In this paper, we present a computational framework for direct trajectory optimization of general manipulation systems with unspecified contact sequences, exploiting environmental constraints as a key tool to accomplish a task. Two approaches are presented to describe the dynamics of systems with contacts, which are based on a penalty formulation and on a velocity- based time-stepping scheme, respectively. In both cases, object and environment contact forces are included among the free optimization variables, and they are rendered consistent via suitably devised sets of complementarity conditions. To maximize computational efficiency, we exploit sparsity patterns in the linear algebra expressions generated during the solution of the optimiza- tion problem and leverage Algorithmic Differentiation to calculate derivatives. The benefits of the proposed methods are evaluated in three simulated planar manipulation tasks, where essential interactions with environmental constraints are automatically synthesized and opportunistically exploited.

A Computational Framework for Environment-Aware Robotic Manipulation Planning

GABICCINI, MARCO
Membro del Collaboration Group
;
ARTONI, ALESSIO
Membro del Collaboration Group
;
PANNOCCHIA, GABRIELE
Membro del Collaboration Group
;
2018

Abstract

In this paper, we present a computational framework for direct trajectory optimization of general manipulation systems with unspecified contact sequences, exploiting environmental constraints as a key tool to accomplish a task. Two approaches are presented to describe the dynamics of systems with contacts, which are based on a penalty formulation and on a velocity- based time-stepping scheme, respectively. In both cases, object and environment contact forces are included among the free optimization variables, and they are rendered consistent via suitably devised sets of complementarity conditions. To maximize computational efficiency, we exploit sparsity patterns in the linear algebra expressions generated during the solution of the optimiza- tion problem and leverage Algorithmic Differentiation to calculate derivatives. The benefits of the proposed methods are evaluated in three simulated planar manipulation tasks, where essential interactions with environmental constraints are automatically synthesized and opportunistically exploited.
Gabiccini, Marco; Artoni, Alessio; Pannocchia, Gabriele; Gillis, J.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11568/759820
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