Trajectory Splitting: A Distributed Formulation for Collision Avoiding Trajectory Optimization

TL;DR

This paper introduces a distributed trajectory optimization method called trajectory splitting, which divides a long trajectory into segments to enable high-quality, collision-free paths with reduced computation time by parallelizing subproblem solutions.

Contribution

It proposes a novel distributed formulation for collision-avoiding trajectory optimization that allows dense waypoints and efficient computation through parallel subproblem solving and consensus updates.

Findings

Improved computational efficiency over existing algorithms.

Enables higher-quality trajectories with denser waypoints.

Demonstrates effectiveness in unstructured environments.

Abstract

Efficient trajectory optimization is essential for avoiding collisions in unstructured environments, but it remains challenging to have both speed and quality in the solutions. One reason is that second-order optimality requires calculating Hessian matrices that can grow with $O(N^2)$ with the number of waypoints. Decreasing the waypoints can quadratically decrease computation time. Unfortunately, fewer waypoints result in lower quality trajectories that may not avoid the collision. To have both, dense waypoints and reduced computation time, we took inspiration from recent studies on consensus optimization and propose a distributed formulation of collocated trajectory optimization. It breaks a long trajectory into several segments, where each segment becomes a subproblem of a few waypoints. These subproblems are solved classically, but in parallel, and the solutions are fused into a single trajectory with a consensus constraint that enforces continuity of the segments through a consensus update. With this scheme, the quadratic complexity is distributed to each segment and enables solving for higher-quality trajectories with denser waypoints. Furthermore, the proposed formulation is amenable to using any existing trajectory optimizer for solving the subproblems. We compare the performance of our implementation of trajectory splitting against leading motion planning algorithms and demonstrate the improved computational efficiency of our method.