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为提高移动机器人的位置估计精度和跟踪效果,提出一种基于道路约束条件下的移动机器人鲁棒约束H∞滤波(CHF)跟踪算法。首先,将机器人移动的道路网络作为跟踪的约束条件,并利用当前统计模型对机器人的运动进行建模。其次,将道路约束条件作为机器人跟踪的非线性状态约束,利用最小协方差估计推导了鲁棒CHF递推方程。通过拉格朗日乘子法对非线性约束优化估计问题进行求解,并利用约束信息对CHF算法的状态更新过程进行了改进。最后,通过对CHF算法和无约束的H∞滤波算法的跟踪性能进行了对比分析和验证。仿真结果表明,该算法可以实现机器人的跟踪,且跟踪精度优于HF算法。
To improve the position estimation accuracy and tracking performance of mobile robots, a Robust Constrained H∞ Filter (CHF) tracking algorithm for mobile robots based on road constraints is proposed. First, the robot moving road network is used as the tracking constraint, and the current statistical model is used to model the robot’s motion. Secondly, using the road constraint as the nonlinear state constraint of the robot tracking, the robust CHF recursion equation is derived using the minimum covariance estimation. The Lagrange multiplier method is used to solve the nonlinear constrained optimization problem and the constraint update is used to update the CHF algorithm. Finally, the tracking performance of the CHF algorithm and the unconstrained H∞ filtering algorithm are compared and verified. The simulation results show that the algorithm can achieve the robot tracking, and the tracking accuracy is better than the HF algorithm.