paper-with-me

Papers

Zero-Error Tracking for Autonomous Vehicles through Epsilon-Trajectory Generation

2020-07-20

This paper presents a control method and trajectory planner for vehicles with first-order nonholonomic constraints that guarantee asymptotic convergence to a time-indexed trajectory. To overcome the nonholonomic constraint, a fixed point in front of the vehicle can be controlled to track a desired trajectory, albeit with a steady-state error. To eliminate steady state error, a sufficiently smooth trajectory is reformulated for the new reference point such that, when tracking the new trajectory, the vehicle asymptotically converges to the original trajectory. The resulting zero-error tracking law is demonstrated through a novel framework for creating time-indexed Clothoids. The Clothoids can be planned to pass through arbitrary waypoints using traditional methods yet result in trajectories that can be followed with zero steady-state error. The results of the control method and planner are illustrated in simulation wherein zero-error tracking is demonstrated.

📄 PDF Abstract BibTeX arXiv:2007.10441

Code (0)

등록된 구현이 없습니다.

Tasks

Autonomous Vehicles

Similar Papers 제목 키워드 기반

Lateral String Stability for Vehicle Platoons: Formulation, Definition, and Analysis

2026-05-03 · Sixu Li, Swaroop Darbha, Yang Zhou arxiv

Platooning of connected and automated vehicles provides significant benefits in terms of energy efficiency, traffic throughput, and, most critically, safety. These safety benefits depend on string stability, which dictat…

Autonomous Vehicles

Cross-Platform Control for Autonomous Surface Vehicles via Adaptive Reinforcement Learning

2026-07-02 · Ruiheng Jiang, Thomas Bi, Raffaello D'Andrea, Aswin Ramachandran arxiv

Autonomous surface vehicles vary widely in hydrodynamic and actuation characteristics, yet most controllers are designed for single-platform deployment. We present an adaptive reinforcement learning approach for trajecto…

Reinforcement Learning

Zero-shot Deep Reinforcement Learning Driving Policy Transfer for Autonomous Vehicles based on Robust Control

2018-12-07 · Zhuo Xu, Chen Tang, Masayoshi Tomizuka

Although deep reinforcement learning (deep RL) methods have lots of strengths that are favorable if applied to autonomous driving, real deep RL applications in autonomous driving have been slowed down by the modeling gap…

Autonomous DrivingAutonomous VehiclesDeep Reinforcement LearningReinforcement Learning

Robust Trajectory Tracking of Autonomous Surface Vehicle via Lie Algebraic Online MPC

2025-11-24 · Yinan Dong, Ziyu Xu, Tsimafei Lazouski, Sangli Teng 외 arxiv

Autonomous surface vehicles (ASVs) are influenced by environmental disturbances such as wind and waves, making accurate trajectory tracking a persistent challenge in dynamic marine conditions. In this paper, we propose a…

Computational Efficiency

Reference-Free Formula Drift with Reinforcement Learning: From Driving Data to Tire Energy-Inspired, Real-World Policies

2024-10-28 · Franck Djeumou, Michael Thompson, Makoto Suminaka, John Subosits

The skill to drift a car--i.e., operate in a state of controlled oversteer like professional drivers--could give future autonomous cars maximum flexibility when they need to retain control in adverse conditions or avoid …