A digital twins enabled underwater intelligent internet vehicle path planning system via reinforcement learning and edge computing

The Autonomous Underwater Glider(AUG)is a kind of prevailing underwater intelligent internet vehicle and occupies a dominant position in industrial applications,in which path planning is an essential problem.Due to the complexity and variability of the ocean,accurate environment modeling and flexible path planning algorithms are pivotal challenges.The traditional models mainly utilize mathematical functions,which are not complete and reliable.Most existing path planning algorithms depend on the environment and lack flexibility.To overcome these challenges,we propose a path planning system for underwater intelligent internet vehicles.It applies digital twins and sensor data to map the real ocean environment to a virtual digital space,which provides a comprehensive and reliable environment for path simulation.We design a value-based reinforcement learning path planning algorithm and explore the optimal network structure parameters.The path simulation is controlled by a closed-loop model integrated into the terminal vehicle through edge computing.The integration of state input enriches the learning of neural networks and helps to improve generalization and flexibility.The task-related reward function promotes the rapid convergence of the training.The experimental results prove that our reinforcement learning based path planning algorithm has great flexibility and can effectively adapt to a variety of different ocean conditions.

Cognitive Granular-Based Path Planning and Tracking for Intelligent Vehicle with Multi-Segment Bezier Curve Stitching

Unmanned vehicles are currently facing many difficulties and challenges in improving safety performance when running in complex urban road traffic environments,such as low intelligence and poor comfort perfor-mance in the driving process.The real-time performance of vehicles and the comfort requirements of passengers in path planning and tracking control of unmanned vehicles have attracted more and more attentions.In this paper,in order to improve the real-time performance of the autonomous vehicle planning module and the comfort requirements of passengers that a local granular-based path planning method and tracking control based on multi-segment Bezier curve splicing and model predictive control theory are pro-posed.Especially,the maximum trajectory curvature satisfying ride comfort is regarded as an important constraint condition,and the corresponding curvature threshold is utilized to calculate the control points of Bezier curve.By using low-order interpolation curve splicing,the planning computation is reduced,and the real-time performance of planning is improved,com-pared with one-segment curve fitting method.Furthermore,the comfort performance of the planned path is reflected intuitively by the curvature information of the path.Finally,the effectiveness of the proposed control method is verified by the co-simulation platform built by MATLAB/Simulink and Carsim.The simulation results show that the path tracking effect of multi-segment Bezier curve fitting is better than that of high-order curve planning in terms of real-time performance and comfort.

Efficient Penetration Testing Path Planning Based on Reinforcement Learning with Episodic Memory

Intelligent penetration testing is of great significance for the improvement of the security of information systems,and the critical issue is the planning of penetration test paths.In view of the difficulty for attackers to obtain complete network information in realistic network scenarios,Reinforcement Learning(RL)is a promising solution to discover the optimal penetration path under incomplete information about the target network.Existing RL-based methods are challenged by the sizeable discrete action space,which leads to difficulties in the convergence.Moreover,most methods still rely on experts’knowledge.To address these issues,this paper proposes a penetration path planning method based on reinforcement learning with episodic memory.First,the penetration testing problem is formally described in terms of reinforcement learning.To speed up the training process without specific prior knowledge,the proposed algorithm introduces episodic memory to store experienced advantageous strategies for the first time.Furthermore,the method offers an exploration strategy based on episodic memory to guide the agents in learning.The design makes full use of historical experience to achieve the purpose of reducing blind exploration and improving planning efficiency.Ultimately,comparison experiments are carried out with the existing RL-based methods.The results reveal that the proposed method has better convergence performance.The running time is reduced by more than 20%.

Energy-efficient joint UAV secure communication and 3D trajectory optimization assisted by reconfigurable intelligent surfaces in the presence of eavesdroppers

We consider a scenario where an unmanned aerial vehicle(UAV),a typical unmanned aerial system(UAS),transmits confidential data to a moving ground target in the presence of multiple eavesdroppers.Multiple friendly reconfigurable intelligent surfaces(RISs) help to secure the UAV-target communication and improve the energy efficiency of the UAV.We formulate an optimization problem to minimize the energy consumption of the UAV,subject to the mobility constraint of the UAV and that the achievable secrecy rate at the target is over a given threshold.We present an online planning method following the framework of model predictive control(MPC) to jointly optimize the motion of the UAV and the configurations of the RISs.The effectiveness of the proposed method is validated via computer simulations.

Rollover Prevention and Motion Planning for an Intelligent Heavy Truck

It is very necessary for an intelligent heavy truck to have the ability to prevent rollover independently.However,it was rarely considered in intelligent vehicle motion planning.To improve rollover stability,a motion planning strategy with autonomous anti rollover ability for an intelligent heavy truck is put forward in this paper.Considering the influence of unsprung mass in the front axle and the rear axle and the body roll stiffness on vehicle rollover stability,a rollover dynamics model is built for the intelligent heavy truck.From the model,a novel rollover index is derived to evaluate vehicle rollover risk accurately,and a model predictive control algorithm is applicated to design the motion planning strategy for the intelligent heavy truck,which integrates the vehicle rollover stability,the artificial potential field for the obstacle avoidance,the path tracking and vehicle dynamics constrains.Then,the optimal path is obtained to meet the requirements that the intelligent heavy truck can avoid obstacles and drive stably without rollover.In addition,three typical scenarios are designed to numerically simulate the dynamic performance of the intelligent heavy truck.The results show that the proposed motion planning strategy can avoid collisions and improve vehicle rollover stability effectively even under the worst driving scenarios.

Intelligent learning technique based-on fuzzy logic for multi-robot path planning

Soccer robot system is a tremendously challenging intelligent system developed to mimic human soccer competition based on the multi discipline research: robotics, intelligent control, computer vision, etc. robot path planning strategy is a very important subject concerning to the performance and intelligence degree of the multi robot system. Therefore, this paper studies the path planning strategy of soccer system by using fuzzy logic. After setting up two fuzziers and two sorts of fuzzy rules for soccer system, fuzzy logic is applied to workspace partition and path revision. The experiment results show that this technique can well enhance the performance and intelligence degree of the system.

An Intelligent Master Model of Computer Aided Process Planning for Large Complicated Stampings

Process planning for large complicated stampings is more complicated, illegible and multiform than that for common stampings. In this paper, an intelligent master model of computer aided process planning （CAPP） for large complicated stampings has been developed based on knowledge based engineering （KBE） and feature technology. This innovative model consists of knowledge base （KB）, process control structure （PCS）, process information model （PIM）, multidisciplinary design optimization （MDO）, model link environment （MLE） and simulation engine （SE）, to realize process planning, optimization, simulation and management integrated to complete intelligent CAPP system. In this model, KBE provides knowledge base, open architecture and knowledge reuse ability to deal with the multi-domain and multi-expression of process knowledge, and forms an integrated environment. With PIM, all the knowledge consisting of objects, constraints, cxtmricncc and decision-makings is carried by object-oriented method dynamically for knowledge-reasoning. PCS makes dynamical knowledge modified and updated timely and accordingly. MLE provides scv. cral methods to make CAPP sysmm associated and integrated. SE provides a programmable mechanism to interpret simulation course and result. Meanwhile, collaborative optimization, one method of MDO, is imported to deal with the optimization distributed for multiple purposes. All these make CAPP sysmm integrated and open to other systems, such as dic design and manufacturing system.

Trajectory Planning and Optimal Lateral Stability Control under Multiple Barriers for Intelligent Vehicle

Based on analysis and evaluation on the circular, cosine type, constant-speed offset type and ladder type lane change trajectory, this paper proposes an intelligent vehicle lane change trajectory model under multiple barriers, proposes its dynamic constraints in the light of the cellular automata theory, obtains the desired lane change trajectory using this method, and finally changes into a simple coefficient selection problem. Secondly, based on the quadratic optimal control theory, this paper proposes a state space analysis method of intelligent vehicle lateral control, and designs an optimal controller for lateral stability of H2 vehicles. The computer simulation results show that compared with other vehicle trajectory methods, the method in this paper is able to simply and rapidly describe the trajectory, and can describe the intelligent vehicle lane change trajectory under a variety of situations, wherein the controller is reliable and capable of fast convergence.

Integrating Process Planning and Scheduling with an Intelligent Facilitator

This paper introduces a dynamic facilitating mechan is m for the integration of process planning and scheduling in a batch-manufacturi ng environment. This integration is essential for the optimum use of production resources and generation of realistic process plans that can be readily executed with little or no modification. In this paper, integration is modeled in two le vels, viz., process planning and scheduling, which are linked by an intelligent facilitator. The process planning module employs an optimization approach in whi ch the entire plan solution space is first generated and a search algorithm is t hen used to find the optimal plan. Based on the result of scheduling module an u nsatisfactory performance parameter is fed back to the facilitator, which then i dentifies a particular job and issues a change to its process plan solution spac e to obtain a satisfactory schedule.

Gait planning and intelligent control for a quadruped robot

We present a method for designing free gaits for a structurally symmetrical quadruped robot capable of performing statically stable, omnidirectional walking on irregular terrain. The robot＇s virtual model is constructed and a control algorithm is proposed by applying virtual components at some strategic locations. The deliberative-based controller can generate flexible sequences of leg transferences while maintaining walking speed, and choose optimum foothold for moving leg based on integration data of exteroceptive terrain profile. Simulation results are presented to show the gait＇s efficiency and system＇s stability in adapting to an uncertain terrain.

Coordinated dynamic mission planning scheme for intelligent multi-agent systems

Mission planning was thoroughly studied in the areas of multiple intelligent agent systems,such as multiple unmanned air vehicles,and multiple processor systems.However,it still faces challenges due to the system complexity,the execution order constraints,and the dynamic environment uncertainty.To address it,a coordinated dynamic mission planning scheme is proposed utilizing the method of the weighted AND/OR tree and the AOE-Network.In the scheme,the mission is decomposed into a time-constraint weighted AND/OR tree,which is converted into an AOE-Network for mission planning.Then,a dynamic planning algorithm is designed which uses task subcontracting and dynamic re-decomposition to coordinate conflicts.The scheme can reduce the task complexity and its execution time by implementing real-time dynamic re-planning.The simulation proves the effectiveness of this approach.

System Design and Implementation of Intelligent Fire Engine Path Planning based on SAT Algorithm

In this paper, in order to make intelligent fi re car complete autonomy path planning in simulation map. Proposed system design of intelligent fi re car path planning based on SAT. The system includes a planning module, a communication module, a control module. Control module via the communication module upload the initial state and the goal state to planning module. Planning module solve this planning solution,and then download planning solution to control module, control the movement of the car fi re. Experiments show this the system is tracking short time, higher planning effi ciency.

Adaptive Resource Planning for AI Workloads with Variable Real-Time Tasks

AI(Artificial Intelligence)workloads are proliferating in modernreal-time systems.As the tasks of AI workloads fluctuate over time,resourceplanning policies used for traditional fixed real-time tasks should be reexamined.In particular,it is difficult to immediately handle changes inreal-time tasks without violating the deadline constraints.To cope with thissituation,this paper analyzes the task situations of AI workloads and findsthe following two observations.First,resource planning for AI workloadsis a complicated search problem that requires much time for optimization.Second,although the task set of an AI workload may change over time,thepossible combinations of the task sets are known in advance.Based on theseobservations,this paper proposes a new resource planning scheme for AIworkloads that supports the re-planning of resources.Instead of generatingresource plans on the fly,the proposed scheme pre-determines resourceplans for various combinations of tasks.Thus,in any case,the workload isimmediately executed according to the resource plan maintained.Specifically,the proposed scheme maintains an optimized CPU(Central Processing Unit)and memory resource plan using genetic algorithms and applies it as soonas the workload changes.The proposed scheme is implemented in the opensourcesimulator SimRTS for the validation of its effectiveness.Simulationexperiments show that the proposed scheme reduces the energy consumptionof CPU and memory by 45.5%on average without deadline misses.

From Parallel Plants to Smart Plants:Intelligent Control and Management for Plant Growth

Precision management of agricultural systems, aiming at optimizing profitability, productivity and sustainability, comprises a set of technologies including sensors, information systems, and informed management, etc. Expert systems are expected to aid farmers in plant management or environment control, but they are mostly based on the offline and static information, deviated from the actual situation. Parallel management, achieved by virtual/artificial agricultural system, computational experiment and parallel execution, provides a generic framework of solution for online decision support. In this paper, we present the three steps toward the parallel management of plant: growth description U+0028 the crop model U+0029, prediction, and prescription. This approach can update the expert system by adding learning ability and the adaption of knowledge database according to the descriptive and predictive model. The possibilities of passing the knowledge of experienced farmers to younger generation, as well as the application to the parallel breeding of plant through such system, are discussed. © 2017 Chinese Association of Automation.

A New Generation of Rules-based Approach:Mivar-based Intelligent Planning of Robot Actions(MIPRA)and Brains for Autonomous Robots

To create autonomous robots,both hardware and software are needed.If enormous progress has already been made in the field of equipment,then robot software depends on the development of artificial intelligence.This article proposes a solution for creating“logical”brains for autonomous robots,namely,an approach for creating an intelligent robot action planner based on Mivar expert systems.The application of this approach provides opportunities to reduce the computational complexity of solving planning problems and the requirements for the computational characteristics of hardware platforms on which intelligent planning systems are deployed.To theoretically and practically justify the expediency of using logically solving systems,in particular Mivar expert systems,to create intelligent planners,the MIPRA(Mivar-based Intelligent Planning of Robot Actions)planner was created to solve problems such as STRIPS for permutation cubes in the Blocks World domain.The planner is based on the platform for creating expert systems of the Razumator.As a result,the Mivar planner can process information about the state of the subject area based on the analysis of cause-effect relationships and an algorithm for automatically constructing logical inference(finding a solution from“Given”to“Find”).Moreover,an important feature of the MIPRA is that the system is built on the principles of a“white box”,due to which the system can explain any of its decisions and provide justification for the actions performed in the form of a retrospective of the stages of the decision-making process.When preparing a set of robot actions aimed at changing control objects,expert knowledge is used,which is the basis for the functioning algorithms of the planner.This approach makes it possible to include an expert in the process of organizing the work of the intelligent planner and use existing knowledge about the subject area.Practical experiments of this study have shown that instead of many hours and powerful multiprocessor servers,the MIPRA on a personal computer solves the planning problems with the following number of cubes:10 cubes can be rearranged in 0.028 seconds,100 cubes in 0.938 seconds,and 1000 cubes in 84.188 seconds.The results of this study can be used to reduce the computational complexity of solving tasks of planning the actions of robots,as well as their groups,multilevel heterogeneous robotic systems,and cyber-physical systems of various bases and purposes.

Swarm Intelligence in Power System Planning

Power system planning is one of the essential tasks in the power system operation management, which requires in-depth knowledge of the system under consideration. It can be regarded as a nonlinear, discontinuous, constrained multi objective optimization problem. Although the traditional optimization tools can be used, the modern planning problem requires more advanced optimization tools. In this paper, a survey of state-of-the-art mathematical optimization methods that facilitates power system planning is provided, and the needs of introducing swarm intelligence approaches into power system planning are discussed.

Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises

As cyber threats keep changing and business environments adapt, a comprehensive approach to disaster recovery involves more than just defensive measures. This research delves deep into the strategies required to respond to threats and anticipate and mitigate them proactively. Beginning with understanding the critical need for a layered defense and the intricacies of the attacker’s journey, the research offers insights into specialized defense techniques, emphasizing the importance of timely and strategic responses during incidents. Risk management is brought to the forefront, underscoring businesses’ need to adopt mature risk assessment practices and understand the potential risk impact areas. Additionally, the value of threat intelligence is explored, shedding light on the importance of active engagement within sharing communities and the vigilant observation of adversary motivations. “Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises” is a comprehensive guide for organizations aiming to fortify their cybersecurity posture, marrying best practices in proactive and reactive measures in the ever-challenging digital realm.

Design and Realization of Simple Intelligent Robotic Lawn Mower Based on Arduino Mega2560

Robotic lawn mowers available in markets are much more complicated with high cost, hence, a new robot is designed in the research. In detail, the control system is made up of Arduino Mega2560 and 11 sensors and the robot works with four wheels （two front and back wheels） driven by an electric motor. Furthermore, the platform of lawn-mowing is designed semicircle, equipped with three small high- speed and low-power electric motors; the cutting distance is determined by width of motherboard. In addition, the hardware of the system is made up of circuit control and working machines, of which the former includes a single chip unit, a wireless remote control, a sensor unit, an infrared array module, a driving module of electric motor, a display unit and a power source; the latter includes a mowing platform and a sensor window. In addition, the related software is programmed using C language and modular programming involving PWM program, Hall sensor program, liquid-crys- tal display, tilt program, supersonic sounding program, infrared obstacle-avoidance program, parking program, and remote control program. After hardware was adjust- ed, the robotic lawn mower was tested for multiple times in a standard lawn, and a satisfied effect was achieved.

基于AI Planning的Parlay X电信业务设计

Parlay X是一组电信网络服务(WS)应用程序接口(APIs),智能规划可作为一种WS组合(WSC)方法,所以理论上可基于智能规划设计使用Parlay X APIs的电信业务.但WSC有容错和对动作变化推理的要求,电信业务有多种触发方式和异步响应等特性,智能规划要有确定状态等Parlay X不满足的条件.结合典型Parlay X电信业务模型,基于AI Planning的Parlay X电信业务设计(APBPTSD)方法被提出.APBPTSD用标准后处理动作(SPCA)处理容错,用等待事件动作(WFEA)处理异步响应,用本地动作(LA)处理动作变化推理并对多种触发进行业务分拆.在已有业务上实验表明APBPTSD可有效指导Parlay X电信业务设计.

路径规划在Intelligent Agent三维人体动画中的应用

1 引言 计算机动画的研究始于60年代初，随着计算机图形学理论的逐步深入与完善，特别是三维几何造型技术、真实感图形生成技术的发展，利用计算机进行三维模拟的应用日趋广泛、复杂。Agent作为一个自包含的、交互的、相互独立的实体出现在计算机动画中，为在虚拟现实中模拟人类社会行为找到了替代者。而这种模拟Agent大体上可分为Avatar与Intelligent Agent两大类。