At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown ...At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments,this paper proposes an Attention-Enhanced Dueling Deep Q-Network(ADDueling DQN),which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework.A multi-objective reward function,centered on energy efficiency,is designed to comprehensively consider path length,terrain slope,motion smoothness,and obstacle avoidance,enabling optimal low-energy trajectory generation in 3D space from the source.The incorporation of a multihead attention mechanism allows the model to dynamically focus on energy-critical state features—such as slope gradients and obstacle density—thereby significantly improving its ability to recognize and avoid energy-intensive paths.Additionally,the prioritized experience replay mechanism accelerates learning from key decision-making experiences,suppressing inefficient exploration and guiding the policy toward low-energy solutions more rapidly.The effectiveness of the proposed path planning algorithm is validated through simulation experiments conducted in multiple off-road scenarios.Results demonstrate that AD-Dueling DQN consistently achieves the lowest average energy consumption across all tested environments.Moreover,the proposed method exhibits faster convergence and greater training stability compared to baseline algorithms,highlighting its global optimization capability under energy-aware objectives in complex terrains.This study offers an efficient and scalable intelligent control strategy for the development of energy-conscious autonomous navigation systems.展开更多
The development of chassis active safety control technology has improved vehicle stability under extreme conditions.However,its cross-system and multi-functional characteristics make the controller difficult to achiev...The development of chassis active safety control technology has improved vehicle stability under extreme conditions.However,its cross-system and multi-functional characteristics make the controller difficult to achieve cooperative goals.In addition,the chassis system,which has high complexity,numerous subsystems,and strong coupling,will also lead to low computing efficiency and poor control effect of the controller.Therefore,this paper proposes a scenario-driven hybrid distributed model predictive control algorithm with variable control topology.This algorithm divides multiple stability regions based on the vehicle’s β−γ phase plane,forming a mapping relationship between the control structure and the vehicle’s state.A control input fusion mechanism within the transition domain is designed to mitigate the problems of system state oscillation and control input jitter caused by switching control structures.Then,a distributed state-space equation with state coupling and input coupling characteristics is constructed,and a weighted local agent cost function in quadratic programming is derived.Through cost coupling,local agents can coordinate global performance goals.Finally,through Simulink/CarSim joint simulation and hardware-in-the-loop(HIL)test,the proposed algorithm is validated to improve vehicle stability while ensuring trajectory tracking accuracy and has good applicability for multi-objective coordinated control.This paper combines the advantages of distributed MPC and decentralized MPC,achieving a balance between approximating the global optimal results and the solution’s efficiency.展开更多
Flapping Wing Aerial Vehicles(FWAVs)hold immense potential for applications such as search-and-rescue missions in complex terrains,environmental monitoring in hazardous areas,and exploration in confined spaces.However...Flapping Wing Aerial Vehicles(FWAVs)hold immense potential for applications such as search-and-rescue missions in complex terrains,environmental monitoring in hazardous areas,and exploration in confined spaces.However,their adoption is hindered by the challenges of autonomous navigation in unknown environments,exacerbated by their limited onboard computational resources and demanding flight dynamics.This work addresses these challenges by presenting a lightweight,vision-based autonomous navigation system weighing 26.0 g,enabling FWAVs to achieve obstacle-avoidance flight at a speed of 9.0 m/s.Central to this system is a novel end-toend Bi-level Cooperative Policy(BCP)that significantly improves flight efficiency and safety.BCP employs lightweight neural networks for real-time performance and leverages Hierarchical Reinforcement Learning(HRL)for robust and efficient training.Quantitative evaluations show that BCP achieves up to 6.5%shorter path lengths,11.2%faster task completion time,and improved explainability compared to state-of-the-art reinforcement learning algorithms.Additionally,BCP demonstrates 35.7%more efficient and stable training,reducing computational overhead while maintaining high performance.The system design incorporates optimized lightweight components,including a 4.0 g customized stereo camera,a 6.0 g 3D-printed camera mount,and a 16.0 g onboard computer,all tailored to FWAV applications.Real-flight experiments validate the sim-toreal transferability of the proposed navigation system,demonstrating its readiness for real-world deployment in challenging scenarios.This research advances the practicality of FWAVs,paving the way for their broader adoption in critical missions where compact,agile aerial robots are indispensable.展开更多
Sail is the core part of autonomous sailboat and wing sail is a new type of sail. Wing sail generates not only propulsion but also lateral force and heeling moment. The latter two will affect the navigation status and...Sail is the core part of autonomous sailboat and wing sail is a new type of sail. Wing sail generates not only propulsion but also lateral force and heeling moment. The latter two will affect the navigation status and bring resistance. Double sail can effectively reduce the center of wind pressure and heeling moment. In order to study the effect of distance between two sails, airfoil and attack angle on the total lift coefficient of double sail propulsion system, pressure coefficient distribution and lift coefficient calculation model have been established based on vortex panel method. By using the basic finite solution, the fluid dynamic forces on the two-dimensional sails are computed.The results show that, the distance in the range of 0 to 1 time chord length, when using the same airfoil in the fore and aft sail, the total lift coefficient of the double sail increases with the increase of distance, finally reaches a stable value in the range of one to three times chord length. Lift coefficients of thicker airfoils are more sensitive to the change of distance. The thicker the airfoil, the longer distance is required of the total lift coefficient toward stable.When different airfoils are adopted in fore and aft sail, the total lift coefficient increases with the increase of the thickness of aft sail. The smaller the thickness difference is, the more sensitive to the distance change the lift coefficient is. The thinner the fore sail is, the lower the influence will be on the lift coefficient of aft sail.展开更多
In recent years, Chinese Long March(LM) launchers have experienced several launch failures, most of which occurred in their propulsion systems, and this paper studies Autonomous Mission Reconstruction(AMRC) technology...In recent years, Chinese Long March(LM) launchers have experienced several launch failures, most of which occurred in their propulsion systems, and this paper studies Autonomous Mission Reconstruction(AMRC) technology to alleviate losses due to these failures. The status of the techniques related to AMRC, including trajectory and mission planning, guidance methods,and fault tolerant technologies, are reviewed, and their features are compared, which reflect the challenges faced by AMRC technology. After a brief introduction about the failure modes of engines that can occur during flight, and the fundamentals of trajectory planning and joint optimization of the target orbit and flight path, an AMRC algorithm is proposed for geostationary transfer orbit launch missions. The algorithm evaluates the residual performance onboard, and plans new objectives and corresponding flight path by iterative guidance mode or segmented state triggered optimization methods in real-time. Three failure scenarios that have occurred during previous LM missions are simulated to check the robustness of the algorithm: imminent explosion risk of the boosters’ engines, thrust drop during the first stage of flight, and being unable to start the engine during the second stage. The payloads would fall from space according to the current design under these conditions, but they were saved with the AMRC algorithm in the simulations, which allowed the rocket to get into the target orbit as intended or the payloads were deployed in other orbits without crashing. Although spaceflight can be very unforgiving, the AMRC algorithm has the potential to avoid the total loss of a launch mission when faced with these kinds of typical failures.展开更多
The fast growth of mobile autonomous machines from traditional equipment to unmanned autonomous vehicles has fueled the demand for accurate and reliable localization solutions in diverse application domains.Ultra Wide...The fast growth of mobile autonomous machines from traditional equipment to unmanned autonomous vehicles has fueled the demand for accurate and reliable localization solutions in diverse application domains.Ultra Wide Band(UWB)technology has emerged as a promising candidate for addressing this need,offering high precision,immunity to multipath interference,and robust performance in challenging environments.In this comprehensive survey,we systematically explore UWB-based localization for mobile autonomous machines,spanning from fundamental principles to future trends.To the best of our knowledge,this review paper stands as the pioneer in systematically dissecting the algorithms of UWB-based localization for mobile autonomous machines,covering a spectrum from bottom-ranging schemes to advanced sensor fusion,error mitigation,and optimization techniques.By synthesizing existing knowledge,evaluating current methodologies,and highlighting future trends,this review aims to catalyze progress and innovation in the field,unlocking new opportunities for mobile autonomous machine applications across diverse industries and domains.Thus,it serves as a valuable resource for researchers,practitioners,and stakeholders interested in advancing the state-of-the-art UWB-based localization for mobile autonomous machines.展开更多
Positioning and navigation technology is a new trend of research in mobile robot area.Existing researches focus on the indoor industrial problems,while many application fields are in the outdoor environment,which put ...Positioning and navigation technology is a new trend of research in mobile robot area.Existing researches focus on the indoor industrial problems,while many application fields are in the outdoor environment,which put forward higher requirements for sensor selection and navigation scheme.In this paper,a complete hybrid navigation system for a class of mobile robots with load tasks and docking tasks is presented.The work can realize large-range autonomous positioning and path planning for mobile robots in unstructured scenarios.The autonomous positioning is achieved by adopting suitable guidance methods to meet different application requirements and accuracy requirements in conditions of different distances.Based on the Bezier curve,a path planning scheme is proposed and a motion controller is designed to make the mobile robot follow the target path.The Kalman filter is established to process the guidance signals and control outputs of the motion controller.Finally,the autonomous positioning and docking experiment are carried out.The results of the research verify the effectiveness of the hybrid navigation,which can be used in autonomous warehousing logistics and multi-mobile robot system.展开更多
In the process of performing a task,autonomous unmanned systems face the problem of scene changing,which requires the ability of real-time decision-making under dynamically changing scenes.Therefore,taking the unmanne...In the process of performing a task,autonomous unmanned systems face the problem of scene changing,which requires the ability of real-time decision-making under dynamically changing scenes.Therefore,taking the unmanned system coordinative region control operation as an example,this paper combines knowledge representation with probabilistic decisionmaking and proposes a role-based Bayesian decision model for autonomous unmanned systems that integrates scene cognition and individual preferences.Firstly,according to utility value decision theory,the role-based utility value decision model is proposed to realize task coordination according to the preference of the role that individual is assigned.Then,multi-entity Bayesian network is introduced for situation assessment,by which scenes and their uncertainty related to the operation are semantically described,so that the unmanned systems can conduct situation awareness in a set of scenes with uncertainty.Finally,the effectiveness of the proposed method is verified in a virtual task scenario.This research has important reference value for realizing scene cognition,improving cooperative decision-making ability under dynamic scenes,and achieving swarm level autonomy of unmanned systems.展开更多
The vibratory roller is a piece of vital construction machinery in the field of road construction.The unmanned vibratory roller efficiently utilizes the automated driving technology in the vehicle engineering field,wh...The vibratory roller is a piece of vital construction machinery in the field of road construction.The unmanned vibratory roller efficiently utilizes the automated driving technology in the vehicle engineering field,which is innovative for the unmanned road construction.This paper develops and implements the autonomous construction system for the unmanned vibratory roller.Not only does the roller have the function of remote-controlled driving,but it also has the capability of autonomous road construction.The overall system design uses the Programmable Logic Controller(PLC)as the kernel controller.It establishes the communication network through multiple Input/Output(I/O)modules,Recommended Standard 232(RS232)serial port,Controller Area Network(CAN)bus,and wireless networks to control the roller vehicle completely.The locating information is obtained through the Global Navigation Satellite System(GNSS)satellite navigation equipment group to support the process of autonomous construction.According to the experimental results,the autonomous construction system can finally enable the roller to perform driving operations and construction independently,which was a significant step forward in engineering application.展开更多
Xi Jinping,general secretary of the Communist Party of China(CPC)Central Committee,stressed that we should adhere to the“two integrations”(namely,integrating the basic tenets of Marxism with China’s specific realit...Xi Jinping,general secretary of the Communist Party of China(CPC)Central Committee,stressed that we should adhere to the“two integrations”(namely,integrating the basic tenets of Marxism with China’s specific realities and fine traditional culture),root ourselves in Chinese soil,carry forward the Chinese cultural heritage,and strengthen the academic foundation.We should accelerate the building of an independent knowledge system for Chinese philosophy and social sciences,and formulate original concepts and develop systems of academic discipline,research and discourse,drawing on China’s rich experience of advancing human rights.In the face of changes of a magnitude not seen in a century,in the historic process of advancing the great rejuvenation of the Chinese nation on all fronts through Chinese modernization,we should and must strengthen our theoretical self-consciousness and confidence in the path of Chinese modernization.We need to enhance human rights research,develop the human rights theoretical system and paradigm that are based on Chinese realities and express Chinese voice,and an independent Chinese knowledge system for human rights.展开更多
The latest 6G improvements secured autonomous driving's realism in Intelligent Autonomous Transport Systems(IATS).Despite the IATS's benefits,security remains a significant challenge.Blockchain technology has ...The latest 6G improvements secured autonomous driving's realism in Intelligent Autonomous Transport Systems(IATS).Despite the IATS's benefits,security remains a significant challenge.Blockchain technology has grown in popularity as a means of implementing safe,dependable,and decentralised independent IATS systems,allowing for more utilisation of legacy IATS infrastructures and resources,which is especially advantageous for crowdsourcing technologies.Blockchain technology can be used to address security concerns in the IATS and to aid in logistics development.In light of the inadequacy of reliance and inattention to rights created by centralised and conventional logistics systems,this paper discusses the creation of a blockchain-based IATS powered by deep learning for secure cargo and vehicle matching(BDL-IATS).The BDL-IATS approach utilises Ethereum as the primary blockchain for storing private data such as order and shipment details.Additionally,the deep belief network(DBN)model is used to select suitable vehicles and goods for transportation.Additionally,the chaotic krill herd technique is used to tune the DBN model’s hyper-parameters.The performance of the BDL-IATS technique is validated,and the findings are inspected under a variety of conditions.The simulationfindings indicated that the BDL-IATS strategy outperformed recent state-of-the-art approaches.展开更多
In order to realize the explorer autonomy, the software architecture of autonomous mission management system (AMMS) is given for the deep space explorer, and the autonomous mission planning system, the kernel part of ...In order to realize the explorer autonomy, the software architecture of autonomous mission management system (AMMS) is given for the deep space explorer, and the autonomous mission planning system, the kernel part of this architecture, is designed in detail. In order to describe the parallel activity, the state timeline is introduced to build the formal model of the planning system and based on this model, the temporal constraint satisfaction planning algorithm is proposed to produce the explorer’s activity sequence. With some key subsystems of the deep space explorer as examples, the autonomous mission planning simulation system is designed. The results show that this system can calculate the executable activity sequence with the given mission goals and initial state of the explorer.展开更多
Recently,autonomous systems become a hot research topic among industrialists and academicians due to their applicability in different domains such as healthcare,agriculture,industrial automation,etc.Among the interest...Recently,autonomous systems become a hot research topic among industrialists and academicians due to their applicability in different domains such as healthcare,agriculture,industrial automation,etc.Among the interesting applications of autonomous systems,their applicability in agricultural sector becomes significant.Autonomous unmanned aerial vehicles(UAVs)can be used for suitable site-specific weed management(SSWM)to improve crop productivity.In spite of substantial advancements in UAV based data collection systems,automated weed detection still remains a tedious task owing to the high resemblance of weeds to the crops.The recently developed deep learning(DL)models have exhibited effective performance in several data classification problems.In this aspect,this paper focuses on the design of autonomous UAVs with decision support system for weed management(AUAV-DSSWM)technique.The proposed AUAV-DSSWM technique intends to identify the weeds by the use of UAV images acquired from the target area.Besides,the AUAV-DSSWM technique primarily performs image acquisition and image pre-processing stages.Moreover,the Adam optimizer with You Only Look Once Object Detector-(YOLOv3)model is applied for the detection of weeds.For the effective classification of weeds and crops,the poor and rich optimization(PRO)algorithm with softmax layer is applied.The design of Adam optimizer and PRO algorithm for the parameter tuning process results in enhanced weed detection performance.A wide range of simulations take place on UAV images and the experimental results exhibit the promising performance of the AUAV-DSSWM technique over the other recent techniques with the accy of 99.23%.展开更多
For decades,antigen presentation on major histocompatibility complex class I for T cell-mediated immunity has been considered the primary function of proteasome-derived peptides1,2.However,whether the products of prot...For decades,antigen presentation on major histocompatibility complex class I for T cell-mediated immunity has been considered the primary function of proteasome-derived peptides1,2.However,whether the products of proteasomal degradation play additional parts in mounting immune responses remains unknown.Antimicrobial peptides serve as a first line of defence against invading pathogens before the adaptive immune system responds.Although the protective function of antimicrobial peptides across numerous tissues is well established,the cellular mechanisms underlying their generation are not fully understood.Here we uncover a role for proteasomes in the constitutive and bacterial-induced generation of defence peptides that impede bacterial growth both in vitro and in vivo by disrupting bacterial membranes.In silico prediction of proteome-wide proteasomal cleavage identified hundreds of thousands of potential proteasome-derived defence peptides with cationic properties that may be generated en route to degradation to act as a first line of defence.展开更多
This paper introduces autonomous driving image perception technology,including deep learning models(such as CNN and RNN)and their applications,analyzing the limitations of traditional algorithms.It elaborates on the s...This paper introduces autonomous driving image perception technology,including deep learning models(such as CNN and RNN)and their applications,analyzing the limitations of traditional algorithms.It elaborates on the shortcomings of Faster R-CNN and YOLO series models,proposes various improvement techniques such as data fusion,attention mechanisms,and model compression,and introduces relevant datasets,evaluation metrics,and testing frameworks to demonstrate the advantages of the improved models.展开更多
Autonomous driving technology is constantly developing to a higher level of complex scenes,and there is a growing demand for the utilization of end-to-end data-driven control.However,the end-to-end path tracking proce...Autonomous driving technology is constantly developing to a higher level of complex scenes,and there is a growing demand for the utilization of end-to-end data-driven control.However,the end-to-end path tracking process often encounters challenges in learning efficiency and generalization.To address this issue,this paper designs a deep deterministic policy gradient(DDPG)-based reinforcement learning strategy that integrates imitation learning and feedforward exploration in the path following process.In imitation learning,the path tracking control data generated by the model predictive control(MPC)method is used to train an end-to-end steering control model of a deep neural network.Another feedforward exploration behavior is predicted by road curvature and vehicle speed,and adds it and imitation learning to the DDPG reinforcement learning to obtain decision-making experience and action prediction behavior of the path tracking process.In the reinforcement learning process,imitation learning is used to update the pre-training parameters of the actor network,and a feedforward steering technique with random noise is adopted for strategy exploration.In the reward function,a hierarchical progressive reward form and a constrained objective reward function referring to MPC are designed,and the actor-critic network architecture is determined.Finally,the path tracking performance of the designed method is verified by comparing various training results,simulations,and HIL tests.The results show that the designed method can effectively utilize pre-training and feedforward prior experience to obtain optimal path tracking performance of an autonomous vehicle,and has better generalization ability than other methods.This study provides an efficient control scheme for improving the end-to-end control performance of autonomous vehicles.展开更多
Lethal autonomous weapon systems(LAWS)have become a prominent issue in global security governance.However,significant divergences remain among the world's major countries regarding their regulation:some support re...Lethal autonomous weapon systems(LAWS)have become a prominent issue in global security governance.However,significant divergences remain among the world's major countries regarding their regulation:some support restrictions,while others oppose any binding rules,and there are also countries adopting a neutral stance,though their choices are subject to external pressure.In the foreseeable future,the deployment of LAWS will increase further,raising the risk of arms races,with international support for regulating these weapons gaining strength.Yet,establishing legally binding international rules within the United Nations(UN)framework remains a distant prospect.Given this backdrop,China should continue to actively participate in international norms setting concerning LAWS under UN auspices and deepen coordination and cooperation with other countries towards more applicable norms and global governance frameworks.展开更多
Self-Explaining Autonomous Systems(SEAS)have emerged as a strategic frontier within Artificial Intelligence(AI),responding to growing demands for transparency and interpretability in autonomous decisionmaking.This stu...Self-Explaining Autonomous Systems(SEAS)have emerged as a strategic frontier within Artificial Intelligence(AI),responding to growing demands for transparency and interpretability in autonomous decisionmaking.This study presents a comprehensive bibliometric analysis of SEAS research published between 2020 and February 2025,drawing upon 1380 documents indexed in Scopus.The analysis applies co-citation mapping,keyword co-occurrence,and author collaboration networks using VOSviewer,MASHA,and Python to examine scientific production,intellectual structure,and global collaboration patterns.The results indicate a sustained annual growth rate of 41.38%,with an h-index of 57 and an average of 21.97 citations per document.A normalized citation rate was computed to address temporal bias,enabling balanced evaluation across publication cohorts.Thematic analysis reveals four consolidated research fronts:interpretability in machine learning,explainability in deep neural networks,transparency in generative models,and optimization strategies in autonomous control.Author co-citation analysis identifies four distinct research communities,and keyword evolution shows growing interdisciplinary links with medicine,cybersecurity,and industrial automation.The United States leads in scientific output and citation impact at the geographical level,while countries like India and China show high productivity with varied influence.However,international collaboration remains limited at 7.39%,reflecting a fragmented research landscape.As discussed in this study,SEAS research is expanding rapidly yet remains epistemologically dispersed,with uneven integration of ethical and human-centered perspectives.This work offers a structured and data-driven perspective on SEAS development,highlights key contributors and thematic trends,and outlines critical directions for advancing responsible and transparent autonomous systems.展开更多
Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving.This study aims to enhan...Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving.This study aims to enhance the learning efficiency ofmulti-sensor feature fusion in autonomous driving tasks,thereby improving the safety and responsiveness of the system.To achieve this goal,we propose an innovative multi-sensor feature fusion model that integrates three distinct modalities:visual,radar,and lidar data.The model optimizes the feature fusion process through the introduction of two novel mechanisms:Sparse Channel Pooling(SCP)and Residual Triplet-Attention(RTA).Firstly,the SCP mechanism enables the model to adaptively filter out salient feature channels while eliminating the interference of redundant features.This enhances the model’s emphasis on critical features essential for decisionmaking and strengthens its robustness to environmental variability.Secondly,the RTA mechanism addresses the issue of feature misalignment across different modalities by effectively aligning key cross-modal features.This alignment reduces the computational overhead associated with redundant features and enhances the overall efficiency of the system.Furthermore,this study incorporates a reinforcement learning module designed to optimize strategies within a continuous action space.By integrating thismodulewith the feature fusion learning process,the entire system is capable of learning efficient driving strategies in an end-to-end manner within the CARLA autonomous driving simulator.Experimental results demonstrate that the proposedmodel significantly enhances the perception and decision-making accuracy of the autonomous driving system in complex traffic scenarios while maintaining real-time responsiveness.This work provides a novel perspective and technical pathway for the application of multi-sensor data fusion in autonomous driving.展开更多
This paper presents a comprehensive overview of distributed Nash equilibrium(NE)seeking algorithms in non-cooperative games for multiagent systems(MASs),with a distinct emphasis on the dynamic control perspective.It s...This paper presents a comprehensive overview of distributed Nash equilibrium(NE)seeking algorithms in non-cooperative games for multiagent systems(MASs),with a distinct emphasis on the dynamic control perspective.It specifically focuses on the research addressing distributed NE seeking problems in which agents are governed by heterogeneous dynamics.The paper begins by introducing fundamental concepts of general non-cooperative games and the NE,along with definitions of specific game structures such as aggregative games and multi-cluster games.It then systematically reviews existing studies on distributed NE seeking for various classes of MASs from the viewpoint of agent dynamics,including first-order,second-order,high-order,linear,and Euler-Lagrange(EL)systems.Furthermore,the paper highlights practical applications of these theoretical advances in cooperative control scenarios involving autonomous systems with complex dynamics,such as autonomous surface vessels,autonomous aerial vehicles,and other autonomous vehicles.Finally,the paper outlines several promising directions for future research.展开更多
文摘At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments,this paper proposes an Attention-Enhanced Dueling Deep Q-Network(ADDueling DQN),which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework.A multi-objective reward function,centered on energy efficiency,is designed to comprehensively consider path length,terrain slope,motion smoothness,and obstacle avoidance,enabling optimal low-energy trajectory generation in 3D space from the source.The incorporation of a multihead attention mechanism allows the model to dynamically focus on energy-critical state features—such as slope gradients and obstacle density—thereby significantly improving its ability to recognize and avoid energy-intensive paths.Additionally,the prioritized experience replay mechanism accelerates learning from key decision-making experiences,suppressing inefficient exploration and guiding the policy toward low-energy solutions more rapidly.The effectiveness of the proposed path planning algorithm is validated through simulation experiments conducted in multiple off-road scenarios.Results demonstrate that AD-Dueling DQN consistently achieves the lowest average energy consumption across all tested environments.Moreover,the proposed method exhibits faster convergence and greater training stability compared to baseline algorithms,highlighting its global optimization capability under energy-aware objectives in complex terrains.This study offers an efficient and scalable intelligent control strategy for the development of energy-conscious autonomous navigation systems.
基金Supported by National Natural Science Foundation of China(Grant Nos.52225212,52272418,U22A20100)National Key Research and Development Program of China(Grant No.2022YFB2503302).
文摘The development of chassis active safety control technology has improved vehicle stability under extreme conditions.However,its cross-system and multi-functional characteristics make the controller difficult to achieve cooperative goals.In addition,the chassis system,which has high complexity,numerous subsystems,and strong coupling,will also lead to low computing efficiency and poor control effect of the controller.Therefore,this paper proposes a scenario-driven hybrid distributed model predictive control algorithm with variable control topology.This algorithm divides multiple stability regions based on the vehicle’s β−γ phase plane,forming a mapping relationship between the control structure and the vehicle’s state.A control input fusion mechanism within the transition domain is designed to mitigate the problems of system state oscillation and control input jitter caused by switching control structures.Then,a distributed state-space equation with state coupling and input coupling characteristics is constructed,and a weighted local agent cost function in quadratic programming is derived.Through cost coupling,local agents can coordinate global performance goals.Finally,through Simulink/CarSim joint simulation and hardware-in-the-loop(HIL)test,the proposed algorithm is validated to improve vehicle stability while ensuring trajectory tracking accuracy and has good applicability for multi-objective coordinated control.This paper combines the advantages of distributed MPC and decentralized MPC,achieving a balance between approximating the global optimal results and the solution’s efficiency.
基金supported by the Fundamental Research Funds for the Central Universities,China。
文摘Flapping Wing Aerial Vehicles(FWAVs)hold immense potential for applications such as search-and-rescue missions in complex terrains,environmental monitoring in hazardous areas,and exploration in confined spaces.However,their adoption is hindered by the challenges of autonomous navigation in unknown environments,exacerbated by their limited onboard computational resources and demanding flight dynamics.This work addresses these challenges by presenting a lightweight,vision-based autonomous navigation system weighing 26.0 g,enabling FWAVs to achieve obstacle-avoidance flight at a speed of 9.0 m/s.Central to this system is a novel end-toend Bi-level Cooperative Policy(BCP)that significantly improves flight efficiency and safety.BCP employs lightweight neural networks for real-time performance and leverages Hierarchical Reinforcement Learning(HRL)for robust and efficient training.Quantitative evaluations show that BCP achieves up to 6.5%shorter path lengths,11.2%faster task completion time,and improved explainability compared to state-of-the-art reinforcement learning algorithms.Additionally,BCP demonstrates 35.7%more efficient and stable training,reducing computational overhead while maintaining high performance.The system design incorporates optimized lightweight components,including a 4.0 g customized stereo camera,a 6.0 g 3D-printed camera mount,and a 16.0 g onboard computer,all tailored to FWAV applications.Real-flight experiments validate the sim-toreal transferability of the proposed navigation system,demonstrating its readiness for real-world deployment in challenging scenarios.This research advances the practicality of FWAVs,paving the way for their broader adoption in critical missions where compact,agile aerial robots are indispensable.
基金financially supported by the JIANG Xinsong Innovation Fund(Grant No.Y8F7010701)
文摘Sail is the core part of autonomous sailboat and wing sail is a new type of sail. Wing sail generates not only propulsion but also lateral force and heeling moment. The latter two will affect the navigation status and bring resistance. Double sail can effectively reduce the center of wind pressure and heeling moment. In order to study the effect of distance between two sails, airfoil and attack angle on the total lift coefficient of double sail propulsion system, pressure coefficient distribution and lift coefficient calculation model have been established based on vortex panel method. By using the basic finite solution, the fluid dynamic forces on the two-dimensional sails are computed.The results show that, the distance in the range of 0 to 1 time chord length, when using the same airfoil in the fore and aft sail, the total lift coefficient of the double sail increases with the increase of distance, finally reaches a stable value in the range of one to three times chord length. Lift coefficients of thicker airfoils are more sensitive to the change of distance. The thicker the airfoil, the longer distance is required of the total lift coefficient toward stable.When different airfoils are adopted in fore and aft sail, the total lift coefficient increases with the increase of the thickness of aft sail. The smaller the thickness difference is, the more sensitive to the distance change the lift coefficient is. The thinner the fore sail is, the lower the influence will be on the lift coefficient of aft sail.
基金co-supported by International Academy of Astronautics (IAA) study group SG 3.32the National Natural Science Foundation of China (No. 61773341)
文摘In recent years, Chinese Long March(LM) launchers have experienced several launch failures, most of which occurred in their propulsion systems, and this paper studies Autonomous Mission Reconstruction(AMRC) technology to alleviate losses due to these failures. The status of the techniques related to AMRC, including trajectory and mission planning, guidance methods,and fault tolerant technologies, are reviewed, and their features are compared, which reflect the challenges faced by AMRC technology. After a brief introduction about the failure modes of engines that can occur during flight, and the fundamentals of trajectory planning and joint optimization of the target orbit and flight path, an AMRC algorithm is proposed for geostationary transfer orbit launch missions. The algorithm evaluates the residual performance onboard, and plans new objectives and corresponding flight path by iterative guidance mode or segmented state triggered optimization methods in real-time. Three failure scenarios that have occurred during previous LM missions are simulated to check the robustness of the algorithm: imminent explosion risk of the boosters’ engines, thrust drop during the first stage of flight, and being unable to start the engine during the second stage. The payloads would fall from space according to the current design under these conditions, but they were saved with the AMRC algorithm in the simulations, which allowed the rocket to get into the target orbit as intended or the payloads were deployed in other orbits without crashing. Although spaceflight can be very unforgiving, the AMRC algorithm has the potential to avoid the total loss of a launch mission when faced with these kinds of typical failures.
文摘The fast growth of mobile autonomous machines from traditional equipment to unmanned autonomous vehicles has fueled the demand for accurate and reliable localization solutions in diverse application domains.Ultra Wide Band(UWB)technology has emerged as a promising candidate for addressing this need,offering high precision,immunity to multipath interference,and robust performance in challenging environments.In this comprehensive survey,we systematically explore UWB-based localization for mobile autonomous machines,spanning from fundamental principles to future trends.To the best of our knowledge,this review paper stands as the pioneer in systematically dissecting the algorithms of UWB-based localization for mobile autonomous machines,covering a spectrum from bottom-ranging schemes to advanced sensor fusion,error mitigation,and optimization techniques.By synthesizing existing knowledge,evaluating current methodologies,and highlighting future trends,this review aims to catalyze progress and innovation in the field,unlocking new opportunities for mobile autonomous machine applications across diverse industries and domains.Thus,it serves as a valuable resource for researchers,practitioners,and stakeholders interested in advancing the state-of-the-art UWB-based localization for mobile autonomous machines.
文摘Positioning and navigation technology is a new trend of research in mobile robot area.Existing researches focus on the indoor industrial problems,while many application fields are in the outdoor environment,which put forward higher requirements for sensor selection and navigation scheme.In this paper,a complete hybrid navigation system for a class of mobile robots with load tasks and docking tasks is presented.The work can realize large-range autonomous positioning and path planning for mobile robots in unstructured scenarios.The autonomous positioning is achieved by adopting suitable guidance methods to meet different application requirements and accuracy requirements in conditions of different distances.Based on the Bezier curve,a path planning scheme is proposed and a motion controller is designed to make the mobile robot follow the target path.The Kalman filter is established to process the guidance signals and control outputs of the motion controller.Finally,the autonomous positioning and docking experiment are carried out.The results of the research verify the effectiveness of the hybrid navigation,which can be used in autonomous warehousing logistics and multi-mobile robot system.
基金the Military Science Postgraduate Project of PLA(JY2020B006).
文摘In the process of performing a task,autonomous unmanned systems face the problem of scene changing,which requires the ability of real-time decision-making under dynamically changing scenes.Therefore,taking the unmanned system coordinative region control operation as an example,this paper combines knowledge representation with probabilistic decisionmaking and proposes a role-based Bayesian decision model for autonomous unmanned systems that integrates scene cognition and individual preferences.Firstly,according to utility value decision theory,the role-based utility value decision model is proposed to realize task coordination according to the preference of the role that individual is assigned.Then,multi-entity Bayesian network is introduced for situation assessment,by which scenes and their uncertainty related to the operation are semantically described,so that the unmanned systems can conduct situation awareness in a set of scenes with uncertainty.Finally,the effectiveness of the proposed method is verified in a virtual task scenario.This research has important reference value for realizing scene cognition,improving cooperative decision-making ability under dynamic scenes,and achieving swarm level autonomy of unmanned systems.
基金This work was supported by the Natural Science Foundation of Jiangsu Province(BK20170681,BK20180701)the National Natural Science Foundation of China(51675281).
文摘The vibratory roller is a piece of vital construction machinery in the field of road construction.The unmanned vibratory roller efficiently utilizes the automated driving technology in the vehicle engineering field,which is innovative for the unmanned road construction.This paper develops and implements the autonomous construction system for the unmanned vibratory roller.Not only does the roller have the function of remote-controlled driving,but it also has the capability of autonomous road construction.The overall system design uses the Programmable Logic Controller(PLC)as the kernel controller.It establishes the communication network through multiple Input/Output(I/O)modules,Recommended Standard 232(RS232)serial port,Controller Area Network(CAN)bus,and wireless networks to control the roller vehicle completely.The locating information is obtained through the Global Navigation Satellite System(GNSS)satellite navigation equipment group to support the process of autonomous construction.According to the experimental results,the autonomous construction system can finally enable the roller to perform driving operations and construction independently,which was a significant step forward in engineering application.
文摘Xi Jinping,general secretary of the Communist Party of China(CPC)Central Committee,stressed that we should adhere to the“two integrations”(namely,integrating the basic tenets of Marxism with China’s specific realities and fine traditional culture),root ourselves in Chinese soil,carry forward the Chinese cultural heritage,and strengthen the academic foundation.We should accelerate the building of an independent knowledge system for Chinese philosophy and social sciences,and formulate original concepts and develop systems of academic discipline,research and discourse,drawing on China’s rich experience of advancing human rights.In the face of changes of a magnitude not seen in a century,in the historic process of advancing the great rejuvenation of the Chinese nation on all fronts through Chinese modernization,we should and must strengthen our theoretical self-consciousness and confidence in the path of Chinese modernization.We need to enhance human rights research,develop the human rights theoretical system and paradigm that are based on Chinese realities and express Chinese voice,and an independent Chinese knowledge system for human rights.
文摘The latest 6G improvements secured autonomous driving's realism in Intelligent Autonomous Transport Systems(IATS).Despite the IATS's benefits,security remains a significant challenge.Blockchain technology has grown in popularity as a means of implementing safe,dependable,and decentralised independent IATS systems,allowing for more utilisation of legacy IATS infrastructures and resources,which is especially advantageous for crowdsourcing technologies.Blockchain technology can be used to address security concerns in the IATS and to aid in logistics development.In light of the inadequacy of reliance and inattention to rights created by centralised and conventional logistics systems,this paper discusses the creation of a blockchain-based IATS powered by deep learning for secure cargo and vehicle matching(BDL-IATS).The BDL-IATS approach utilises Ethereum as the primary blockchain for storing private data such as order and shipment details.Additionally,the deep belief network(DBN)model is used to select suitable vehicles and goods for transportation.Additionally,the chaotic krill herd technique is used to tune the DBN model’s hyper-parameters.The performance of the BDL-IATS technique is validated,and the findings are inspected under a variety of conditions.The simulationfindings indicated that the BDL-IATS strategy outperformed recent state-of-the-art approaches.
文摘In order to realize the explorer autonomy, the software architecture of autonomous mission management system (AMMS) is given for the deep space explorer, and the autonomous mission planning system, the kernel part of this architecture, is designed in detail. In order to describe the parallel activity, the state timeline is introduced to build the formal model of the planning system and based on this model, the temporal constraint satisfaction planning algorithm is proposed to produce the explorer’s activity sequence. With some key subsystems of the deep space explorer as examples, the autonomous mission planning simulation system is designed. The results show that this system can calculate the executable activity sequence with the given mission goals and initial state of the explorer.
基金This research was supported by the Researchers Supporting Program(TUMAProject-2021-27)Almaarefa UniversityRiyadh,Saudi Arabia.Taif University Researchers Supporting Project number(TURSP-2020/161),Taif University,Taif,Saudi Arabia.
文摘Recently,autonomous systems become a hot research topic among industrialists and academicians due to their applicability in different domains such as healthcare,agriculture,industrial automation,etc.Among the interesting applications of autonomous systems,their applicability in agricultural sector becomes significant.Autonomous unmanned aerial vehicles(UAVs)can be used for suitable site-specific weed management(SSWM)to improve crop productivity.In spite of substantial advancements in UAV based data collection systems,automated weed detection still remains a tedious task owing to the high resemblance of weeds to the crops.The recently developed deep learning(DL)models have exhibited effective performance in several data classification problems.In this aspect,this paper focuses on the design of autonomous UAVs with decision support system for weed management(AUAV-DSSWM)technique.The proposed AUAV-DSSWM technique intends to identify the weeds by the use of UAV images acquired from the target area.Besides,the AUAV-DSSWM technique primarily performs image acquisition and image pre-processing stages.Moreover,the Adam optimizer with You Only Look Once Object Detector-(YOLOv3)model is applied for the detection of weeds.For the effective classification of weeds and crops,the poor and rich optimization(PRO)algorithm with softmax layer is applied.The design of Adam optimizer and PRO algorithm for the parameter tuning process results in enhanced weed detection performance.A wide range of simulations take place on UAV images and the experimental results exhibit the promising performance of the AUAV-DSSWM technique over the other recent techniques with the accy of 99.23%.
文摘For decades,antigen presentation on major histocompatibility complex class I for T cell-mediated immunity has been considered the primary function of proteasome-derived peptides1,2.However,whether the products of proteasomal degradation play additional parts in mounting immune responses remains unknown.Antimicrobial peptides serve as a first line of defence against invading pathogens before the adaptive immune system responds.Although the protective function of antimicrobial peptides across numerous tissues is well established,the cellular mechanisms underlying their generation are not fully understood.Here we uncover a role for proteasomes in the constitutive and bacterial-induced generation of defence peptides that impede bacterial growth both in vitro and in vivo by disrupting bacterial membranes.In silico prediction of proteome-wide proteasomal cleavage identified hundreds of thousands of potential proteasome-derived defence peptides with cationic properties that may be generated en route to degradation to act as a first line of defence.
文摘This paper introduces autonomous driving image perception technology,including deep learning models(such as CNN and RNN)and their applications,analyzing the limitations of traditional algorithms.It elaborates on the shortcomings of Faster R-CNN and YOLO series models,proposes various improvement techniques such as data fusion,attention mechanisms,and model compression,and introduces relevant datasets,evaluation metrics,and testing frameworks to demonstrate the advantages of the improved models.
基金Supported by National Natural Science Foundation of China(Grant No.52405104)Jiangxi Provincial Natural Science Foundation(Grant Nos.20242BAB20249 and 20232BAB204041)Science and Technology Project of Department of Transportation of Jiangxi Province(Grant No.2025QN003).
文摘Autonomous driving technology is constantly developing to a higher level of complex scenes,and there is a growing demand for the utilization of end-to-end data-driven control.However,the end-to-end path tracking process often encounters challenges in learning efficiency and generalization.To address this issue,this paper designs a deep deterministic policy gradient(DDPG)-based reinforcement learning strategy that integrates imitation learning and feedforward exploration in the path following process.In imitation learning,the path tracking control data generated by the model predictive control(MPC)method is used to train an end-to-end steering control model of a deep neural network.Another feedforward exploration behavior is predicted by road curvature and vehicle speed,and adds it and imitation learning to the DDPG reinforcement learning to obtain decision-making experience and action prediction behavior of the path tracking process.In the reinforcement learning process,imitation learning is used to update the pre-training parameters of the actor network,and a feedforward steering technique with random noise is adopted for strategy exploration.In the reward function,a hierarchical progressive reward form and a constrained objective reward function referring to MPC are designed,and the actor-critic network architecture is determined.Finally,the path tracking performance of the designed method is verified by comparing various training results,simulations,and HIL tests.The results show that the designed method can effectively utilize pre-training and feedforward prior experience to obtain optimal path tracking performance of an autonomous vehicle,and has better generalization ability than other methods.This study provides an efficient control scheme for improving the end-to-end control performance of autonomous vehicles.
文摘Lethal autonomous weapon systems(LAWS)have become a prominent issue in global security governance.However,significant divergences remain among the world's major countries regarding their regulation:some support restrictions,while others oppose any binding rules,and there are also countries adopting a neutral stance,though their choices are subject to external pressure.In the foreseeable future,the deployment of LAWS will increase further,raising the risk of arms races,with international support for regulating these weapons gaining strength.Yet,establishing legally binding international rules within the United Nations(UN)framework remains a distant prospect.Given this backdrop,China should continue to actively participate in international norms setting concerning LAWS under UN auspices and deepen coordination and cooperation with other countries towards more applicable norms and global governance frameworks.
基金partially funded by the Programa Nacional de Becas y Crédito Educativo of Peru and the Universitat de València,Spain.
文摘Self-Explaining Autonomous Systems(SEAS)have emerged as a strategic frontier within Artificial Intelligence(AI),responding to growing demands for transparency and interpretability in autonomous decisionmaking.This study presents a comprehensive bibliometric analysis of SEAS research published between 2020 and February 2025,drawing upon 1380 documents indexed in Scopus.The analysis applies co-citation mapping,keyword co-occurrence,and author collaboration networks using VOSviewer,MASHA,and Python to examine scientific production,intellectual structure,and global collaboration patterns.The results indicate a sustained annual growth rate of 41.38%,with an h-index of 57 and an average of 21.97 citations per document.A normalized citation rate was computed to address temporal bias,enabling balanced evaluation across publication cohorts.Thematic analysis reveals four consolidated research fronts:interpretability in machine learning,explainability in deep neural networks,transparency in generative models,and optimization strategies in autonomous control.Author co-citation analysis identifies four distinct research communities,and keyword evolution shows growing interdisciplinary links with medicine,cybersecurity,and industrial automation.The United States leads in scientific output and citation impact at the geographical level,while countries like India and China show high productivity with varied influence.However,international collaboration remains limited at 7.39%,reflecting a fragmented research landscape.As discussed in this study,SEAS research is expanding rapidly yet remains epistemologically dispersed,with uneven integration of ethical and human-centered perspectives.This work offers a structured and data-driven perspective on SEAS development,highlights key contributors and thematic trends,and outlines critical directions for advancing responsible and transparent autonomous systems.
文摘Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving.This study aims to enhance the learning efficiency ofmulti-sensor feature fusion in autonomous driving tasks,thereby improving the safety and responsiveness of the system.To achieve this goal,we propose an innovative multi-sensor feature fusion model that integrates three distinct modalities:visual,radar,and lidar data.The model optimizes the feature fusion process through the introduction of two novel mechanisms:Sparse Channel Pooling(SCP)and Residual Triplet-Attention(RTA).Firstly,the SCP mechanism enables the model to adaptively filter out salient feature channels while eliminating the interference of redundant features.This enhances the model’s emphasis on critical features essential for decisionmaking and strengthens its robustness to environmental variability.Secondly,the RTA mechanism addresses the issue of feature misalignment across different modalities by effectively aligning key cross-modal features.This alignment reduces the computational overhead associated with redundant features and enhances the overall efficiency of the system.Furthermore,this study incorporates a reinforcement learning module designed to optimize strategies within a continuous action space.By integrating thismodulewith the feature fusion learning process,the entire system is capable of learning efficient driving strategies in an end-to-end manner within the CARLA autonomous driving simulator.Experimental results demonstrate that the proposedmodel significantly enhances the perception and decision-making accuracy of the autonomous driving system in complex traffic scenarios while maintaining real-time responsiveness.This work provides a novel perspective and technical pathway for the application of multi-sensor data fusion in autonomous driving.
基金National Natural Science Foundation of China(62325304).
文摘This paper presents a comprehensive overview of distributed Nash equilibrium(NE)seeking algorithms in non-cooperative games for multiagent systems(MASs),with a distinct emphasis on the dynamic control perspective.It specifically focuses on the research addressing distributed NE seeking problems in which agents are governed by heterogeneous dynamics.The paper begins by introducing fundamental concepts of general non-cooperative games and the NE,along with definitions of specific game structures such as aggregative games and multi-cluster games.It then systematically reviews existing studies on distributed NE seeking for various classes of MASs from the viewpoint of agent dynamics,including first-order,second-order,high-order,linear,and Euler-Lagrange(EL)systems.Furthermore,the paper highlights practical applications of these theoretical advances in cooperative control scenarios involving autonomous systems with complex dynamics,such as autonomous surface vessels,autonomous aerial vehicles,and other autonomous vehicles.Finally,the paper outlines several promising directions for future research.
