采用改进拓扑模型预测控制的巡检机器人动态避障行为研究

买超凡; 何丽; 王宏伟; 李知远; 杜洋鋆

doi:10.7652/xjtuxb202611009

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采用改进拓扑模型预测控制的巡检机器人动态避障行为研究

更新时间：2026-11-09

- 采用改进拓扑模型预测控制的巡检机器人动态避障行为研究
- Research on Dynamic Obstacle Avoidance Behavior of Inspection Robots Using Improved Topological Model Predictive Control
- 西安交通大学学报 2026年60卷第11期页码：95-107
- 作者机构：
  
  新疆大学机械工程学院，830047，乌鲁木齐
- 作者简介：
  
  买超凡（2002—），男，硕士生；
  何丽（通信作者），女，教授，博士生导师。
- 基金信息：
  
  新疆维吾尔自治区天山英才青年拔尖人才资助项目(2024TSYCCX0009);国家自然科学基金资助项目(62563035)
- DOI：10.7652/xjtuxb202611009
  中图分类号： TP242.6
- 收稿：2026-03-27，
  
  网络首发：2026-06-25，
  
  纸质出版：2026-11-10
- 稿件说明：
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买超凡, 何丽, 王宏伟, 等. 采用改进拓扑模型预测控制的巡检机器人动态避障行为研究[J/OL]. 西安交通大学学报,2026,60 (11):95-107. https://doi.org/10.7652/xjtuxb202611009.

MAI Chaofan, HE Li, WANG Hongwei, et al. Research on Dynamic Obstacle Avoidance Behavior of Inspection Robots Using Improved Topological Model Predictive Control[J/OL]. Journal of Xi'an Jiaotong University,2026,60 (11):95-107. https://doi.org/10.7652/xjtuxb202611009.
买超凡, 何丽, 王宏伟, 等. 采用改进拓扑模型预测控制的巡检机器人动态避障行为研究[J/OL]. 西安交通大学学报,2026,60 (11):95-107. https://doi.org/10.7652/xjtuxb202611009. DOI：

MAI Chaofan, HE Li, WANG Hongwei, et al. Research on Dynamic Obstacle Avoidance Behavior of Inspection Robots Using Improved Topological Model Predictive Control[J/OL]. Journal of Xi'an Jiaotong University,2026,60 (11):95-107. https://doi.org/10.7652/xjtuxb202611009. DOI：

摘要

针对巡检机器人在动态行人环境中避障不稳定与易陷入死锁的问题，提出了一种融合弹性恢复与互惠责任的控制障碍函数拓扑模型预测控制（ERCBF-TMPC）算法。该算法以控制障碍函数为底层约束，保证预测时域内无碰撞；引入弹性恢复机制，将刚性安全边界松弛为弹性缓冲层，使机器人在安全边界被突破时自主恢复可行解；建立互惠责任分配模型，实现从相互避让到机器人全责的连续调节，从而抑制振荡与死锁；在此基础上，完整框架ERCBF-TMPC++算法在求解器集合中引入无引导基准规划器以增强鲁棒性。在直线道路、凸型道路与开放区域3类仿真环境中开展仿真实验，结果表明：高密度行人场景中，ERCBF-TMPC++算法在直线道路和凸型道路上的安全避障率分别达95.5%、82.6%，侵入严重度指数在直线道路上较TMPC、TMPC++算法分别降低了67 %和44%，在凸型道路上较TMPC++算法降低了44%，求解器成功率稳定在99 %以上，平均控制周期约为22 ms。该研究验证了弹性恢复与互惠责任机制在增强避障安全性的有效性，可为巡检机器人避障及自动驾驶等动态场景下的运动规划提供参考。

Abstract

To address the issues of unstable obstacle avoidance and susceptibility to deadlocks for inspection robots in dynamic pedestrian environments

a topological model predictive control (TMPC) algorithm based on control barrier functions (CBFs)

integrating elastic recovery and reciprocal responsibility (ERCBF-TMPC)

is proposed.This algorithm employs CBFs as safety constraints to ensure collision-free traj ectories within the prediction horizon.An elastic recovery mechanism is introduced to relax the rigid safety boundary into an elastic buffer layer

enabling the robot to autonomously recover feasible solutions when the safety boundary is violated.A reciprocal responsibility allocation model is established to achieve continuous adj ustment from mutual avoidance to full robot responsibility

thereby suppressing oscillations and deadlocks. Building on this

the complete framework ERCBF-TMPC++introduces an unguided baseline planner into the solver set to enhance robustness.Simulations were conducted in three types of environments：straight roads

convex roads

and open areas.The results demonstrate that in high-density pedestrian scenarios

the safe obstacle avoidance rates of ERCBF-TMPC++on straight and convex roads reached 95.5 % and 82.6%

respectively.The intrusion severity index on straight road was reduced by 67% and 44%

respectively

compared to TMPC and TMPC++algorithms

and reduced by 44% compared to TMPC++ algorithm on convex road.Furthermore

the solver success rate remained stably above 99 %

with an average control cycle of approximately 22 ms.This study verifies the effectiveness of the elastic recovery and reciprocal responsibility mechanisms in enhancing obstacle avoidance safety

providing a valuable reference for motion planning in dynamic scenarios such as inspection robot navigation and autonomous driving.

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