This paper introduces the Passive Transformable Omni-Ball (PTOB), an advanced omnidirectional wheel engineered to enhance step-climbing performance, incorporate built-in actuators, diminish vibrations, and fortify structural integrity. By modifying the omni-ball's structure from two to three segments, we have achieved improved in-wheel actuation and a reduction in vibrational feedback. Additionally, we have implemented a sliding mechanism in the follower wheels to boost the wheel's step-climbing abilities. A prototype with a 127 mm diameter PTOB was constructed, which confirmed its functionality for omnidirectional movement and internal actuation. Compared to a traditional omni-wheel, the PTOB demonstrated a comparable level of vibration while offering superior capabilities. Extensive testing in varied settings showed that the PTOB can adeptly handle step obstacles up to 45 mm, equivalent to 35 $\%$ of the wheel's diameter, in both the forward and lateral directions. The PTOB showcased robust construction and proved to be versatile in navigating through environments with diverse obstacles.
To achieve safe legged locomotion, it is important to generate motion in real-time considering various constraints in robots and environments. In this study, we propose a lightweight real-time perspective motion control system for the newly developed six-wheeled-telescopic-legged robot, Tachyon 3. In the proposed method, analytically smoothed constraints including Smooth Separating Axis Theorem (Smooth SAT) as a novel higher order differentiable collision detection for 3D shapes is applied to the Control Barrier Function (CBF). The proposed system integrating the CBF achieves online motion generation in a short control cycle of 1 ms that satisfies joint limitations, environmental collision avoidance and safe convex foothold constraints. The efficiency of Smooth SAT is shown from the collision detection time of 1 us or less and the CBF constraint computation time for Tachyon3 of several us. Furthermore, the effectiveness of the proposed system is verified through the stair-climbing motion, integrating online recognition in a simulation and a real machine.