.png){kind=icon}
.png){kind=icon}
Return
Smoothing-homotopy-based sequential convex programming for trajectory optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ast.2025.109995.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes a new smoothing-homotopy-based sequential convex programming (SCP) method for general trajectory optimization problems. The surrogates, derived from convolving the smoothing kernel with the terminal states, are firstly incorporated into the terminal constraints as replacements of the original ones. The smoothing parameter, taken as the homotopic parameter, decreases from a larger value to zero, with which the corresponding optimization problem gradually transitions from an easier and smoothed counterpart to the original one. Both the modified Chebyshev-Picard iteration (MCPI) approach and the trapezoidal rule are employed to transcribe the continuous-time optimization problem into a series of finite-dimensional subproblems, respectively, which are then solved by the primal-dual interior-point solver with the aid of convexity techniques. Numerical simulations for an ascent trajectory optimization problem are provided to demonstrate the performance of the proposed method, showcasing its superior convergence compared to the standard SCP methods.
Keywords:
Smoothing homotopy
Modified Chebyshev-Picard iteration
Sequential convex programming
Trajectory optimization
Journal
IF:
5.8
Papers:
10.0K
Citations:
3.0W