用单次射击法实现自平衡车实时精准避障控制
Nonlinear Model Predictive Control with Single-Shooting Method for Autonomous Personal Mobility Vehicle
- 采用非线性模型预测控制结合单次射击法求解最优路径
- 仿真中成功达成目标位姿，全程满足避障等约束条件
- 适合研究自动驾驶车辆实时控制与运动规划的工程师
本文针对自主个人移动车辆——单人电动自动驾驶运输车（SEATER），提出一种基于非线性模型预测控制（NMPC）的控制方法。该方法采用单次射击法通过非线性规划（NLP）求解最优控制问题（OCP），用于具有差速驱动系统的非完整车辆。系统利用里程计数据作为定位反馈，引导车辆到达目标位姿，同时满足障碍物避让等约束。在Gazebo仿真环境中构建了SEATER模型和测试场景，控制算法在机器人操作系统（ROS）框架内实现。仿真结果表明，所提方法能有效引导车辆抵达目标位置，并在无障碍物及静态障碍物环境下均满足各项约束。研究进一步验证了单次射击法在该类场景下的鲁棒性与实时可行性。
原文摘要 · Abstract (English)
This paper introduces a proposed control method for autonomous personal mobility vehicles, specifically the Single-passenger Electric Autonomous Transporter (SEATER), using Nonlinear Model Predictive Control (NMPC). The proposed method leverages a single-shooting approach to solve the optimal control problem (OCP) via non-linear programming (NLP). The proposed NMPC is implemented to a non-holonomic vehicle with a differential drive system, using odometry data as localization feedback to guide the vehicle towards its target pose while achieving objectives and adhering to constraints, such as obstacle avoidance. To evaluate the performance of the proposed method, a number of simulations have been conducted in both obstacle-free and static obstacle environments. The SEATER model and testing environment have been developed in the Gazebo Simulation and the NMPC are implemented within the Robot Operating System (ROS) framework. The simulation results demonstrate that the NMPC-based approach successfully controls the vehicle to reach the desired target location while satisfying the imposed constraints. Furthermore, this study highlights the robustness and real-time effectiveness of NMPC with a single-shooting approach for autonomous vehicle control in the evaluated scenarios.
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