Several ballbots have been developed, yet only a handful have been equipped with arms to enhance their maneuverability and manipulability. The incorporation of 7-DOF arms to the CMU ballbot has presented challenges in balancing and navigation due to the constantly changing center of mass. This project aims to propose a control strategy that incorporates the arms dynamics. Our approach is to use a simplified whole-body dynamics model, with only the shoulder and elbow joints moving for each arm. This reduces the number of states and accelerates convergence. We focused on two specific tasks: navigation (straight and curved paths) and pushing a wall. Trajectories were generated using direct collocation for the navigation task and hybrid contact trajectory optimization for pushing the wall. A time-variant linear-quadratic-regulator (TVLQR) was designed to track the trajectories. The resulting trajectories were tracked with a mean-average error of less than 4 cm, even for the more complex path. These experiments represent an initial step towards unlocking the full potential of ballbots in dynamic and interactive environments.
- Stabilize Ballbot with arms during navigation
- Achieve faster speed and more agile motion with wall pushing
- Install Julia 1.6.5 https://julialang.org/downloads/
You can find the code for this task at this link: Dircol navigation without arms.
You can find the code for this task at this link: Dircol navigation with arms.
You can find the code for this task at this link: Hybrid trajectory optimization for pushing the wall.
| Ball speed constrained < 5 m/s | Ball speed constrained < 1 m/s |
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You can find the code for this task at this link: Hybrid trajectory optimization for pushing the wall.
