Path-Following Control With Obstacle Avoidance of Autonomous Surface Vehicles Subject to Actuator Faults

This paper investigates the path-following control problem with obstacle avoidance of autonomous surface vehicles in the presence of actuator faults,uncertainty and external disturbances.Autonomous surface vehicles inevitably suffer from actuator faults in complex sea environments,which may cause existing obstacle avoidance strategies to fail.To reduce the influence of actuator faults,an improved artificial potential function is constructed by introducing the lower bound of actuator efficiency factors.The nonlinear state observer,which only depends on measurable position information of the autonomous surface vehicle,is used to address uncertainties and external disturbances.By using a backstepping technique and adaptive mechanism,a path-following control strategy with obstacle avoidance and fault tolerance is designed which can ensure that the tracking errors converge to a small neighborhood of zero.Compared with existing results,the proposed control strategy has the capability of obstacle avoidance and fault tolerance simultaneously.Finally,the comparison results through simulations are given to verify the effectiveness of the proposed method.

Research on Anthropomorphic Obstacle Avoidance Trajectory Planning for Adaptive Driving Scenarios Based on Inverse Reinforcement Learning Theory

The forward design of trajectory planning strategies requires preset trajectory optimization functions,resulting in poor adaptability of the strategy and an inability to accurately generate obstacle avoidance trajectories that conform to real driver behavior habits.In addition,owing to the strong time-varying dynamic characteristics of obstacle avoidance scenarios,it is necessary to design numerous trajectory optimization functions and adjust the corresponding parameters.Therefore,an anthropomorphic obstacle-avoidance trajectory planning strategy for adaptive driving scenarios is proposed.First,numerous expert-demonstrated trajectories are extracted from the HighD natural driving dataset.Subsequently,a trajectory expectation feature-matching algorithm is proposed that uses maximum entropy inverse reinforcement learning theory to learn the extracted expert-demonstrated trajectories and achieve automatic acquisition of the optimization function of the expert-demonstrated trajectory.Furthermore,a mapping model is constructed by combining the key driving scenario information that affects vehicle obstacle avoidance with the weight of the optimization function,and an anthropomorphic obstacle avoidance trajectory planning strategy for adaptive driving scenarios is proposed.Finally,the proposed strategy is verified based on real driving scenarios.The results show that the strategy can adjust the weight distribution of the trajectory optimization function in real time according to the“emergency degree”of obstacle avoidance and the state of the vehicle.Moreover,this strategy can generate anthropomorphic trajectories that are similar to expert-demonstrated trajectories,effectively improving the adaptability and acceptability of trajectories in driving scenarios.

基于权值的新能源集控电量均衡CSMA/CA改进算法

针对新能源集控任务下发过程中,单个节点电量消耗较大,直接影响电量均衡的问题,提出一种基于权值的新能源集控电量均衡CSMA/CA改进算法。建立电池等效模型,通过计算健康系数推导新能源集控节点当前电量。计算各个电池当前电量与预期之间的差值,当60%以上节点的差值大于设定的阈值,认为电量存在不均衡的问题。利用熵权法与CSMA/CA算法计算三个关键变量的权值,即NB、CW和BE,并赋权,实现CSMA/CA算法改进,以此动态调控任务,实现新能源集控电量均衡消耗。结果表明:利用权值改进后,电量均衡度平均值明显更高。这种方法为促进电量均衡提供新思路。

LSDA-APF:A Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on 5G Communication Environment

In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone to fall into the trap of local optimization.Therefore,this paper proposes an improved artificial potential field(APF)algorithm,which uses 5G communication technology to communicate between the USV and the control center.The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios.Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks,the algorithm introduces the concept of dynamic artificial potential field.For the multiple obstacles encountered in the process of USV sailing,based on the International Regulations for Preventing Collisions at Sea(COLREGS),the USV determines whether the next step will fall into local optimization through the discriminationmechanism.The local potential field of the USV will dynamically adjust,and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions.The objective function and cost function are designed at the same time,so that the USV can smoothly switch between the global path and the local obstacle avoidance.The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment,and take navigation time and economic cost into account.

ZigBee网络CSMA/CA协议及典型网络拓扑性能仿真分析

ZigBee网络在未来物联网行业尤其是智慧城市的建设中将得到广泛的应用。CSMA/CA协议用于节点间数据传输时的信道争用机制,利用ACK信号来避免冲突的发生。介绍了ZigBee网络技术特点,阐述了IEEE802.11MAC层的点协调功能PCF和分布协调功能DCF,分析了CSMA/CA协议尽量避免碰撞的机制和MAC层成帧效率。基于OPNET仿真平台,搭建了星型、簇型和网状型三种典型ZigBee网络拓扑,设置了仿真场景和节点属性,分析了不同拓扑结构、不同节点数对网络吞吐量的影响。结果表明,三种典型网络拓扑中,网状型网络的吞吐量明显高于其它两种网络,但无论哪种网络,其吞吐量都随着节点数的增多而呈下降趋势。因此,在ZigBee网络的实际应用中,需综合考虑网络布设的拓扑结构和合适的节点数,提高ZigBee网络的通信效率。

物联网数据融合的动态队列感知CSMA调度方案

物联网环境下的数据融合要求采集和处理各种具有不同时间特性的数据,包括实时数据和非实时数据。数据的实时交付作为数据融合的前提对融合结果有着极其重要的影响。论文提出了一种基于动态队列感知的分布式调度算法(QACSMA),该机制考虑了数据的丢包率、时延约束和队列预测占用率,利用贪婪算法动态调整竞争窗口,以在链路队列长度变大时,提高长队列的服务效率,降低队列长度和时延,提高吞吐量。仿真结果表明,与其他典型调度方案相比,该算法的吞吐量和延迟性能均有所提高。

Co-Frequency Interference Analysis and Avoidance Between NGSO Constellations: Challenges, Techniques, and Trends

In recent years,as giant satellite constellations grow rapidly worldwide,the co-existence between constellations has been widely concerned.In this paper,we overview the co-frequency interference(CFI)among the giant non-geostationary orbit(NGSO)constellations.Specifically,we first summarize the CFI scenario and evaluation index among different NGSO constellations.Based on statistics about NGSO constellation plans,we analyse the challenges in mitigation and analysis of CFI.Next,the CFI calculation methods and research progress are systematically sorted out from the aspects of interference risk analysis framework,numerical calculation and link construction.Then,the feasibility of interference mitigation technologies based on space,frequency domain isolation,power control,and interference alignment mitigation in the NGSO mega-constellation CFI scenario are further sorted out.Finally,we present promising directions for future research in CFI analysis and CFI avoidance.

基于以太网负载的改进CSMA协议

针对以太网网络性能随网络负载变化的特性,提出一种基于以太网负载的改进CSMA协议。该协议根据终端冲突数目,相应调整1-坚持CSMA协议、非坚持CSMA协议和P-坚持CSMA协议,从而在轻载时降低延时,在重载时减少冲突,使得网络系统处于稳定状态。实验结果表明,负载对网络吞吐量与网络延时有重大影响,改进CSMA协议能达到网络吞吐量最优化的目的,并且提高网络实时性,大大提升了网络通信效率。

Improved RRT^(∗)Algorithm for Automatic Charging Robot Obstacle Avoidance Path Planning in Complex Environments

A new and improved RRT∗algorithm has been developed to address the low efficiency of obstacle avoidance planning and long path distances in the electric vehicle automatic charging robot arm.This algorithm enables the robot to avoid obstacles,find the optimal path,and complete automatic charging docking.It maintains the global completeness and path optimality of the RRT algorithmwhile also improving the iteration speed and quality of generated paths in both 2D and 3D path planning.After finding the optimal path,the B-sample curve is used to optimize the rough path to create a smoother and more optimal path.In comparison experiments,the new algorithmyielded reductions of 35.5%,29.2%,and 11.7%in search time and 22.8%,19.2%,and 9%in path length for the 3D environment.Finally,experimental validation of the automatic charging of electric vehicles was conducted to further verify the effectiveness of the algorithm.The simulation experimental validation was carried out by kinematic modeling and building an experimental platform.The error between the experimental results and the simulation results is within 10%.The experimental results show the effectiveness and practicality of the algorithm.

A Novel Obstacle Avoidance Consensus Control for Multi-AUV Formation System

In this paper, the fixed-time event-triggered obstacle avoidance consensus control for a multi-AUV time-varying formation system in a 3D environment is presented by using an improved artificial potential field and leader-follower strategy(IAPF-LF). Firstly, the proposed fixed-time control can achieve the desired multi-AUV formation within a fixed settling time in any initial system state. Secondly, an event-triggered communication strategy is developed to govern the communication among AUVs, and the communication energy consumption can be decremented. The time-varying formation obstacle avoidance control algorithm based on IAPF-LF is designed to avoid static and dynamic obstacles, the desired formation is maintained in the presence of external disturbances, and there is no Zeno behavior under the fixed-time event-triggered consensus control strategy.The stability of the system is proved by the Lyapunov function and inequality scaling. Finally, simulation examples and water pool experiments are reported to verify the performance of the proposed theoretical algorithms.

FSE2R:An Improved Collision-Avoidance-based Energy Efficient Route Selection Protocolin USN

The 3D Underwater Sensor Network(USNs)has become the most optimistic medium for tracking and monitoring underwater environment.Energy and collision are two most critical factors in USNs for both sparse and dense regions.Due to harsh ocean environment,it is a challenge to design a reliable energy efficient with collision free protocol.Diversity in link qualities may cause collision and frequent communication lead to energy loss;that effects the network performance.To overcome these challenges a novel protocol Forwarder Selection Energy Efficient Routing(FSE2R)is proposed.Our proposal’s key idea is based on computation of node distance from the sink,Residual Energy(RE)of each node and Signal to Interference Noise Ratio(SINR).The node distance from sink and RE is computed for reliable forwarder node selection and SINR is used for analysis of collision.The novel proposal compares with existing protocols like H2AB,DEEP,and E2LR to achieve Quality of Service(QoS)in terms of through-put,packet delivery ratio and energy consumption.The comparative analysis shows that FSE2R gives on an average 30%less energy consumption,24.62%better PDR and 48.31%less end-to-end delay compared to other protocols.

Circular Formation Control with Collision Avoidance Based on Probabilistic Position

In this paper,we study the circular formation problem for the second-order multi-agent systems in a plane,in which the agents maintain a circular formation based on a probabilistic position.A distributed hybrid control protocol based on a probabilistic position is designed to achieve circular formation stabilization and consensus.In the current framework,the mobile agents follow the following rules:1)the agent must follow a circular trajectory;2)all the agents in the same circular trajectory must have the same direction.The formation control objective includes two parts:1)drive all the agents to the circular formation;2)avoid a collision.Based on Lyapunov methods,convergence and stability of the proposed circular formation protocol are provided.Due to limitations in collision avoidance,we extend the results to LaSalle’s invariance principle.Some theoretical examples and numerical simulations show the effectiveness of the proposed scheme.

Tax avoidance and earnings management:a neural network approach for the largest European economies

In this study,we investigate the relationship between tax avoidance and earnings management in the largest five European Union economies by using artificial neural network regressions.This methodology allows us to deal with nonlinearities detected in the data,which is the principal contribution to the previous literature.We ana-lyzed Compustat data for Germany,the United Kingdom,France,Italy,and Spain for the 2006–2015 period,focusing on discretionary accruals.We considered three tax avoidance measures,two based on the effective tax rate(ETR)and one on book-tax differences(BTD).Our results indicate the presence of nonlinear patterns and a posi-tive,statistically significant relationship between discretionary accruals and both ETR indicators implying that when companies resort to earnings management,a larger tax-able income—and thus higher ETR and lesser tax avoidance–would ensue.Hence,as also highlighted by the fact that discretionary accruals do not appear to affect BTD,our evidence does not suggest that companies are exploiting tax manipulation to reduce their tax payments;thus,the gap between accounting and taxation seems largely unaf-fected by earnings management.

Research on Obstacle Avoidance Method of Intelligent Car Based on Optimized Fuzzy Control Algorithm

In order to realize the accurate obstacle avoidance function of intelligent car, we propose an intelligent car obstacle avoidance system based on optimized fuzzy control algorithm. Firstly, the kinematics model of intelligent car obstacle avoidance is established, and an efficient environment information collection system composed of multiple sensors is designed to realize the comprehensive collection of obstacle information. Then, the optimized fuzzy control system is adopted to improve the position control accuracy and obstacle avoidance ability. Through the physical debugging and joint simulation of the intelligent car fuzzy controller in the MATLAB and Simulink environment, the simulation results show that the control method can make the collision-free path planned by the intelligent car from the initial state to the obstacle avoidance smoother, and at the same time, the obstacle avoidance of the intelligent car. The actual running distance is reduced by about 16%, which can ensure the practicability of the obstacle avoidance system, provide a new guarantee for the safe operation of the car, and also provide a new idea for the development of the unmanned car.

Driver Intent Prediction and Collision Avoidance With Barrier Functions

For autonomous vehicles and driver assist systems,path planning and collision avoidance algorithms benefit from accurate predictions of future location of other vehicles and intent of their drivers.In the literature,the algorithms that provide driver intent belong to two categories:those that use physics based models with some type of filtering,and machine learning based approaches.In this paper we employ barrier functions(BF)to decide driver intent.BFs are typically used to prove safety by establishing forward invariance of an admissible set.Here,we decide if the“target”vehicle is violating one or more possibly fictitious(i.e.,non-physical)barrier constraints determined based on the context provided by the road geometry.The algorithm has a very small computational footprint and better false positive and negative rates than some of the alternatives.The predicted intent is then used by a control barrier function(CBF)based collision avoidance system to prevent unnecessary interventions,for either an autonomous or human-driven vehicle.

Cost-effective distributed FTFC for uncertain nonholonomic mobile robot fleet with collision avoidance and connectivity preservation

In this paper,the fault-tolerant formation control(FTFC)problem is investigated for a group of uncertain nonholonomic mobile robots with limited communication ranges and unpredicted actuator faults,where the communication between the robots is in a directed one-to-one way.In order to guarantee the connectivity preservation and collision avoidance among the robots,some properly chosen performance functions are incorporated into the controller to per-assign the asymmetrical bounds for relative distance and bearing angle between each pair of adjacent mobile robots.Particularly,the resultant control scheme remains at a costeffective level because its design does not use any velocity information from neighbors,any prior knowledge of system nonlinearities or any nonlinear approximator to account for them despite the presence of modeling uncertainties,unknown external disturbances,and unexpected actuator faults.Meanwhile,each follower is derived to track the leader with the tracking errors regarding relative distance and bearing angle subject to prescribed transient and steady-state performance guarantees,respectively.Moreover,all the closed-loop signals are ensured to be ultimately uniformly bounded.Finally,a numerical example is simulated to verify the effectiveness of this methodology.

AGV路径规划及避障算法研究综述

自动导向车(Automated Guided Vehicle,AGV)目前被广泛应用于物流、仓储、制造业和仓储等领域.AGV的路径规划和避障算法是实现AGV自主导航的关键技术,决定了AGV在复杂环境中能否高效、安全地完成任务,近年来成为AGV领域的重要研究热点之一.本文根据AGV路径规划及避障算法的原理与特点,将主流AGV路径规划及避障算法划分为局部避障路径规划算法、基于几何模型的路径规划算法、智能路径规划算法和混合算法4类,对算法的原理、工作流程、优缺点进行了深入分析,并介绍了相应的改进算法.最后,本文对AGV路径规划及避障的未来发展趋势进行展望,为AGV路径规划及避障算法的研究指出了方向.

Summarizing vehicle driving decision-making methods on vulnerable road user collision avoidance

With the development of intelligent vehicles and autonomous driving technology,the safety of vulnerable road user(VRU)in traffic has been more guaranteed,and many research achievements have been made in the key area of collision avoidance decision-making methods.In this paper,the knowledge mapping method is used to mine the available literature in depth,and it is found that the research focus has shifted from the traditional accident cause analysis to emerging deep learning and virtual reality technology.This paper summarizes research on the three core dimensions of environmental perception,behavior cognition and collision avoidance decision-making in intelligent vehicle systems.In terms of perception,accurate identification of pedestrians and cyclists in complex environments is a major demand for VRU perception;in terms of behavior cognition,the coupling of VRU intention identification and motion trajectory prediction and other multiple factors needs further research;in terms of decision-making,the intention identification and trajectory prediction of collision objects are not included in the risk assessment model,and there is a lack of exploration specifically for cyclists'collision risk.On this basis,this paper provides guidance for the improvement of traffic safety of contemporary VRU under the conditions of intelligent and connected transportation.

采摘机器人机械手避障方法研究——基于遗传优化和模糊PID控制器

为了提高采摘机器人机械手移动的自动化程度,使其在移动过程中有效避开障碍物,将基于PID控制器的避障系统引入到了机械手的设计上,并采用遗传算法和模糊算法对控制器进行了优化,提高了机械手移动的准确性和平稳性。模拟采摘机器人机械手的作业环境,对机械手的避障功能进行了测试,结果表明:机械手可以成功躲避障碍物,且采用遗传和模糊算法优化的机械手具有更高的移动平稳性,对于提高果实采摘效率和质量具有重要的作用。

采摘机器人的路径规划系统动态性优化研究

为进一步改善采摘机器人的工作性能,提出以动态调控为主导的理念,针对整机的路径规划系统展开优化研究。以当前果园采摘机器人的通用性结构组成为前提,将云平台数据处理与路径规划核心算法有效融合后搭建动态控制模型,分别针对路径规划系统的硬件配置与软件控制进行合理设计,得到可应用于采摘实践且布局完整的路径规划系统。展开动态性优化下的采摘作业试验,结果表明:优化后采摘机器人路径规划系统的整体路径搜索率与路径平滑性得到明显提升,相对提升度分别为10.93%和9.71%,路径偏离率相对降低了50%左右,很好地优化了机器人的避障能力,满足系统稳定性需求,具有较高的实用价值。