Dynamic Control of Soft Robots with Internal Constraints in the Presence of Obstacles The development of effective reduced order models for soft robots is paving the way toward the development of a new generation of model based techniques, which leverage classic rigid robot control. However, several soft robot features differentiate the softbodied case from the rigidbodied one. First, soft robots are built to work in the environment, so the presence of obstacles in their path should always be explicitly accounted by their control systems. Second, due to the complex kinematics, the actuation of soft robots is mapped to the state space nonlinearly resulting in spaces with different sizes. Moreover, soft robots often include internal constraints and thus actuation is typically limited in the range of action and it is often unidirectional. This paper proposes a control pipeline to tackle the challenge of controlling soft robots with internal constraints in environments with obstacles. We show how the constraints on actuation can be propagated and integrated with geometrical constraints, taking into account physical limits imposed by the presence of obstacles. We present a hierarchical control architecture capable of handling these constraints, with which we are able to regulate the position in space of the tip of a soft robot with the discussed characteristics.

Passive Model Reduction and Switching for Fast Soft Object Simulation with Intermittent Contacts We propose a novel fast simulation framework for soft objectsrobots with intermittent contacts, whose contact areaslocations can be varying. We first perform a balanced model reduction of the fullorder FEM (finite element method) model for each contact mode with the contact forcing as the input and the shape of the objectrobot as the output. We then devise the strategy of passive model reduction and passive model switching of these reducedorder models (each with its contact mode) utilizing the techniques of our recentlyproposed passive midpoint integration (for the passivity of each reducedorder model) and simultaneous diagonalization (for the passivity of model reduction and model switching). The efficacy of the theory is then demonstrated with simulation and experimental results.