We demonstrate the physical rearrangement of wheeled stools in a moderately cluttered indoor environment by a quadrupedal robot that autonomously achieves a user’s desired configuration. The robot’s behaviors are planned and executed by a three layer hierarchical architecture consisting of: an offline symbolic task and motion planner; a reactive layer that tracks the reference output of the deliberative layer and avoids unanticipated obstacles sensed online; and a gait layer that realizes the abstract unicycle commands from the reactive module through appropriately coordinated joint level torque feedback loops. This work also extends prior formal results about the reactive layer to a broad class of nonconvex obstacles. Our design is verified both by formal proofs as well as empirical demonstration of various assembly tasks.
This paper applies an extension of classical averaging methods to hybrid dynamical systems, thereby achieving formally specified, physically effective and robust instances of all virtual bipedal gaits on a quadrupedal robot. Gait specification takes the form of a three parameter family of coupling rules mathematically shown to stabilize limit cycles in a low degree of freedom template: an abstracted pair of vertical hoppers whose relative phase locking encodes the desired physical leg patterns. These coupling rules produce the desired gaits when appropriately applied to the physical robot.
This paper demonstrates a fully sensor-based reactive homing behavior on a physical quadrupedal robot, using onboard sensors, in simple (convex obstacle-cluttered) unknown, GPS-denied environments. Its implementation is enabled by our empirical success in controlling the legged machine to approximate the (abstract) unicycle mechanics assumed by the navigation algorithm, and our proposed method of range-only target localization using particle filters.
We document empirically stable bounding using an actively powered spine on the Inu quadrupedal robot, and propose a reduced-order model to capture the dynamics associated with this additional, actuated spine degree of freedom. This model is sufficiently accurate as to roughly describe the robots mass center trajectory during a bounding limit cycle, thus making it a potential option for low dimensional representations of spine actuation in steady-state legged locomotion.
This video documents our field experiments at White Sands National Monument with the RHex robot, in March 2016. It demonstrates the great potential for RHex to assist aeolian scientists in desert research. By collecting data through sensors mounted on RHex, we gather transformative datasets that are required to calibrate and verify existing and future dune dynamics and sand transport models. This work is produced by Nicholas Lancaster, Desert Research Institute, and will be presented at the 2016 Geological Society of America Conference.
The Penn Jerboa showcases new leaping behaviors and demonstrates an innovative method of describing and categorizing these leaps across robot platforms.
http://www.ghostrobotics.io Ghost Minitaur™ is a patent-pending medium-sized legged robot highly adept at perceiving tactile sensations. Its high torque motors, motor controllers, and specialized leg design allow this machine run and jump over difficult terrain, climb fences and stairs, and even open doors. High-speed and high-resolution encoders let the robot see and feel the ground through the motors and adapt faster than the blink of an eye. Minitaur was developed in Kod*lab.
Playlist of all of Kod*lab Research Videos