指数控制、障碍函数和模型预测控制四旋翼机瞬变级反应运动规划

IF 4.6 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Zeinab Shayan , Mohammadreza Izadi , Vincenzo Scognamiglio , Simone D’Angelo , Shashank Singoji , Vincenzo Lippiello , Reza Faieghi
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引用次数: 0

摘要

四旋翼飞行器需要高效的反应运动规划算法,以确保在动态环境中安全自主运行。反应性运动规划的一个常见策略是模型预测控制(MPC);然而,传统的基于mpc的方法往往不能保证在遇到动态障碍物时避免碰撞。为了解决这一限制,我们开发了一个增强的框架,将MPC与控制障碍函数(cbf)相结合,以提高安全性,并将卡尔曼滤波器(KF)用于预测障碍物行为,提高对动态障碍物的响应性。我们利用四旋翼的高阶闭环模型以及指数cbf,实现了在突跳水平上的轨迹控制,而不像现有的MPC-CBF方法依赖于加速度水平的规划。在多种场景下进行的大量硬件实验表明,该方法通过增加车辆与障碍物的最小距离,并在复杂情况下成功导航,例如避开快速摇摆的障碍物,从而显著提高了安全性,而传统的仅使用mpc的方法在这些情况下都无法实现。基于硬件的灵敏度分析进一步揭示了该算法对参数值变化的整体鲁棒性,为参数调整提供了见解,并强调了在动态环境中准确预测障碍物的关键作用。我们的研究结果表明,MPC-CBF-KF框架是一个有前途的,鲁棒的,计算上可行的四旋翼运动规划在动态和静态环境中的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exponential control barrier function and model predictive control for jerk-level reactive motion planning of quadrotors
Quadrotors require efficient reactive motion planning algorithms to ensure safe autonomous operations in dynamic environments. One common strategy for reactive motion planning is model predictive control (MPC); however, conventional MPC-based methods often fall short of guaranteeing collision avoidance when encountering dynamic obstacles. To address this limitation, we develop an enhanced framework that combines MPC with control barrier functions (CBFs) for improved safety and a Kalman Filter (KF) for predicting obstacle behavior, increasing responsiveness to dynamic obstacles. We utilize a high-order closed-loop model of the quadrotor along with exponential CBFs, enabling trajectory control at the jerk level, unlike existing MPC-CBF methods that rely on acceleration-level planning. Extensive hardware experiments across multiple scenarios demonstrate that this approach significantly enhances safety by increasing the minimum vehicle-obstacle distance and enabling successful navigation through complex situations, such as avoiding fast-swinging obstacles, where traditional MPC-only methods fail. Hardware-based sensitivity analysis further reveals the algorithm’s overall robustness to variations in parameter values, provides insight into parameter tuning, and highlights the critical role of accurate obstacle predictions in dynamic environments. Our findings indicate that the MPC-CBF-KF framework is a promising, robust, and computationally feasible solution for quadrotor motion planning in both dynamic and static environments.
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来源期刊
Control Engineering Practice
Control Engineering Practice 工程技术-工程:电子与电气
CiteScore
9.20
自引率
12.20%
发文量
183
审稿时长
44 days
期刊介绍: Control Engineering Practice strives to meet the needs of industrial practitioners and industrially related academics and researchers. It publishes papers which illustrate the direct application of control theory and its supporting tools in all possible areas of automation. As a result, the journal only contains papers which can be considered to have made significant contributions to the application of advanced control techniques. It is normally expected that practical results should be included, but where simulation only studies are available, it is necessary to demonstrate that the simulation model is representative of a genuine application. Strictly theoretical papers will find a more appropriate home in Control Engineering Practice''s sister publication, Automatica. It is also expected that papers are innovative with respect to the state of the art and are sufficiently detailed for a reader to be able to duplicate the main results of the paper (supplementary material, including datasets, tables, code and any relevant interactive material can be made available and downloaded from the website). The benefits of the presented methods must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods. Moreover, a thorough analysis of failures that may happen in the design process and implementation can also be part of the paper. The scope of Control Engineering Practice matches the activities of IFAC. Papers demonstrating the contribution of automation and control in improving the performance, quality, productivity, sustainability, resource and energy efficiency, and the manageability of systems and processes for the benefit of mankind and are relevant to industrial practitioners are most welcome.
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