Real-Time Velocity Profile Optimization for Time-Optimal Maneuvering with Generic Acceleration Constraints
Mattia Piazza, Mattia Piccinini, Sebastiano Taddei, Francesco Biral, Enrico Bertolazzi
AI summary
Problem
Existing velocity planning methods either sacrifice accuracy for speed using conservative box constraints, or achieve high accuracy with optimal control but at prohibitive computational costs for online planning.
Approach
FBGA discretizes the path into short segments and uses iterative forward and backward passes with a custom signed distance function to maximize velocity while strictly satisfying arbitrary, speed-dependent g-g-v acceleration limits.
Key results
- Handles complex non-convex g-g-v constraints for cars and motorcycles
- Achieves lap times within 0.11%-0.36% of optimal control baselines
- Computes profiles up to three orders of magnitude faster than OCP methods
- Maintains high accuracy even with coarse path discretization
Why it matters
Provides a computationally efficient, accurate planning tool essential for real-time trajectory generation in autonomous racing and dynamic robotics.
Abstract
The computation of time-optimal velocity profiles along prescribed paths, subject to generic acceleration constraints, is a crucial problem in robot trajectory planning, with particular relevance to autonomous racing. However, the existing methods either support arbitrary acceleration constraints at high computational cost or use conservative box constraints for computational efficiency. We propose FBGA, a new Forward-Backward algorithm with Generic Acceleration constraints, which achieves both high accuracy and low computation time. FBGA operates forward and backward passes to maximize the velocity profile in short, discretized path segments, while satisfying user-defined performance limits. Tested on five racetracks and two vehicle classes, FBGA handles complex, non-convex acceleration constraints with custom formulations. Its maneuvers and lap times closely match optimal control baselines (within 0.11%-0.36%), while being up to three orders of magnitude faster. FBGA maintains high accuracy even with coarse discretization, making it well-suited for online multi-query trajectory planning. Our open-source C++ implementation is available at: https://github.com/DRIVEWISE/ FBGA.