The purpose of this project is researching flexible tensegrity spines for walking robots. Robots with flexible spines have many potential advantages over those with rigid body structures. Motion between a robot’s hips and shoulders could allow for more complex and efficient locomotion for quadrupeds, as well as greater ability to traverse unknown terrain and interact with unknown environments while keeping stable and safe. This project, the walking quadrupeds is designed to utilize a tensegrity spine as its backbone.

The purpose of this project is to study the stability analysis for the dynamics of a four-legged robot with a soft tensegrity spine which is a challenging task due to the complexity of the system. The stability of a robot can be analyzed in several ways.

For legged robots, motorized wheels offer a number of significant advantages over feet. Locked wheels can behave similarly to point feet, and unlocking them gives legged robots the ability to travel both faster and more efficiently. To commercialize wheel-legged robots for a wide variety of tasks including mapping, inspection, disaster relief, and logistics in urban environments, to name a few.

The proposed control system utilizes a combination of model-based and learning-based control techniques to achieve robust and efficient motion control. The control system takes into account the dynamics of the flexible spine, which is modeled using tensegrity theory, and uses this information to generate appropriate control signals for the actuators. The control system also incorporates sensor feedback to ensure accurate tracking of the desired motion.

In this project, we present a novel approach for optimizing the tension force on the flexible spine based on a tensegrity mechanism during the gait cycle of a four-legged robot. The proposed optimization method utilizes a combination of model-based and learning-based optimization techniques to achieve efficient and robust optimization of the tension force. The optimization process takes into account the dynamics of the flexible spine modeled using tensegrity theory and uses this information to generate the appropriate tension force.