.
The first part of the algorithm enables the robot to detect an obstacle and estimate its size and distance. To simplify this task for the robot, researchers devised a formula that represents the ground as a straight line, and any obstacles as deviations from that line. Using this simplified representation of the ground, the robot can estimate an obstacle’s height and distance from itself.
The second part of the algorithm kicks in after the robot has detected an obstacle. This part of the algorithm allows 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.”