Inequality Constrained Trajectory Optimization with A Hybrid Multiple-shooting iLQR
This work addresses trajectory optimization for robotic systems, offering an incremental improvement by enhancing constraint handling and initialization in iLQR methods.
The paper tackles the problem of trajectory optimization in robotics by proposing a hybrid constrained iLQR variant with a multiple-shooting framework to handle inequality constraints and infeasible state initialization, resulting in outperformance over common methods like collocation and shooting in various constrained problems.
Trajectory optimization has been used extensively in robotic systems. In particular, iterative Linear Quadratic Regulator (iLQR) has performed well as an off-line planner and online nonlinear model predictive control solver, with a lower computational cost. However, standard iLQR cannot handle any constraints or perform reasonable initialization of a state trajectory. In this paper, we propose a hybrid constrained iLQR variant with a multiple-shooting framework to incorporate general inequality constraints and infeasible states initialization. The main technical contributions are twofold: 1) In addition to inheriting the simplicity of the initialization in multiple-shooting settings, a two-stage framework is developed to deal with state and/or control constraints robustly without loss of the linear feedback term of iLQR. Such a hybrid strategy offers fast convergence of constraint satisfaction. 2) An improved globalization strategy is proposed to exploit the coupled effects between line-searching and regularization, which is able to enhance the numerical robustness of the constrained iLQR approaches. Our approach is tested on various constrained trajectory optimization problems and outperforms the commonly-used collocation and shooting methods.