Pub Date : 2025-10-10DOI: 10.1109/TIV.2025.3604262
{"title":"Share Your Preprint Research with the World!","authors":"","doi":"10.1109/TIV.2025.3604262","DOIUrl":"https://doi.org/10.1109/TIV.2025.3604262","url":null,"abstract":"","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"10 6","pages":"3886-3886"},"PeriodicalIF":14.3,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11199365","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-09DOI: 10.1109/TIV.2025.3620013
Nicolas Lucotte;Adolfo Perrusquía;Antonios Tsourdos;Weisi Guo;Hyo-Sang Shin
The accurate interception of adversarial uncrewed aerial vehicles (UAVs) is paramount for the protection of people and national facilities. Urban cities pose several challenges for target interception algorithms due to the presence of buildings and flying constraints that limit the manoeuvrability of UAVs for target interception. Deep Reinforcement Learning (DRL) algorithms have been deployed to solve the task effectively. However, the design of its inner elements such as the reward function and action distribution limits its generalisation to different environments. To solve this issue, this paper proposes a novel twin-delayed deep deterministic policy gradient (TD3) based expert learning algorithm that combines previous expert experiences with on-line learning to regularise and improve the policy learning effectively. This is done by following an action distribution algorithm that allows a learner agent to mix its own actions with expert ones for learning improvement and fast convergence. Extensive simulation studies are carried out under diverse urban cities configurations to show the robustness and high-accuracy of the proposed approach compared with traditional DRL baseline algorithms.
{"title":"Tactical Planning Interception Enhancement Using Expert Learning: Twin Delayed Deep Deterministic Policy Gradient","authors":"Nicolas Lucotte;Adolfo Perrusquía;Antonios Tsourdos;Weisi Guo;Hyo-Sang Shin","doi":"10.1109/TIV.2025.3620013","DOIUrl":"https://doi.org/10.1109/TIV.2025.3620013","url":null,"abstract":"The accurate interception of adversarial uncrewed aerial vehicles (UAVs) is paramount for the protection of people and national facilities. Urban cities pose several challenges for target interception algorithms due to the presence of buildings and flying constraints that limit the manoeuvrability of UAVs for target interception. Deep Reinforcement Learning (DRL) algorithms have been deployed to solve the task effectively. However, the design of its inner elements such as the reward function and action distribution limits its generalisation to different environments. To solve this issue, this paper proposes a novel twin-delayed deep deterministic policy gradient (TD3) based expert learning algorithm that combines previous expert experiences with on-line learning to regularise and improve the policy learning effectively. This is done by following an action distribution algorithm that allows a learner agent to mix its own actions with expert ones for learning improvement and fast convergence. Extensive simulation studies are carried out under diverse urban cities configurations to show the robustness and high-accuracy of the proposed approach compared with traditional DRL baseline algorithms.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"11 1","pages":"174-184"},"PeriodicalIF":14.3,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145802331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-09DOI: 10.1109/TIV.2025.3619812
Omid Mofid;Farhad Bayat;Saleh Mobayen
The trajectory tracking of the quadrotor in the presence of an external disturbance and an actuator failure is the main topic of the upcoming paper. As a result, the novel adaptive fault-tolerant control technique is developed in two stages, which are described below. The nominal model of the quadrotor is taken into consideration first. Thus, LaSalle's Lemma is used to design a powerful controller that certifies the global asymptotic trajectory tracking. Second, the nominal model of the quadrotor is extended to include the actuator defect and external perturbation. Global asymptotic stability of the following error is then certified by developing an adaptive fault-tolerant control method for the disturbed quadrotor. In order to strengthen the system's implementation against the unknown upper-bounded disturbance, the adaptive control process is used in the final section to estimate the external disruption's unknown upper bound. The suggested approach's primary benefits are the tracking error's global asymptotic reachability, the introduction of a fault-tolerant control strategy, and the suggestion of a chattering-free controller. Lastly, the viability and efficiency of the proposed controller for the trajectory tracking of the disrupted quadrotor with the actuator defect are verified using simulation and hardware-in-loop implementations.
{"title":"Adaptive Fault-Tolerant Tracker Design for Perturbed Quadrotors Subject to Actuator Faults","authors":"Omid Mofid;Farhad Bayat;Saleh Mobayen","doi":"10.1109/TIV.2025.3619812","DOIUrl":"https://doi.org/10.1109/TIV.2025.3619812","url":null,"abstract":"The trajectory tracking of the quadrotor in the presence of an external disturbance and an actuator failure is the main topic of the upcoming paper. As a result, the novel adaptive fault-tolerant control technique is developed in two stages, which are described below. The nominal model of the quadrotor is taken into consideration first. Thus, LaSalle's Lemma is used to design a powerful controller that certifies the global asymptotic trajectory tracking. Second, the nominal model of the quadrotor is extended to include the actuator defect and external perturbation. Global asymptotic stability of the following error is then certified by developing an adaptive fault-tolerant control method for the disturbed quadrotor. In order to strengthen the system's implementation against the unknown upper-bounded disturbance, the adaptive control process is used in the final section to estimate the external disruption's unknown upper bound. The suggested approach's primary benefits are the tracking error's global asymptotic reachability, the introduction of a fault-tolerant control strategy, and the suggestion of a chattering-free controller. Lastly, the viability and efficiency of the proposed controller for the trajectory tracking of the disrupted quadrotor with the actuator defect are verified using simulation and hardware-in-loop implementations.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"11 1","pages":"163-173"},"PeriodicalIF":14.3,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145802376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01DOI: 10.1109/TIV.2025.3616980
Mohsen Eskandari;Andrey V. Savkin;Mohammad Deghat
Navigation of a team of autonomous underwater vehicles (AUVs) coordinated by an unmanned surface vehicle (USV) is efficient and reliable for deep ocean exploration. AUVs depart from and return to the USV after collaborative navigation, data collection, and ocean exploration missions. Efficient path planning and accurate localization are essential, the latter of which is critical due to the lack of global localization signals and poor radio frequency (RF) communication in deep waters. Inertial navigation and acoustic communication are common solutions for localization. However, the former is subject to odometry drifts, and the latter is limited to short distances. This paper proposes a systematic approach for localization-aware energy-efficient collision-free path planning for a USV-AUVs team. Path planning is formulated as finite receding horizon model predictive control (MPC) optimization. A dynamic-aware linear kinodynamic motion equation is developed. The mathematical formulation for the MPC optimization is effectively developed where localization is integrated as consensus graph optimization among AUV nodes. Edges in the optimized AUV-to-USV (A2U) and AUV-to-AUV (A2A) graphs are constrained to the sonar range of acoustic modems. The time complexity of the consensus MPC optimization problem is analyzed, revealing a nonconvex NP-hard problem, which is solved using sequential convex programming. Numerical simulation results are provided to evaluate the proposed method.
{"title":"Integrated Path Planning and Localization for an Ocean Exploring Team of Autonomous Underwater Vehicles With Consensus Graph Model Predictive Control","authors":"Mohsen Eskandari;Andrey V. Savkin;Mohammad Deghat","doi":"10.1109/TIV.2025.3616980","DOIUrl":"https://doi.org/10.1109/TIV.2025.3616980","url":null,"abstract":"Navigation of a team of autonomous underwater vehicles (AUVs) coordinated by an unmanned surface vehicle (USV) is efficient and reliable for deep ocean exploration. AUVs depart from and return to the USV after collaborative navigation, data collection, and ocean exploration missions. Efficient path planning and accurate localization are essential, the latter of which is critical due to the lack of global localization signals and poor radio frequency (RF) communication in deep waters. Inertial navigation and acoustic communication are common solutions for localization. However, the former is subject to odometry drifts, and the latter is limited to short distances. This paper proposes a systematic approach for localization-aware energy-efficient collision-free path planning for a USV-AUVs team. Path planning is formulated as finite receding horizon model predictive control (MPC) optimization. A dynamic-aware linear kinodynamic motion equation is developed. The mathematical formulation for the MPC optimization is effectively developed where localization is integrated as consensus graph optimization among AUV nodes. Edges in the optimized AUV-to-USV (A2U) and AUV-to-AUV (A2A) graphs are constrained to the sonar range of acoustic modems. The time complexity of the consensus MPC optimization problem is analyzed, revealing a nonconvex NP-hard problem, which is solved using sequential convex programming. Numerical simulation results are provided to evaluate the proposed method.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"11 1","pages":"150-162"},"PeriodicalIF":14.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145802371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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