Vision Navigation Based PID Control for Line Tracking Robot

In a controlled indoor environment,line tracking has become the most practical and reliable navigation strategy for autonomous mobile robots.A line tracking robot is a self-mobile machine that can recognize and track a painted line on thefloor.In general,the path is set and can be visible,such as a black line on a white surface with high contrasting colors.The robot’s path is marked by a distinct line or track,which the robot follows to move.Several scientific contributions from the disciplines of vision and control have been made to mobile robot vision-based navigation.Localization,automated map generation,autonomous navigation and path tracking is all becoming more frequent in vision applications.A visual navigation line tracking robot should detect the line with a camera using an image processing technique.The paper focuses on combining computer vision techniques with a proportional-integral-derivative(PID)control-ler for automatic steering and speed control.A prototype line tracking robot is used to evaluate the proposed control strategy.

Ubiquitous indoor vision navigation using a smart device

GPS-based technology has served the positioning and navigation industry for decades with outstanding reliability and accuracy.However,limitations for location-based services in indoor scenarios remain where GPS signal reception suffers from severe degradation or even outages.Wi-Fi-based positioning is currently the most popular indoor location solution,with an availability and time-to-first-fix that are significantly better than GPS.However,the achievable position accuracy is only at the level of tens of meters on average depending on database density and signal reception quality.In order to improve positioning accuracy and availability,motion sensors like accelerometers,gyros,and magnetic compasses are increasingly integrated into smart devices.However,their position solutions suffer from the effects of significant accumulative errors.In this paper,a vision-based indoor positioning method is developed to overcome the limitations above.The proposed vision-based system relies on a single camera,widely available on smart phones and tablets.The derivation of the absolute 3D position from 2D snapshots of a single camera requires the use of an external geo-reference database.In this research,a ubiquitous floor plan database has been used to provide accurate geodetic information.Unlike other popular geo-reference databases,the database used in this work can easily be generated with existing resources.The proposed system has been developed as an iOS App and was tested on iPad for various indoor scenarios.The results show that the performance of the proposed system is superior to Wi-Fi-based positioning systems.

GPS/VISNAV integrated relative navigation and attitude determination system for ultra-close spacecraft formation flying

For the improvement of accuracy and better fault-tolerant performance, a global position system （GPS）/vision navigation （VISNAV） integrated relative navigation and attitude determination approach is presented for ultra-close spacecraft formation flying. Onboard GPS and VISNAV system are adopted and a federal Kalman filter architecture is used for the total navigation system design. Simulation results indicate that the integrated system can provide a total improvement of relative navigation and attitude estimation performance in accuracy and fault-tolerance.

Automatic navigation path detection method for tillage machines working on high crop stubble fields based on machine vision

Due to the influence of complex working environment and artificial factors,it is easy to cause crop up over or less tillage problem when straw returning machine is working in paddy field.A new method for path detection suitable for rice,rape and wheat high crop stubble tilling environments was proposed.First the distribution characteristics of rice,rape and wheat high crop stubble images in paddy field based on RGB color model were analyzed,and rice,the color images of rape and wheat high crop stubble were converted into gray ones using custom factor combination R+G-2B;Then,the gray images of rice,rape and wheat high crop stubble were segmented from soil background by means of luminance mean texture descriptor;Next,the binary image through custom shear-binary-image algorithm was cut to remove big noise blobs in high crop stubble’s tilled area;Finally,navigation path from navigation points by using the least square method was derived.The experimental results indicated that the navigation path detection algorithm was fast and effective to obtain navigation path in rice,rape and wheat high crop stubble tilling environments with up to 96.7% of segmentation accuracy within 0.6 s of processing time.

Absolute pose estimation of UAV based on large-scale satellite image

Obtaining absolute pose based on pre-loaded satellite images is one of the important means of autonomous navigation for small Unmanned Aerial Vehicles(UAVs)in Global Navigation Satellite System(GNSS)denied environments.Most of the previous works have tended to build Convolutional Neural Networks(CNNs)to extract features and then directly regress the pose,which will fail when solving the challenges caused by the huge viewpoint and size differences between“UAV-satellite”image pairs in real-world scenarios.Therefore,this paper proposes a probability distribution/regression integrated deep model with the attention-guided triple fusion mechanism,which estimates discrete distributions in pose space and three-dimensional vectors in translation space.In order to overcome the shortage of the relevant dataset,this paper simulates image datasets captured by UAVs with forward-facing cameras during target searching and autonomous attacking.The effectiveness,superiority,and robustness of the proposed method are verified by simulated datasets and flight tests.