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.

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.

UPF based autonomous navigation scheme for deep space probe

The autonomous "celestial navigation scheme" for deep space probe departing from the earth and the autonomous "optical navigation scheme" for encountering object celestial body are presented. Then, aiming at the conditions that large initial estimation errors and non-Gaussian distribution of state or measurement errors may exist in orbit determination process of the two phases, UPF (unscented particle filter) is introduced into the navigation schemes. By tackling nonlinear and non-Gaussian problems, UPF overcomes the accuracy influence brought by the traditional EKF (extended Kalman filter), UKF (unscented Kalman filter), and PF (particle filter) schemes in approximate treatment to nonlinear and non-Gaussian state model and measurement model. The numerical simulations demonstrate the feasibility and higher accuracy of the UPF navigation scheme.

Autonomous Navigation for Unmanned Aerial Vehicles Based on Chaotic Bionics Theory

In this paper a new reactive mechanism based on perception-action bionics for multi-sensory integration applied to Un- manned Aerial Vehicles (UAVs) navigation is proposed.The strategy is inspired by the olfactory bulb neural activity observed in rabbits subject to external stimuli.The new UAV navigation technique exploits the use of a multiscroll chaotic system which is able to be controlled in real-time towards less complex orbits,like periodic orbits or equilibrium points,considered as perceptive orbits.These are subject to real-time modifications on the basis of environment changes acquired through a Synthetic Aperture Radar (SAR) sensory system.The mathematical details of the approach are given including simulation results in a virtual en- vironment.The results demonstrate the capability of autonomous navigation for UAV based on chaotic bionics theory in com- plex spatial environments.

A Technical Framework for Selection of Autonomous UAV Navigation Technologies and Sensors

The autonomous navigation of an Unmanned Aerial Vehicle(UAV)relies heavily on the navigation sensors.The UAV’s level of autonomy depends upon the various navigation systems,such as state measurement,mapping,and obstacle avoidance.Selecting the correct components is a critical part of the design process.However,this can be a particularly difficult task,especially for novices as there are several technologies and components available on the market,each with their own individual advantages and disadvantages.For example,satellite-based navigation components should be avoided when designing indoor UAVs.Incorporating them in the design brings no added value to the final product and will simply lead to increased cost and power consumption.Another issue is the number of vendors on the market,each trying to sell their hardware solutions which often incorporate similar technologies.The aim of this paper is to serve as a guide,proposing various methods to support the selection of fit-for-purpose technologies and components whilst avoiding system layout conflicts.The paper presents a study of the various navigation technologies and supports engineers in the selection of specific hardware solutions based on given requirements.The selection methods are based on easy-to-follow flow charts.A comparison of the various hardware components specifications is also included as part of this work.

New autonomous celestial navigation method for lunar satellite

Celestial navigation system is an important autonomous navigation system widely used for deep space exploration missions, in which extended Kalman filter and the measurement of angle between celestial bodies are used to estimate the position and velocity of explorer. In a conventional cartesian coordinate, this navigation system can not be used to achieve accurate determination of position for linearization errors of nonlinear spacecraft motion equation. A new autonomous celestial navigation method has been proposed for lunar satellite using classical orbital parameters. The error of linearizafion is reduced because orbit parameters change much more slowly than the position and velocity used in the cartesian coordinate. Simulations were made with both the cartesiane system and a system based on classical orbital parameters using extended Kalman filter under the same conditions for comparison. The results of comparison demonstrated high precision position determination of lunar satellite using this new method.

Artificial Pheromone System Using RFID for Navigation of Autonomous Robots

Navigation system based on the animal behavior has received a growing attention in the past few years. The navigation systems using artificial pheromone are still few so far. For this reason, this paper presents our research that aim to implement autonomous navigation with artificial pheromone system. By introducing artificial pheromone system composed of data carriers and autonomous robots, the robotic system creates a potential field to navigate their group. We have developed a pheromone density model to realize the function of pheromones with the help of data carders. We intend to show the effectiveness of the proposed system by performing simulations and realization using modified mobile robot. The pheromone potential field system can be used for navigation of autonomous robots.

Research on Autonomous Orbit Determination of Navigation Satellite Based on Crosslink Range and Orientation Parameters Constraining

Autonomous navigation of navigation satellite is discussed. The method of auto-orbit determination using the erosslink range and orientation parameters constraining is put forward. On the basis of the analysis of its feasibility, some useful conclusions are given.

Hybrid Navigation System Based Autonomous Positioning and Path Planning for Mobile Robots

Positioning and navigation technology is a new trend of research in mobile robot area.Existing researches focus on the indoor industrial problems,while many application fields are in the outdoor environment,which put forward higher requirements for sensor selection and navigation scheme.In this paper,a complete hybrid navigation system for a class of mobile robots with load tasks and docking tasks is presented.The work can realize large-range autonomous positioning and path planning for mobile robots in unstructured scenarios.The autonomous positioning is achieved by adopting suitable guidance methods to meet different application requirements and accuracy requirements in conditions of different distances.Based on the Bezier curve,a path planning scheme is proposed and a motion controller is designed to make the mobile robot follow the target path.The Kalman filter is established to process the guidance signals and control outputs of the motion controller.Finally,the autonomous positioning and docking experiment are carried out.The results of the research verify the effectiveness of the hybrid navigation,which can be used in autonomous warehousing logistics and multi-mobile robot system.

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.

Autonomous optical navigation for interplanetary exploration based on information of earth-moon

The image elements of earth-center and moon-center are obtained by processing the images of earthand moon, these image elements in combination with the inertial attitude information and the moon ephemerisare utilized to obtain the probe initial position relative to earth, and the Levenberg-Marquardt algorithm is usedto determine the accurate probe position relative to earth, and the probe orbit relative to earth is estimated by u-sing the extended Kalman filter. The autonomous optical navigation algorithm is validated using the digital simu-lation.

GNSS autonomous navigation method for HEO spacecraft

For global navigation satellite system(GNSS)in the application of high earth orbit(HEO)determination,there are problems such as small number of visible satellites and weak signal magnitude.The transmitting and receiving errors of GNSS signal in the environment of HEO space are analyzed,and related compensating scheme is also proposed.Acquisition of GNSS signal is implemented by using weak signal acquisition technology based on Duffing.Precise tracking of weak GNSS signal is also realized by adopting dynamic detection and compensation technology based on Duffing chaotic oscillator.Simulation results show that,certain acquisition sensitivity and navigation precision can be reached,and the acquisition and tracking of weak GNSS signal can be realized by using the proposed technology,which provides good technology support for autonomous navigation of HEO and large elliptical spacecrafts.

Autonomous optical navigation of Mars probe aided by one-way Doppler measurements in capture stage

The optical navigation errors of Mars probe in the capture stage depend closely on which targets are selected to be observed in the Mars system.As for this problem,an integrated navigation scheme is proposed wherein the optical observation is aided by one-way Doppler measurements.The errors are then analyzed respectively for the optical observation and one-way Doppler measurements.The real-time calculating scheme which exploits the extended Kalman filter(EKF)framework is designed for the integrated navigation.The simulation tests demonstrate that the errors of optical navigation,which select the Mars moon as the observation target,are relatively smaller than those in the Mars-orientation optical navigation case.On one hand,the integrated navigation errors do not depend on the selecting pattern of optical observation targets.On the other hand,the integrated navigation errors are significantly reduced as compared with those in the optical-alone autonomous navigation mode.

Applying of Switch Strong Tracking UKF in Spacecraft Autonomous Celestial Navigation

Star & Horizon sensor based autonomous navigation methods play an increasingly important role in spacecraft celestial navigation. However,the measurements of star sensors and horizon sensor are frequently affected by uncertain noises from space environment. To improve the estimation precision,a state estimation algorithm named Switch Strong Tracking Unscented Kalman Filter( SSTUKF) is presented. Firstly,the adaptive fading factor is deduced through the adoption of unknown instrumental diagonal matrixes to real time rectify the measurement covariance matrix. Secondly,according to the deduction of Chebyshev law of large numbers,innovation criterion is introduced during estimation to decrease the unnecessary calculation. Finally,SSTUKF is suggested through the adoption of adaptive fading factor and innovation criterion. The filter can switch between the normal filter mode and adaptive filter mode. As the calculation of innovation criterion is less than the adaptive fading factor,SSTUKF improves the estimation efficiency. To demonstrate the effectiveness,SSTUKF is applied to Star & Horizon sensor based autonomous navigation system with uncertain measurement noises. The simulation results verify the proposed algorithm.

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.

Study on autonomous satellite navigation from SHAR observations

This paper uses two navigation schemes to prove the potential of a novel autonomous orbit determination with stellar horizon atmospheric refraction measurements. Scheme one needs a single processor and uses an extended Kalman filter. The second scheme needs two parallel processors. One processor uses a hatched leastsquare initial state estimator and a high-precision dynamic state propagator. The other processor uses a real-time orbit predictor. Simulations have been executed respectively for three types （low/medial/high） of satellite orbits on which various numbers of stars are observed. The results show both schemes can autonomously determine the orbits with a considerable performance. The second scheme in general performs a little better than the first scheme.

An Autonomous Navigation Methodology for a Pioneer 3DX Robot

Autonomous navigation is a complex challenge that involves the interpretation and analysis of information about the scenario to facilitate the cognitive processes of a robot to perform free trajectories in dynamic environments. To solve this, the paper introduces a Case-Based Reasoning methodology to endow robots with an efficient decision structure aiming of selecting the best maneuver to avoid collisions. In particular, Manhattan Distance was implemented to perform the retrieval process in CBR method. Four scenarios were depicted to run a set of experiments in order to validate the functionality of the implemented work. Finally, conclusions emphasize the advantages of CBR methodology to perform autonomous navigation in unknown and uncertain environments.

Software framework for off-road autonomous robot navigation system

This paper presents a software framework for off-road autonomous robot navigation system.With the requirements of accurate terrain perception and instantaneous obstacles detection,one navigation software framework was advanced based on the principles of "three layer architecture" of intelligence system.Utilized the technologies of distributed system,machine learning and multiple sensor fusion,individual functional module was discussed.This paper aims to provide a framework reference for autonomous robot navigation system design.

Mobile Robot Indoor Autonomous Navigation with Position Estimation Using RF Signal Triangulation

In the mobile robotic systems a precise estimate of the robot pose (Cartesian [x y] position plus orientation angle theta) with the intention of the path planning optimization is essential for the correct performance, on the part of the robots, for tasks that are destined to it, especially when intention is for mobile robot autonomous navigation. This work uses a ToF (Time-of-Flight) of the RF digital signal interacting with beacons for computational triangulation in the way to provide a pose estimative at bi-dimensional indoor environment, where GPS system is out of range. It’s a new technology utilization making good use of old ultrasonic ToF methodology that takes advantage of high performance multicore DSP processors to calculate ToF of the order about ns. Sensors data like odometry, compass and the result of triangulation Cartesian estimative, are fused in a Kalman filter in the way to perform optimal estimation and correct robot pose. A mobile robot platform with differential drive and nonholonomic constraints is used as base for state space, plants and measurements models that are used in the simulations and for validation the experiments.

基于LIO-SAM建图和激光视觉融合定位的温室自主行走系统

为解决传统导航方案在温室内无法应对光照变化大、作物行间距窄、接收GPS信号差等问题,该研究提出了基于即时定位与地图构建技术的激光视觉融合式自主导航算法。该系统利用三维激光雷达VLP-16(Velodyne LiDAR,VLP-16)和惯性测量单元获取温室环境信息,采用基于紧耦合的雷达惯导定位建图(tightly-coupled lidar inertial odometry via smoothing and mapping,LIO-SAM)算法构建导航地图,基于轮式里程计和视觉里程计采用扩展卡尔曼滤波器算法实现局部定位,融合激光点云配准算法和自适应蒙特卡洛定位算法实现全局定位。同时,在自主行走系统应用A*算法规划全局路径和动态窗口算法规划局部路径,从而实现自主导航。试验结果表明,LIO-SAM算法构建的温室导航地图最大相对误差、最大绝对误差和均方根误差分别为9.9%、0.081和0.063 m,在温室内改进后的定位算法横向偏差小于0.020 m,纵向偏差小于0.090 m;当自主行走系统以0.15、0.30和0.50 m/s的速度运行时,横向偏差、纵向偏差和航向偏角的平均值分别小于0.120 m、0.10 m和8.5°,标准差分别小于0.070 m、0.140 m和6.6°。该导航方案满足自主行走系统在温室内高精度建图、定位和导航的需求,可为自主移动平台提供理论与技术支撑。