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| 1 | Autonomous maneuver strategy of swarm air combat based on DDPG显示文摘Unmanned aerial vehicles(UAVs)have been found significantly important in the air combats,where intelligent and swarms of UAVs will be able to tackle with the tasks of high complexity and dynamics.The key to empower the UAVs with such capability is the autonomous maneuver decision making.In this paper,an autonomous maneuver strategy of UAV swarms in beyond visual range air combat based on reinforcement learning is proposed.First,based on the process of air combat and the constraints of the swarm,the motion model of UAV and the multi-to-one air combat model are established.Second,a two-stage maneuver strategy based on air combat principles is designed which include inter-vehicle collaboration and target-vehicle confrontation.Then,a swarm air combat algorithm based on deep deterministic policy gradient strategy(DDPG)is proposed for online strategy training.Finally,the effectiveness of the proposed algorithm is validated by multi-scene simulations.The results show that the algorithm is suitable for UAV swarms of different scales. | Luhe Wang Jinwen Hu Zhao Xu Chunhui Zhao | 2021 | Autonomous Intelligent Systems2021，1，1： | 3 |
| 2 | Lessons from human vision for robotic design显示文摘The visual guidance of goal-directed movements requires transformations of incoming visual information that are different from those required for visual perception.For us to grasp an object successfully,our brain must use justin-time computations of the object’s real-world size and shape,and its orientation and disposition with respect to our hand.These requirements have led to the emergence of dedicated visuomotor modules in the posterior parietal cortex of the human brain(the dorsal visual stream)that are functionally distinct from networks in the occipito-temporal cortex(the ventral visual stream)that mediate our conscious perception of the world.Although the identification and selection of goal objects and an appropriate course of action depends on the perceptual machinery of the ventral stream and associated cognitive modules,the execution of the subsequent goal-directed action is mediated by dedicated online control systems in the dorsal stream and associated motor areas.The dorsal stream allows an observer to reach out and grasp objects with exquisite ease,but by itself,deals only with objects that are visible at the moment the action is being programmed.The ventral stream,however,allows an observer to escape the present and bring to bear information from the past-including information about the function of objects,their intrinsic properties,and their location with reference to other objects in the world.Ultimately then,both streams contribute to the production of goal-directed actions.The principles underlying this division of labour between the dorsal and ventral streams are relevant to the design and implementation of autonomous robotic systems. | Melvyn A.Goodale | 2021 | Autonomous Intelligent Systems2021，1，1： | 1 |
| 3 | Positioning accuracy improvement of laser navigation using unscented Kalman filter显示文摘 | Kim J Jung K Kim J | 2013 | Intelligent Autonomous Systems Springer2013，193，1： | 1 |
| 4 | An Incremental Self- Deployment Algorithm for Mobile Sensor Networks 显示文摘 | Howard A Mataric M J Sukhatme G S | 2002 | Autonomous Robots Special Issue on Intelligent Embedded Systems2002，13，2： | 1 |
| 5 | Lot Release Control Using Genetics Based Machine Learning in a Semiconductor Manufacturing System 显示文摘 | Ryohei T Nobutada F Kanji U | 2006 | Intelligent Autonomous Systems2006，，9： | 1 |
| 6 | Distributed multi-robot sweep coverage for a region with unknown workload distribution显示文摘This paper considers the scenario where multiple robots collaboratively cover a region in which the exact distribution of workload is unknown prior to the operation.The workload distribution is not uniform in the region,meaning that the time required to cover a unit area varies at different locations of the region.In our approach,we divide the target region into multiple horizontal stripes,and the robots sweep the current stripe while partitioning the next stripe concurrently.We propose a distributed workload partition algorithm and prove that the operation time on each stripe converges to the minimum under the discrete-time update law.We conduct comprehensive simulation studies and compare our method with the existing methods to verify the theoretical results and the advantage of the proposed method.Flight experiments on mini drones are also conducted to demonstrate the practicality of the proposed algorithm. | Muqing Cao Kun Cao Xiuxian Li Shenghai Yuan Yang Lyu Thien-Minh Nguyen Lihua Xie | 2021 | Autonomous Intelligent Systems2021，1，1： | 1 |
| 7 | Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic显示文摘Autonomous driving has attracted significant research interests in the past two decades as it offers many potential benefits,including releasing drivers from exhausting driving and mitigating traffic congestion,among others.Despite promising progress,lane-changing remains a great challenge for autonomous vehicles(AV),especially in mixed and dynamic traffic scenarios.Recently,reinforcement learning(RL)has been widely explored for lane-changing decision makings in AVs with encouraging results demonstrated.However,the majority of those studies are focused on a single-vehicle setting,and lane-changing in the context of multiple AVs coexisting with human-driven vehicles(HDVs)have received scarce attention.In this paper,we formulate the lane-changing decision-making of multiple AVs in a mixed-traffic highway environment as a multi-agent reinforcement learning(MARL)problem,where each AV makes lane-changing decisions based on the motions of both neighboring AVs and HDVs.Specifically,a multi-agent advantage actor-critic(MA2C)method is proposed with a novel local reward design and a parameter sharing scheme.In particular,a multi-objective reward function is designed to incorporate fuel efficiency,driving comfort,and the safety of autonomous driving.A comprehensive experimental study is made that our proposed MARL framework consistently outperforms several state-of-the-art benchmarks in terms of efficiency,safety,and driver comfort. | Wei Zhou Dong Chen Jun Yan Zhaojian Li Huilin Yin Wanchen Ge | 2022 | Autonomous Intelligent Systems2022，2，1： | 1 |
| 8 | Model Based Multi-Level Prototyping 显示文摘 | Ansgar Bredenfeld Jorg Wilberg | 1999 | IEEE Institute for Autonomous Intelligent Systems (AiS) D-53754 Sankt Augustin Germany1999，，： | 1 |
| 9 | Learning In- verse Dynamics for Redundant Manipulator Control 显示文摘 | Sun de la Cruz Dana Kulic William Owen | 2010 | Autonomous and Intelligent Systems2010，，： | 1 |
| 10 | An incremental self-deployment algorithm for mobile sensor network 显示文摘 | HOWARD A MATARIC M J SUKHATME G S | 2002 | Autonomous Robots: Special Issue on Intelligent Embedded Systems2002，13，2： | 1 |
| 11 | Model based visual servoing tasks with an autono- mous humanoid robot UR 显示文摘 | MOUGHLBAY A A CERVERA E MARTINET P | 2013 | Frontiers of Intelli- gent Autonomous Systems Studies in Computational Intelligence2013，466，： | 1 |
| 12 | An incremental self-deployment algorithm for mobile sensor networks 显示文摘 | HOWARD A MATARIC M J SUKHATME G S | 2002 | Autonomous Robots Special Issue on Intelligent Embedded Systems2002，13，2： | 1 |
| 13 | An Incremental Self-Deployment Algorithm for Mobile Sensor Networks显示文摘 | Andrew Howard Maja J Mataric Gaurav S Sukhatme | 2003 | Autonomous Robots Special Issue on Intelligent Embedded Systems2003，13，2： | 1 |
| 14 | Sensors used for autonomous navigation显示文摘 | | 1999 | Advances in Intelligent autonomous system1999，65，4： | 1 |
| 15 | Drivable road recognition by muhi- layered LiDAR and vision 显示文摘 | Gim S Meo I Park Y | 2013 | Intelligent Autonomous Systems2013，，12： | 1 |
| 16 | An Incremental Self-Deployment Algorithm for Mobile Sensor Networks 显示文摘 | Howard A Matari M J A G S S | 2002 | Autonomous Robots Special Issue on Intelligent Embedded Systems2002，13，2： | 1 |
| 17 | Extreme learning machine with adaptive growth of hidden nodes and incremen- tal updating of output weight 显示文摘 | ZHANG R LAN Y HUANG G B | 2011 | International Conference on Autonomous and Intelligence Systems Burnaby Canada2011，6752，： | 1 |
| 18 | Neural network-based adaptive sliding mode control for underactuated dual overhead cranes suffering from matched and unmatched disturbances显示文摘To improve transportation capacity,dual overhead crane systems(DOCSs)are playing an increasingly important role in the transportation of large/heavy cargos and containers.Unfortunately,when trying to deal with the control problem,current methods fail to fully consider such factors as external disturbances,input dead zones,parameter uncertainties,and other unmodeled dynamics that DOCSs usually suffer from.As a result,dramatic degradation is caused in the control performance,which badly hinders the practical applications of DOCSs.Motivated by this fact,this paper designs a neural network-based adaptive sliding mode control(SMC)method for DOCS to solve the aforementioned issues,which achieves satisfactory control performance for both actuated and underactuated state variables,even in the presence of matched and mismatched disturbances.The asymptotic stability of the desired equilibrium point is proved with rigorous Lyapunov-based analysis.Finally,extensive hardware experimental results are collected to verify the efficiency and robustness of the proposed method. | Tianci Wen Yongchun Fang Biao Lu | 2022 | Autonomous Intelligent Systems2022，2，1： | 0 |
| 19 | An ontological modelling of multi-attribute criticality analysis to guide Prognostics and Health Management program development显示文摘Digital technologies are becoming more pervasive and industrial companies are exploiting them to enhance the potentialities related to Prognostics and Health Management(PHM).Indeed,PHM allows to evaluate the health state of the physical assets as well as to predict their future behaviour.To be effective in developing PHM programs,the most critical assets should be identified so to direct modelling efforts.Several techniques could be adopted to evaluate asset criticality;in industrial practice,criticality analysis is amongst the most utilised.Despite the advancement of artificial intelligence for data analysis and predictions,the criticality analysis,which is built upon both quantitative and qualitative data,has not been improved accordingly.It is the goal of this work to propose an ontological formalisation of a multi-attribute criticality analysis in order to i)fix the semantics behind the terms involved in the analysis,ii)standardize and uniform the way criticality analysis is performed,and iii)take advantage of the reasoning capabilities to automatically evaluate asset criticality and associate a suitable maintenance strategy.The developed ontology,called MOCA,is tested in a food company featuring a global footprint.The application shows that MOCA can accomplish the prefixed goals;specifically,high priority assets towards which direct PHM programs are identified.In the long run,ontologies could serve as a unique knowledge base that integrate multiple data and information across facilities in a consistent way.As such,they will enable advanced analytics to take place,allowing to move towards cognitive Cyber Physical Systems that enhance business performance for companies spread worldwide. | Adalberto Polenghi Irene Roda Marco Macchi Alessandro Pozzetti | 2022 | Autonomous Intelligent Systems2022，2，1： | 0 |
| 20 | End-of-Life Decision making in circular economy using generalized colored stochastic Petri nets显示文摘Circular economy enables to restore product value at the end of life i.e.when no longer used or damaged.Thus,the product life cycle is extended and this economy permits to reduce waste increase and resources rarefaction.There are several revaluation options(reuse,remanufacturing,recycling,...).So,decisionmakers need to assess these options to determine which is the best decision.Thus,we will present a study about an End-Of-Life(EoL)decision making which aims to facilitate the industrialization of circular economy.For this,it is essential to consider all variables and parameters impacting the decision of the product trajectory.A first part of the work proposes to identify the variables and parameters impacting the decision making.A second part proposes an assessment approach based on a modeling by Generalized Colored Stochastic Petri Net(GCSPN)and on a Monte-Carlo simulation.The approach developed is tested on an industrial example from the literature to analyze the efficiency and effectiveness of the model.This first application showed the feasibility of the approach,and also the limits of the GCSPN modelling. | Gautier Vanson Pascale Marangé Eric Levrat | 2022 | Autonomous Intelligent Systems2022，2，1： | 0 |
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