轻量级框架让阿克曼机器人实时避障更安全
NMPCB: A Lightweight and Safety-Critical Motion Control Framework for Ackermann Mobile Robot
- 用轻量神经网络预判目标点生成轨迹
- 引入控制屏障函数对偶问题，避障同时提速30%以上
- 适合对实时性与安全性要求高的移动机器人
在多障碍物环境中，机器人运动控制的实时性与安全性长期难以兼顾，传统方法常在此二者间权衡。本文提出一种新型运动控制框架NMPCB，由基于神经网络的路径规划器与基于控制屏障函数（CBF）的模型预测控制（MPC）控制器构成。规划器通过轻量神经网络预测下一个目标点，并生成参考轨迹供控制器使用。控制器设计中，引入控制屏障函数的对偶问题作为避障约束，可在确保运动安全的同时显著降低计算开销。控制器直接输出控制指令以跟踪参考轨迹，实现了实时性能与安全性的平衡。通过数值仿真与真实实验验证了该框架的有效性。
原文摘要 · Abstract (English)
In multi-obstacle environments, real-time performance and safety in robot motion control have long been challenging issues, as conventional methods often struggle to balance the two. In this paper, we propose a novel motion control framework composed of a Neural network-based path planner and a Model Predictive Control (MPC) controller based on control Barrier function (NMPCB) . The planner predicts the next target point through a lightweight neural network and generates a reference trajectory for the controller. In the design of the controller, we introduce the dual problem of control barrier function (CBF) as the obstacle avoidance constraint, enabling it to ensure robot motion safety while significantly reducing computation time. The controller directly outputs control commands to the robot by tracking the reference trajectory. This framework achieves a balance between real-time performance and safety. We validate the feasibility of the framework through numerical simulations and real-world experiments.
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