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ECE PhD Dissertation Defense: Aykut Onol
November 25, 2020 @ 12:00 pm - 1:00 pm
PhD Dissertation Defense: Planning of Contact-Interaction Trajectories Using Numerical Optimization
Location: Zoom Link
Abstract: Dynamic multi-contact behaviors, such as locomotion and item manipulation, remain to be a challenge for today’s robotic systems. This is primarily due to the discontinuous and non-smooth dynamics introduced by contacts. For mobile manipulators (e.g., humanoids) to become useful for dangerous, dirty, and dull tasks, such as those in disaster response, they need to be capable of interacting with their cluttered, constrained, and changing environments. It is therefore essential to develop methods that would enable robots to plan and execute contact-rich motions in dynamic surroundings.
In this dissertation research, we investigate the planning of contact-interaction trajectories and utilize numerical optimal control techniques to solve this problem in a generalizable and computationally-tractable way. We develop a contact-implicit trajectory optimization framework for the automatic discovery of dynamic contact-rich behaviors given only a high-level goal, i.e., the desired configuration of the environment. A variable smooth contact model is introduced to improve the convergence of gradient-based optimization without compromising the physical fidelity of resulting motions. This is achieved by employing smooth virtual forces that act as a decoupled relaxation of the rigid-body contact model. Second, we develop a sequential convex optimization procedure that provides reliable convergence characteristics while solving this non-convex problem. Third, a penalty loop approach is proposed to generalize this method to a wide range of robotic applications.
In addition to these, we develop a novel Coulomb friction model and an on-the-fly contact constraint activation method using state-triggered constraints, STCs. STCs are a more modular alternative to complementarity constraints which are widely used to model discrete aspects in contact-related problems. Our extensive simulation experiments demonstrate that STCs hold immense promise to efficiently model a broad range of discrete elements in the planning and control of contact-interaction trajectories. As a result, this dissertation presents methods that enable the planning of dynamic contact-rich behaviors for different robots and tasks without requiring any parameter tuning or tailored initial guess.