Parameter Optimization in Trajectory Planning via Differentiable Convex Programming

TL;DR

This paper presents a differentiable sequential convex programming framework that enables end-to-end parameter optimization in trajectory planning, improving convergence, performance, and design efficiency for aerospace applications.

Contribution

It introduces a novel differentiable convex optimization approach integrated with sequential convex programming for end-to-end parameter tuning.

Findings

Enables reliable gradient-based parameter learning.

Improves numerical performance and convergence.

Enhances design efficiency in trajectory planning.

Abstract

Sequential convex programming has been established as an effective framework for solving nonconvex trajectory planning problems. However, its performance is highly sensitive to problem parameters, including trajectory variables, algorithmic hyperparameters, and physical vehicle parameters. This paper introduces a differentiable sequential convex programming framework that integrates differentiable convex optimization with sequential convex programming to enable end-to-end parameter optimization. By deriving first-order sensitivity relations of second-order cone programming solutions with respect to problem data, exact gradients of trajectory performance metrics with respect to arbitrary parameters are obtained and propagated through iterations. The effectiveness of the proposed framework is validated through three representative applications: optimal terminal-time prediction for powered landing, trust-region penalty optimization in subproblems, and surface-to-mass ratio optimization for hypersonic gliding vehicles. Simulation results show that the proposed framework enables reliable gradient-based parameter learning and significantly improves numerical performance, convergence behavior, and design efficiency. These results indicate that the differentiable sequential convex programming framework provides a powerful and general tool for vehicle design, mission optimization, and hyperparameter selection in aerospace trajectory planning.