2025/09/30 by Mattia Piazza, Mattia Piccinini, Piazza, Mattia +7
Engineering · #Advanced Control Systems Optimization #Aerospace Engineering and Control Systems #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Optimization and Control (math.OC) #Real-time simulation and control systems #Robotics (cs.RO) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2509.26428
openalex publication_date 2025/09/30 · openalex created_date 2025/10/19 · openalex updated_date 2026/07/30
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 \underlineForward-\underlineBackward algorithm with \underlineGeneric \underlineAcceleration 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://anonymous.4open.science/r/FBpublicRAL.