3,590 research outputs found
Contact-Implicit Trajectory Optimization Based on a Variable Smooth Contact Model and Successive Convexification
In this paper, we propose a contact-implicit trajectory optimization (CITO)
method based on a variable smooth contact model (VSCM) and successive
convexification (SCvx). The VSCM facilitates the convergence of gradient-based
optimization without compromising physical fidelity. On the other hand, the
proposed SCvx-based approach combines the advantages of direct and shooting
methods for CITO. For evaluations, we consider non-prehensile manipulation
tasks. The proposed method is compared to a version based on iterative linear
quadratic regulator (iLQR) on a planar example. The results demonstrate that
both methods can find physically-consistent motions that complete the tasks
without a meaningful initial guess owing to the VSCM. The proposed SCvx-based
method outperforms the iLQR-based method in terms of convergence, computation
time, and the quality of motions found. Finally, the proposed SCvx-based method
is tested on a standard robot platform and shown to perform efficiently for a
real-world application.Comment: Accepted for publication in ICRA 201
Non-prehensile Planar Manipulation via Trajectory Optimization with Complementarity Constraints
Contact adaption is an essential capability when manipulating objects. Two
key contact modes of non-prehensile manipulation are sticking and sliding. This
paper presents a Trajectory Optimization (TO) method formulated as a
Mathematical Program with Complementarity Constraints (MPCC), which is able to
switch between these two modes. We show that this formulation can be applicable
to both planning and Model Predictive Control (MPC) for planar manipulation
tasks. We numerically compare: (i) our planner against a mixed integer
alternative, showing that the MPCC planer converges faster, scales better with
respect to time horizon, and can handle environments with obstacles; (ii) our
controller against a state-of-the-art mixed integer approach, showing that the
MPCC controller achieves better tracking and more consistent computation times.
Additionally, we experimentally validate both our planner and controller with
the KUKA LWR robot on a range of planar manipulation tasks
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