An unsupported bipedal robot at the University of Michigan can now walk down steep slopes, through a thin layer of snow, and over uneven and unstable ground. The robot’s feedback control algorithms should be able to help other two-legged robots as well as powered prosthetic legs gain similar capabilities.
“The robot has no feeling in her tiny feet, but she senses the angles of her joints-for instance, her knee angles, hip angles, and the rotation angle of her torso,” said Jessy Grizzle, PhD, professor of electrical engineering and computer science and of mechanical engineering.
MARLO is Grizzle’s first robot that can walk (and fall) in any direction, known as 3D walking; the robot was built by his long-term collaborator Jonathan Hurst, PhD, an associate professor of mechanical engineering at Oregon State University.
Xingye (Dennis) Da, a doctoral student in mechanical engineering, developed a way to control the robot with two 2D algorithms. The main controller handles the forward and backward motion and balance, while a second controller handles side-to-side balance. Da created a library of 15 gaits to handle different walking speeds and ground heights. Each gait is optimized for energy efficiency-making the movement as natural as possible-and respects the constraints of the robot. MARLO demonstrated this algorithm in tests walking through snow, down a steep hill, and on randomly stacked plywood squares covered in AstroTurf and scattered with more obstacles. However, this approach works as long as the robot doesn’t have to make quick turns or sideways movements. To achieve true agility, the control algorithm must be more integrated.
Working toward this goal, Brent Griffin, a doctoral student in electrical engineering and computer science, is developing a fully 3D controller. Through computer simulations, Griffin tested the 3D controller against a range of terrain and speed disturbances to ensure reliable walking in varying real-world conditions. MARLO demonstrated this control method by walking over plywood obstacles in the lab and for hundreds of steps outdoors on pavement. The researchers believe that MARLO’s gait using this algorithm is the most efficient yet for a bipedal robot.
Over the summer, the team plans to merge the two control algorithms into one super-algorithm that can support more agile movements.
“The major product of our research is a recipe for legged locomotion,” said Grizzle, the Elmer G. Gilbert distinguished university professor of engineering and the Jerry W. and Carol L. Levin professor of engineering.
The codes that Grizzle’s team used to make MARLO walk on flat ground serve as the basis for algorithms developed in other labs for different kinds of robots. Robert Gregg, PhD, an assistant professor of mechanical engineering and bioengineering at the University of Texas at Dallas, adapted the algorithm to control a prosthetic lower leg. When a person with an amputation tried out Gregg’s robotic prosthetic leg on a treadmill, he was able to walk naturally.
“The ability of MARLO to gracefully navigate uneven terrains is very exciting for my work in prosthetics,” Gregg said. “We hope to encode similar abilities into our robotic prosthetic leg so that lower-limb amputees can just as easily walk about the community without having to think about the terrain.”