{"title":"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","authors":"Zeinab Shayan , Mohammadreza Izadi , Vincenzo Scognamiglio , Simone D’Angelo , Shashank Singoji , Vincenzo Lippiello , Reza Faieghi","doi":"10.1016/j.conengprac.2025.106489","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106489"},"PeriodicalIF":4.6000,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exponential control barrier function and model predictive control for jerk-level reactive motion planning of quadrotors\",\"authors\":\"Zeinab Shayan , Mohammadreza Izadi , Vincenzo Scognamiglio , Simone D’Angelo , Shashank Singoji , Vincenzo Lippiello , Reza Faieghi\",\"doi\":\"10.1016/j.conengprac.2025.106489\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":50615,\"journal\":{\"name\":\"Control Engineering Practice\",\"volume\":\"164 \",\"pages\":\"Article 106489\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2025-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Control Engineering Practice\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0967066125002515\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Control Engineering Practice","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0967066125002515","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
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.
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.