Superquadric Object Representation as a Control Barrier Function for Obstacle Avoidance

Ensuring successful robot task performance and safety in unstructured environments is a critical challenge in robotics. A key requirement in addressing this challenge is to accurately model the scene in which robots operate to effectively perform obstacle avoidance. State-of-the-art approaches for online obstacle avoidance generally rely on simplified representations (e.g. ellipsoids) that often result in highly conservative collision models that limit their effectiveness. To address this challenge, this paper proposes a controller which leverages superquadrics to reduce conservative behaviours. The parametric nature of superquadrics allow for accurate modelling of the robotic system and the environment. The calculated distance between the superquadrics informs the construction of a Control Barrier Function, which is integrated into a Quadratic Program to enable obstacle avoidance. Finally, by formulating the problem in the operating space and considering object volumes, the proposed controller is able to utilise rotational deviation to achieve safer behaviours. The proposed approach is evaluated through simulation and real-world experiments. The simulation results demonstrate the effectiveness of the proposed framework, while results from the real-world experiments highlight the advantages of the framework in different scenarios.