{"title":"COVER: cross-vehicle transition framework for quadrotor control in air-ground cooperation","authors":"Qiuyu Ren, Miao Xu, Mengke Zhang, Nanhe Chen, Mingwei Lai, Chao Xu, Fei Gao, Yanjun Cao","doi":"10.1007/s10514-025-10209-4","DOIUrl":null,"url":null,"abstract":"<div><p>UAV transitions across UGVs enable diverse air-ground cooperation (AGC) applications, such as cross-vehicle landing, delivery, and rescue. However, achieving precise and efficient transitions across multiple moving UGVs without prior knowledge of their trajectories remains highly challenging. This paper proposes COVER, a cross-vehicle transition framework for quadrotor control in AGC scenarios. In COVER, the UAV is directly controlled in UGVs’ body frames as non-inertial frames, thus eliminating all dependencies in the world frame. Each transition process is divided into three stages: the initial stage, transition stage, and final stage, with pre-set stage transition points and stage-varying system states. Then, an optimal reference trajectory is generated at each stage by solving a non-linear programming (NLP) problem. The effect of the target UGV’s rotation on the initial relative velocity is eliminated to obtain a dynamically feasible and smooth transition reference trajectory. Finally, we design a stage-adaptive model predictive control (SAMPC) method, proposing a novel MPC position reference mode to avoid indirect routes at the transition stage. The SAMPC method effectively mitigates the flight instability caused by reference frame transition and eliminates the effect of reference frame rotation at the transition stage. And it can flexibly adapt to accurate requirements at the final stage by switching position reference mode and adjusting cost weights. Simulation benchmarks and extensive real-world experiments validate that our approach can achieve smooth, short-distance, and accurate cross-vehicle operations.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":"49 3","pages":""},"PeriodicalIF":4.3000,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Autonomous Robots","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10514-025-10209-4","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
UAV transitions across UGVs enable diverse air-ground cooperation (AGC) applications, such as cross-vehicle landing, delivery, and rescue. However, achieving precise and efficient transitions across multiple moving UGVs without prior knowledge of their trajectories remains highly challenging. This paper proposes COVER, a cross-vehicle transition framework for quadrotor control in AGC scenarios. In COVER, the UAV is directly controlled in UGVs’ body frames as non-inertial frames, thus eliminating all dependencies in the world frame. Each transition process is divided into three stages: the initial stage, transition stage, and final stage, with pre-set stage transition points and stage-varying system states. Then, an optimal reference trajectory is generated at each stage by solving a non-linear programming (NLP) problem. The effect of the target UGV’s rotation on the initial relative velocity is eliminated to obtain a dynamically feasible and smooth transition reference trajectory. Finally, we design a stage-adaptive model predictive control (SAMPC) method, proposing a novel MPC position reference mode to avoid indirect routes at the transition stage. The SAMPC method effectively mitigates the flight instability caused by reference frame transition and eliminates the effect of reference frame rotation at the transition stage. And it can flexibly adapt to accurate requirements at the final stage by switching position reference mode and adjusting cost weights. Simulation benchmarks and extensive real-world experiments validate that our approach can achieve smooth, short-distance, and accurate cross-vehicle operations.
期刊介绍:
Autonomous Robots reports on the theory and applications of robotic systems capable of some degree of self-sufficiency. It features papers that include performance data on actual robots in the real world. Coverage includes: control of autonomous robots · real-time vision · autonomous wheeled and tracked vehicles · legged vehicles · computational architectures for autonomous systems · distributed architectures for learning, control and adaptation · studies of autonomous robot systems · sensor fusion · theory of autonomous systems · terrain mapping and recognition · self-calibration and self-repair for robots · self-reproducing intelligent structures · genetic algorithms as models for robot development.
The focus is on the ability to move and be self-sufficient, not on whether the system is an imitation of biology. Of course, biological models for robotic systems are of major interest to the journal since living systems are prototypes for autonomous behavior.