Development of autonomous navigation system for rice transplanter

Rice transplanting requires the operator to manipulate the rice transplanter in straight trajectories.Various markers are proposed to help experienced drivers in keeping straightforward and parallel to the previous path,which are extremely boring in terms of large-scale fields.The objective of this research was to develop an autonomous navigation system that automatically guided a rice transplanter working along predetermined paths in the field.The rice transplanter used in this research was commercially available and originally manually-operated.An automatic manipulating system was developed instead of manual functions including steering,stop,going forward and reverse.A sensor fusion algorithm was adopted to integrate measurements of the Real-Time Kinematic Global Navigation Satellite System(RTK-GNSS)and Inertial Measurement Unit(IMU),and calculate the absolute moving direction under the UTM coordinate system.A headland turning control method was proposed to ensure a robust turning process considering that the rice transplanter featured a small turning radius and a relatively large slip rate at extreme steering angles.Experiments were designed and conducted to verify the performance of the newly developed autonomous navigation system.Results showed that both lateral and heading errors were less than 8 cm and 3 degrees,respectively,in terms of following straight paths.And headland turns were robustly executed according to the required pattern.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Autonomous navigation method of satellite constellation based on adaptive forgetting factors

To address the problem that model uncertainty and unknown time-varying system noise hinder the filtering accuracy of the autonomous navigation system of satellite constellation,an autonomous navigation method of satellite constellation based on the Unscented Kalman Filter with Adaptive Forgetting Factors(UKF-AFF)is proposed.The process noise covariance matrix is estimated online with the strategy that combines covariance matching and adaptive adjustment of forgetting factors.The adaptive adjustment coefficient based on squared Mahalanobis distance of state residual is employed to achieve online regulation of forgetting factors,equipping this method with more adaptability.The intersatellite direction vector obtained from photographic observations is introduced to determine the constellation satellite orbit together with the distance measurement to avoid rank deficiency issues.Considering that the number of available measurements varies online with intersatellite visibility in practical applications such as time-varying constellation configurations,the smooth covariance matrix of state correction determined by innovation and gain is adopted and constructed recursively.Stability analysis of the proposed method is also conducted.The effectiveness of the proposed method is verified by the Monte Carlo simulation and comparison experiments.The estimation accuracy of constellation position and velocity of UKF-AFF is improved by 30%and 44%respectively compared to those of the extended Kalman filter,and the method proposed is also better than other several adaptive filtering methods in the presence of significant model uncertainty.

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.

A 3D Simulator for Autonomous Robotic Fish

This paper presents a 3D simulator used for studying the motion control and autonomous navigation of robotic fish. The simulator’s system structure and computation flow are presented. Simplified kinematics and hydrodynamics models for a virtual robotic fish are proposed. Many other object models are created for water, obstacles, sonar sensors and a swimming pool. Experimental results show that the simulator provides a realistic and convenient way to develop autonomous navigation algorithms for robotic fish.

Pulsar/CNS integrated navigation based on federated UKF

In order to improve the autonomous navigation capability of satellite,a pulsar/CNS（celestial navigation system） integrated navigation method based on federated unscented Kalman filter（UKF） is proposed.The celestial navigation is a mature and stable navigation method.However,its position determination performance is not satisfied due to the low accuracy of horizon sensor.Single pulsar navigation is a new navigation method,which can provide highly accurate range measurements.The major drawback of single pulsar navigation is that the system is completely unobservable.As two methods are complementary to each other,the federated UKF is used here for fusing the navigation data from single pulsar navigation and CNS.Compared to the traditional celestial navigation method and single pulsar navigation,the integrated navigation method can provide better navigation performance.The simulation results demonstrate the feasibility and effectiveness of the navigation method.

Fault tolerant navigation method for satellite based on information fusion and unscented Kalman filter

An effective autonomous navigation system for the integration of star sensor,infrared horizon sensor,magnetometer,radar altimeter and ultraviolet sensor is developed.The requirements of the integrated navigation system manager make optimum use of the various navigation sensors and allow rapid fault detection,isolation and recovery.The normal full fusion feedback method of federated unscented Kalman filter（UKF） cannot meet the needs of it.So a no-reset feedback federated Kalman filter architecture is developed and used in the autonomous navigation system.The minimal skew sigma points are chosen to improve the calculation speed.Simulation results are presented to demonstrate the advantages of the algorithm.These advantages include improved failure detection and correction,improved computational efficiency,and reliability.Additionally,its＇ accuracy is higher than that of the full fusion feedback method.

Dynamic A^*path finding algorithm and 3D lidar based obstacle avoidance strategy for autonomous vehicles

This paper presents a novel dynamic A^*path finding algorithm and 3D lidar based local obstacle avoidance strategy for an autonomous vehicle.3D point cloud data is collected and analyzed in real time.Local obstacles are detected online and a 2D local obstacle grid map is constructed at 10 Hz/s.The A^*path finding algorithm is employed to generate a local path in this local obstacle grid map by considering both the target position and obstacles.The vehicle avoids obstacles under the guidance of the generated local path.Experiment results have shown the effectiveness of the obstacle avoidance navigation algorithm proposed.

Novel algorithm for geomagnetic navigation

To solve the highly nonlinear and non-Gaussian recursive state estimation problem in geomagnetic navigation, the unscented particle filter （UPF） was introduced to navigation system. The simulation indicates that geomagnetic navigation using UPF could complete the position estimation with large initial horizontal position errors. However, this navigation system could only provide the position information. To provide all the kinematics states estimation of aircraft, a novel autonomous navigation algorithm, named unscented particle and Kalman hybrid navigation algorithm （UPKHNA）, was proposed for geomagnetic navigation, The UPKHNA used the output of UPF and barometric altimeter as position measurement, and employed the Kahnan filter to estimate the kinematics states of aircraft. The simulation shows that geomagnetic navigation using UPKHNA could provide all the kinematics states estimation of aircraft continuously, and the horizontal positioning performance is better than that only using the UPF.

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.