Deep Learning Based Autonomous Transport System for Secure Vehicle and Cargo Matching

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.

Current trends in the development of intelligent unmanned autonomous systems

Intelligent unmanned autonomous systems are some of the most important applications of artificial intelligence （AI）. The development of such systems can significantly promote innovation in AI technologies. This paper introduces the trends in the development of intelligent unmanned autonomous systems by summarizing the main achievements in each technological platform. Furthermore, we classify the relevant technologies into seven areas, including AI technologies, unmanned vehicles, unmanned aerial vehicles, service robots, space robots, marine robots, and unmanned workshops/intelligent plants. Current trends and de- velopments in each area are introduced.

Investigation on full distribution CNC system based on SERCOS bus

A full distribution CNC system based on SERCOS bus is studied in accordance with the limitations of traditional PC-based motion card. The conventional PC-based motion control card is dispersed into several autonomous intelligent servo-control units with the function of servo driver. The autonomous intelligent servocontrol units realize the loop control of position, velocity and current. Interpolation computation is completed in PC and the computational results are transferred to every autonomous intelligent servo-control unit by high speed SERCOS bus. Software or hardware synchronization technology is used to ensure all servomotors are successive and synchronously running. The communication and synchronization technology of SERCOS are also researched and the autonomous intelligent servo-control card is developed byself. Finally, the experiment of circle contour process on a prototype system proves the feasibility.

Adaptive Object Tracking Discriminate Model for Multi-Camera Panorama Surveillance in Airport Apron

Autonomous intelligence plays a significant role in aviation security.Since most aviation accidents occur in the take-off and landing stage,accurate tracking of moving object in airport apron will be a vital approach to ensure the operation of the aircraft safely.In this study,an adaptive object tracking method based on a discriminant is proposed in multi-camera panorama surveillance of large-scale airport apron.Firstly,based on channels of color histogram,the pre-estimated object probability map is employed to reduce searching computation,and the optimization of the disturbance suppression options can make good resistance to similar areas around the object.Then the object score of probability map is obtained by the sliding window,and the candidate window with the highest probability map score is selected as the new object center.Thirdly,according to the new object location,the probability map is updated,the scale estimation function is adjusted to the size of real object.From qualitative and quantitative analysis,the comparison experiments are verified in representative video sequences,and our approach outperforms typical methods,such as distraction-aware online tracking,mean shift,variance ratio,and adaptive colour attributes.

Model predictive control for autonomous ground vehicles:a review

This paper reviews model predictive control(MPC)and its wide applications to both single and multiple autonomous ground vehicles(AGVs).On one hand,MPC is a well-established optimal control method,which uses the predicted future information to optimize the control actions while explicitly considering constraints.On the other hand,AGVs are able to make forecasts and adapt their decisions in uncertain environments.Therefore,because of the nature of MPC and the requirements of AGVs,it is intuitive to apply MPC algorithms to AGVs.AGVs are interesting not only for considering them alone,which requires centralized control approaches,but also as groups of AGVs that interact and communicate with each other and have their own controller onboard.This calls for distributed control solutions.First,a short introduction into the basic theoretical background of centralized and distributed MPC is given.Then,it comprehensively reviews MPC applications for both single and multiple AGVs.Finally,the paper highlights existing issues and future research directions,which will promote the development of MPC schemes with high performance in AGVs.