Highly Dynamic Bipedal Locomotion in Unknown, Loosely Structured Environments
Project Abstract/Statement of Work:
Bipedal robots offer a more versatile solution to traversing uneven outdoor terrain and cluttered indoor environments compared to their wheeled counterparts. The state-of-the-art control algorithms for dynamic biped locomotion are capable of providing stabilizing feedback for a biped robot blindly walking across uneven terrain with height disturbances of about 10 cm per step; however, without perceiving the environment, the application of legged robots remains extremely limited.
The ultimate objective of this project is the development of an integrated perception and planning systems that can operate in conjunction with available stabilizing walking control algorithms.
We aim to integrate robot perception onto the Cassie-series biped robot to enable:
autonomous navigation through a forest at “real-life” speeds (walking at ∼ 0.5 m/s)
walking across highly uneven terrain while maintaining stability (UM’s Wave Field)
planning and safely maneuvering around obstacles in complex indoor environments
autonomously walking up and down flights of stairs found in homes
These goals will be achieved by extending state-of-the-art SLAM algorithms to include leg odometry, developing novel path-planning algorithms, and designing feedback controllers that make use of terrain information by utilizing machine learning and trajectory optimization techniques.