Bio-inspired geomagnetic navigation method for autonomous underwater vehicle

This paper presents a bio-inspired geomagnetic navigation method for autonomous underwater vehicle(AUV) without using any a priori geomagnetic information. Firstly, the multi-objective search problem is raised. Secondly, the geomagnetic navigation model is established by constructing a cost function. Then, by taking into consideration the biological magneto-taxis movement behavior for the geomagnetic environment stimulus, the multiobjective evolutionary search algorithm is derived to describe the search process. Finally, compared to the state-of-the-art, the proposed method presents better robustness. The simulation results demonstrate the reliability and feasibility of the proposed method.

Cooperative Navigation for Autonomous Underwater Vehicles Based on Estimation of Motion Radius Vectors

A cooperative navigation algorithm for a group of autonomous underwater vehicles is proposed on the basis of motion radius vector estimation.Combined the dead reckoning data with the mutual range data through an acoustic communication network among the group members, the relative positioning problem can be solved. A novel approach for solving the relative positioning is presented by using a recursive trigonometry technique and extended Kalman filter(EKF). Simulation results verify the correctness and effectiveness of this navigation method.

Simulation platform of navigation system for autonomous underwater vehicle

In view of the characteristics of underwater navigation, the simulation platform of navigation system for autonomous underwater vehicle has been developed based on Windows platform. The system architecture, net communication and the information flow are discussed. The methods of software realization and some key techniques of the Vehicle Computer and the Navigation Equipment Computer are introduced in particular. The software design of Terrain Matching Computer is introduced also. The simulation platform is verified and analyzed through simulation. The results show that the architecture of the platform is reasonable and reliable, and the mathematic models and simulation algorithms of sub-systems are also valid and practicable.

UAV Autonomous Navigation for Wireless Powered Data Collection with Onboard Deep Q-Network

In a rechargeable wireless sensor network,utilizing the unmanned aerial vehicle(UAV)as a mobile base station(BS)to charge sensors and collect data effectively prolongs the network’s lifetime.In this paper,we jointly optimize the UAV’s flight trajectory and the sensor selection and operation modes to maximize the average data traffic of all sensors within a wireless sensor network(WSN)during finite UAV’s flight time,while ensuring the energy required for each sensor by wireless power transfer(WPT).We consider a practical scenario,where the UAV has no prior knowledge of sensor locations.The UAV performs autonomous navigation based on the status information obtained within the coverage area,which is modeled as a Markov decision process(MDP).The deep Q-network(DQN)is employed to execute the navigation based on the UAV position,the battery level state,channel conditions and current data traffic of sensors within the UAV’s coverage area.Our simulation results demonstrate that the DQN algorithm significantly improves the network performance in terms of the average data traffic and trajectory design.

Navigation for autonomous vehicles via fast-stable and smooth reinforcement learning

This paper investigates the navigation problem of autonomous vehicles based on reinforcement learning(RL)with both stability and smoothness guarantees.By introducing a data-based Lyapunov function,the stability criterion in mean cost is obtained,where the Lyapunov function has a property of fast descending.Then,an off-policy RL algorithm is proposed to train safe policies,in which a more strict constraint is exerted in the framework of model-free RL to ensure the fast convergence of policy generation,in contrast with the existing RL merely with stability guarantee.In addition,by simultaneously introducing constraints on action increments and action distribution variations,the difference between the adjacent actions is effectively alleviated to ensure the smoothness of the obtained policy,instead of only seeking the similarity of the distributions of adjacent actions as commonly done in the past literature.A navigation task of a ground differentially driven mobile vehicle in simulations is adopted to demonstrate the superiority of the proposed algorithm on the fast stability and smoothness.

Autonomous vehicles: challenges, opportunities, and future implications for transportation policies

This study investigates the challenges and opportunities pertaining to transportation policies that may arise as a result of emerging autonomous vehicle （AV） technologies. AV technologies can decrease the transportation cost and increase accessibility to low-income households and persons with mobility issues. This emerging technology also has far-reaching applications and implications beyond all current expectations. This paper provides a comprehensive review of the relevant literature and explores a broad spectrum of issues from safety to machine ethics. An indispensable part of a prospective AV development is communication over cars and infrastructure （connected vehicles）. A major knowledge gap exists in AV technology with respect to routing behaviors. Connected- vehicle technology provides a great opportunity to imple- ment an efficient and intelligent routing system. To this end, we propose a conceptual navigation model based on a fleet of AVs that are centrally dispatched over a network seeking system optimization literature on two fronts： （i） This study contributes to the it attempts to shed light on future opportunities as well as possible hurdles associated with AV technology; and （ii） it conceptualizes a navigation model for the AV which leads to highly efficient traffic circulations.

Research on High Precision Autonomous Navigation of Shared Balancing Vehicles Based on EKF-SLAM

In view of the technical difficulties of autonomous navigation in local areas,this paper proposes a high-precision autonomous navigation shared bal-ancing bike system based on EKF-SLAM.This system uses the EKF-SLAM algorithm in robot localization to achieve simultaneous localization and map con-struction using the extended Kalmanfilter.At the same time,GPS and IMU are also employed for absolute positioning,and point cloud matching is used for rel-ative positioning to achieve multisensor fusion positioning.For the convenience of users,this system uses the RNN-T model for speech recognition destinations.Through experimental verification,the EKF-SLAM-based autonomous naviga-tion technology proposed in this paper can meet the accurate localization service and can realize the function of high precision autonomous navigation and voice recognition of destinations for shared balancing vehicles in a local area.

Deep-reinforcement-learning-based UAV autonomous navigation and collision avoidance in unknown environments

In some military application scenarios,Unmanned Aerial Vehicles(UAVs)need to perform missions with the assistance of on-board cameras when radar is not available and communication is interrupted,which brings challenges for UAV autonomous navigation and collision avoidance.In this paper,an improved deep-reinforcement-learning algorithm,Deep Q-Network with a Faster R-CNN model and a Data Deposit Mechanism(FRDDM-DQN),is proposed.A Faster R-CNN model(FR)is introduced and optimized to obtain the ability to extract obstacle information from images,and a new replay memory Data Deposit Mechanism(DDM)is designed to train an agent with a better performance.During training,a two-part training approach is used to reduce the time spent on training as well as retraining when the scenario changes.In order to verify the performance of the proposed method,a series of experiments,including training experiments,test experiments,and typical episodes experiments,is conducted in a 3D simulation environment.Experimental results show that the agent trained by the proposed FRDDM-DQN has the ability to navigate autonomously and avoid collisions,and performs better compared to the FRDQN,FR-DDQN,FR-Dueling DQN,YOLO-based YDDM-DQN,and original FR outputbased FR-ODQN.

Development of autonomous navigation controller for agricultural vehicles

Agricultural vehicles are adopted to undertake farming tasks by traversing along crop rows in the field.Working quality depends significantly on the driving skills of the operator.Automatic guidance has been introduced into agriculture to achieve high-accuracy path tracking during the last decades,which contributes considerably to straight-line navigation.The objective of this research was to develop an autonomous navigation controller that allowed movement autonomy for various agricultural vehicles.Three wheel-type vehicles were used as the test platform featuring automatic steering,hydrostatic transmission and speed control,which included a rice transplanter,a high-clearance sprayer and a tractor.A dual-antenna RTK-GNSS receiver was attached to the vehicles to provide spatial information on both positioning and heading by using the RTX service from Trimble.A path planning method was proposed to create a straight-line reference path by giving two points,and the target path was determined according to the vehicle initial status and working assignment.Headland turning was comprehensively taken into account by listing different turn patterns in order to realize autonomous navigation at the headland.The navigation controller hardware was fabricated for program execution,data processing and information communication with peripherals.A human-machine interface was designed for the operator to complete basic setting,path planning and navigation control by providing controls.Field experiments were conducted to evaluate the performance and versatility of the newly developed autonomous navigation controller in guiding agricultural vehicles to follow straight paths and turn at the headland.Results showed that an appropriate turn pattern was automatically executed when finishing straight-line navigation.The lateral error in straight-line tracking was no more than 6 cm,6 cm and 5 cm for the rice transplanter,the high-clearance sprayer and the tractor,respectively.And the maximum lateral RMS error was 3.10 cm,4.75 cm,2.21 cm in terms of straight-line tracking,which indicated that the newly developed autonomous navigation controller was versatile and of high robustness in guiding various agricultural vehicles.

Integrated navigation for autonomous underwater vehicles in aquaculture: A review

Aquaculture is the world’s fastest growing sector within the food industry,supplying humans with over half their aquatic products.Water quality monitoring or cage inspection is an indispensable part in aquaculture and is usually done manually.Autonomous underwater vehicles(AUVs)are increasingly being used in aquaculture as technology advances and the cost reduction.Autonomous navigation is considered as a basic function of AUVs but is a challenging issue primarily due to the attenuated nature of electromagnetic waves in water and unstructured underwater environments.An inertial navigation system(INS)is usually selected as the core navigation equipment for AUV navigation because it never fails to measure.This paper reviews and surveys the latest advances in integrated navigation technologies for AUVs and provides a comprehensive reference for researchers who intend to apply AUVs to autonomous monitoring of aquaculture.Pure INS has difficulty obtaining long-range precision navigation due to the inherent error accumulation of inertial sensors over time;aiding inertial navigation systems with auxiliary sensors are common means to improve the navigation accuracy of an INS for AUVs.The survey is conducted according to different assisted navigation technologies for inertial navigation.Finally,the future challenges of the AUV navigation are also presented.

Safe Navigation for UAV-Enabled Data Dissemination by Deep Reinforcement Learning in Unknown Environments

Unmanned aerial vehicles(UAVs) are increasingly considered in safe autonomous navigation systems to explore unknown environments where UAVs are equipped with multiple sensors to perceive the surroundings. However, how to achieve UAVenabled data dissemination and also ensure safe navigation synchronously is a new challenge. In this paper, our goal is minimizing the whole weighted sum of the UAV’s task completion time while satisfying the data transmission task requirement and the UAV’s feasible flight region constraints. However, it is unable to be solved via standard optimization methods mainly on account of lacking a tractable and accurate system model in practice. To overcome this tough issue,we propose a new solution approach by utilizing the most advanced dueling double deep Q network(dueling DDQN) with multi-step learning. Specifically, to improve the algorithm, the extra labels are added to the primitive states. Simulation results indicate the validity and performance superiority of the proposed algorithm under different data thresholds compared with two other benchmarks.

Underwater Terrain-Aided Navigation Based on Multibeam Bathymetric Sonar Images

在水下帮助地面的航行被用来补充采用由的传统的惯性的航行自治在水下车辆在过长的使命期间。它能由与一张数字地面地图匹配即时深度数据提供固定评价。这研究介绍使用处理技术在的图象的概念在水下地面匹配过程。一幅图象的一张传统的灰阶的直方图被加入在象素与空间信息充实。边角落象素然后被定义并且过去常构造一张边角落直方图，扫描数字地面地图并且由寻找关联山峰估计车辆的改正作为一个模板采用它。模拟被执行调查建议方法的坚韧性，特别地在与它到背景噪音，即时图象的规模，和车辆的旅行方向的敏感的关系。在 1 m 2/pixel, 的一个图象决定，本地化的精确性是超过 10 米。

Multi-Scale Fusion Algorithm for AUVs Integrated Navigation Systems

To deal with the low location accuracy issue of existing underwater navigation technologies in autonomous underwater vehicles(AUVs),a distributed fusion algorithm which combines the model's analysis method with a multi-scale transformation method is proposed for integrated navigation system based on AUV.First,integrated navigation system theory and system error sources are introduced in details.Secondly,a navigation system's observation equation on the original scale is decomposed into different scales by the discrete wavelet transform method,and noise reduction is performed by setting the wavelet de-noising threshold.At last,the dynamic equation and observation equations are fused on different scales by the wavelet transformation and Kalman filter.The results show that the proposed algorithm has smaller navigation error and higher navigation accuracy.

Research on Visual Autonomous Navigation Indoor for Unmanned Aerial Vehicle

The aim of this paper is to study visual autonomous navigation of unmanned aerial vehicle(UAV) in indoor global positioning system(GPS) denied environment. The UAV platform of the autonomous navigation flight control system is designed and built. The principle of visual localization and mapping algorithm is studied.According to the characteristics of UAV platform, the visual localization is designed and improved. Experimental results demonstrate that the UAV platform can realize the tasks of autonomous localization, navigation and mapping based on visual in unknown environments.

Correction of Inertial Navigation System’s Errors by the Help of Video-Based Navigator Based on Digital Terrarium Map

This paper deals with the error analysis of a novel navigation algorithm that uses as input the sequence of images acquired from a moving camera and a Digital Terrain (or Elevation) Map (DTM/DEM). More specifically, it has been shown that the optical flow derived from two consecutive camera frames can be used in combination with a DTM to estimate the position, orientation and ego-motion parameters of the moving camera. As opposed to previous works, the proposed approach does not require an intermediate explicit reconstruction of the 3D world. In the present work the sensitivity of the algorithm outlined above is studied. The main sources for errors are identified to be the optical-flow evaluation and computation, the quality of the information about the terrain, the structure of the observed terrain and the trajectory of the camera. By assuming appropriate characterization of these error sources, a closed form expression for the uncertainty of the pose and motion of the camera is first developed and then the influence of these factors is confirmed using extensive numerical simulations. The main conclusion of this paper is to establish that the proposed navigation algorithm generates accurate estimates for reasonable scenarios and error sources, and thus can be effectively used as part of a navigation system of autonomous vehicles.

基于SAC的无人机自主导航方法研究

针对现有深度强化学习算法在无人机自主导航任务中面临环境局部可观且感知信息不足问题,基于非确定性策略SAC(soft actor-critic)强化学习算法对未知环境下的端到端无人机自主导航任务展开研究。具体而言,提出了一种基于记忆增强机制的策略网络,通过对历史记忆信息与当前的观测整合处理,提取观测数据的时序依赖关系,从而增强局部可观条件下的状态估计能力,避免算法陷入局部最优解;设计了非稀疏奖励函数以缓解强化学习策略在稀疏奖励条件下难以收敛的问题;在Airsim+UE4仿真平台进行了多个复杂场景的训练验证。实验结果表明,所提方法导航成功率比基准算法提高10%,平均飞行距离缩短21%,有效增强了无人机自主导航算法稳定性和收敛性。

面向果园车辆自主导航算法的仿真测试平台设计

为测试和评价果园车辆自主导航算法的性能,采用数学分析软件MATLAB搭建面向果园车辆自主导航算法的仿真测试平台。仿真测试平台通过构建果园种植模型、激光雷达(Light detection and ranging,LiDAR)测量模型、车辆运动学模型、车辆动力学模型和电机控制模型实现虚拟果园测量和车辆运动过程模拟,通过可视化测量数据、导航过程和导航偏差显示导航结果并评价导航算法性能。对单一模型和平台整体的性能测试结果表明,仿真测试平台可靠,针对基于2D LiDAR的导航路径生成和跟踪算法,仿真测试平台能够有效测试和评价不同果园种植参数及车辆初始状态下的算法性能,满足果园车辆自主导航算法的测试需求。

融合载体动力学特征的智能多源自主导航方法研究

多源自主导航系统技术是新一代国家综合定位、导航与授时(PNT)体系下终端用户的重要技术发展方向。面向新一代健壮性综合PNT体系跨场景的导航任务,多源自主导航系统面临着智能化的迫切需求。针对复杂导航行为决策可信性问题,提出了融合载体动力学特征的智能多源自主导航基本概念,归纳了其实践中面临的主要科学问题,系统阐述了深度融合载体动力学特征与人工智能方法的多源自主导航理论框架与方法内涵,为智能多源自主导航系统的理论、方法及应用提供支撑。

深海ARV在海洋资源调查中的应用及展望

深海自主/遥控式水下机器人(ARV)作为新兴的复合型水下机器人,融合了自主式水下机器人的灵活性和遥控式水下机器人的人机交互性等优势,开启了自主与遥控混合作业的新模式。深海ARV可切换为AUV自主航行模式,独立采集周边区域近海底地形地貌、地质结构以及环境参数等数据,也能下潜至目标区域后切换为ROV遥控模式进行局部观察和采样操作,其探测作业一体化技术代表了具备复杂使命执行力的第三代深海水下机器人的发展方向。通过分析国内外深海混合型水下机器人的发展现状,结合ARV在中国深海矿产资源调查中的主要应用案例和取得的成果,以6 000米级“问海一号”ARV系统的研发为例,给出其关键技术集成以及面对的技术挑战,并对未来深海ARV的应用场景、功能集成和发展方向提出了一些设想和建议。

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.