scholarly journals A Compressed Sensing Approach for Multiple Obstacle Localisation Using Sonar Sensors in Air

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5511
Author(s):  
Eduardo Tondin Ferreira Dias ◽  
Hugo Vieira Neto ◽  
Fábio Kurt Schneider
Keyword(s):  
Compressed Sensing ◽  
Autonomous Navigation ◽  
Matching Pursuit ◽  
Obstacle Detection ◽  
Destructive Interference ◽  
Navigation Systems ◽  
Proper Mapping ◽  
Sonar Sensors ◽  
Reconstruction Methods ◽  
Multiple Obstacles

Methods for autonomous navigation systems using sonars in air traditionally use the time-of-flight technique for obstacle detection and environment mapping. However, this technique suffers from constructive and destructive interference of ultrasonic reflections from multiple obstacles in the environment, requiring several acquisitions for proper mapping. This paper presents a novel approach for obstacle detection and localisation using inverse problems and compressed sensing concepts. Experiments were conducted with multiple obstacles present in a controlled environment using a hardware platform with four transducers, which was specially designed for sending, receiving and acquiring raw ultrasonic signals. A comparison between the performance of compressed sensing using Orthogonal Matching Pursuit and two traditional image reconstruction methods was conducted. The reconstructed 2D images representing the cross-section of the sensed environment were quantitatively assessed, showing promising results for robotic mapping tasks using compressed sensing.

scholarly journals Autonomous navigation system with small four-wheel drive platform

2020 ◽  
Vol 166 ◽  
pp. 05004
Author(s):  
Martin Bogdanovskyi ◽  
Andrii Tkachuk ◽  
Oleksandr Dobrzhanskyi ◽  
Anna Humeniuk
Keyword(s):  
Autonomous Navigation ◽  
Hybrid Control ◽  
Vision System ◽  
Movement Control ◽  
Image Sensor ◽  
Obstacle Detection ◽  
Raspberry Pi ◽  
Navigation Systems ◽  
Wheel Drive ◽  
Four Wheel Drive

The task of achieving greater flexibility and maneuverability of small transport and service units’ motion in modern factories by developing small autonomous navigation systems plays crucial role in complex automation of transport logistics nowadays. To solve navigation task, it was proposed the following approach, where as a means of assessing the environment was used computer vision system based on 5-megapixel CMOS image sensor and for the front obstacle detection was used auxiliary ultrasonic sensor as a limit switch. Authors solved the problem of yawing using artificial marking approach as along two-colored leading lines. For maneuverability increase during the turn was used speed movement control based on lines perspective. The basic design and technical characteristics of the four-wheel drive platform and the algorithm of the Raspberry PI 3/Arduino Nano hybrid control system are presented. Experimental results proved the viability of the proposed approach.

Obstacle Avoidance and Navigation of Autonomous Mobile Robot

2011 ◽  
Vol 403-408 ◽  
pp. 4633-4642 ◽  
Author(s):  
Rekha Raja ◽  
S N. Shome ◽  
S. Nandy ◽  
R. Ray
Keyword(s):  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Autonomous Navigation ◽  
Fuzzy Logic Controller ◽  
Classical Method ◽  
Obstacle Detection ◽  
Autonomous Mobile Robot ◽  
Unstructured Environment ◽  
Multiple Obstacles ◽  
Robot Platform

This paper presents a hybrid obstacle avoidance methodology for autonomous navigation of a mobile robot in an unstructured environment. Decision is taken based on the classical method depending on the environmental scenario where the space between multiple obstacles is measured and the feasibility of passing the robot through any immediate pair of obstacles examined. In other cases, the decision is taken by the Fuzzy Logic controller. The developed algorithm is simulated and experimentally validated with a mobile robot platform equipped with forward-looking sonar for obstacle detection. Odometry sensors assist in localization of the mobile robot. The developed algorithm is found adequately intelligent to navigate the robot from any start position through to the desired goal position avoiding obstacles, and without taking recourse to any pre-built map. The simulated results exhibit fair agreement with the experimental results.

Geophysical Support of Magnetometer Autonomous Navigation Systems

2020 ◽  
Author(s):  
Vladimir T. Minligareev ◽  
Elena N. Khotenko ◽  
Vadim V. Tregubov ◽  
Tatyana V. Sazonova ◽  
Vaclav L. Kravchenok
Keyword(s):  
Autonomous Navigation ◽  
Navigation Systems

Compressed Sensing Image Reconstruction Based on Improved Particle Swarm Optimization Algorithm

2014 ◽  
Vol 599-601 ◽  
pp. 1453-1456
Author(s):  
Ju Wang ◽  
Yin Liu ◽  
Wei Juan Zhang ◽  
Kun Li
Keyword(s):  
Particle Swarm Optimization ◽  
Image Reconstruction ◽  
Compressed Sensing ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Matching Pursuit ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The reconstruction algorithm has a hot research in compressed sensing. Matching pursuit algorithm has a huge computational task, when particle swarm optimization has been put forth to find the best atom, but it due to the easy convergence to local minima, so the paper proposed a algorithm ,which based on improved particle swarm optimization. The algorithm referred above combines K-mean and particle swarm optimization algorithm. The algorithm not only effectively prevents the premature convergence, but also improves the K-mean’s local. These findings indicated that the algorithm overcomes premature convergence of particle swarm optimization, and improves the quality of image reconstruction.

scholarly journals Constrained Backtracking Matching Pursuit Algorithm for Image Reconstruction in Compressed Sensing

2021 ◽  
Vol 11 (4) ◽  
pp. 1435
Author(s):  
Xue Bi ◽  
Lu Leng ◽  
Cheonshik Kim ◽  
Xinwen Liu ◽  
Yajun Du ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Matching Pursuit ◽  
Reconstruction Algorithm ◽  
Energy Criterion ◽  
Step Size ◽  
Relationship Analysis ◽  
Support Set ◽  
Reconstruction Performance ◽  
Sparsity Level

Image reconstruction based on sparse constraints is an important research topic in compressed sensing. Sparsity adaptive matching pursuit (SAMP) is a greedy pursuit reconstruction algorithm, which reconstructs signals without prior information of the sparsity level and potentially presents better reconstruction performance than other greedy pursuit algorithms. However, SAMP still suffers from being sensitive to the step size selection at high sub-sampling ratios. To solve this problem, this paper proposes a constrained backtracking matching pursuit (CBMP) algorithm for image reconstruction. The composite strategy, including two kinds of constraints, effectively controls the increment of the estimated sparsity level at different stages and accurately estimates the true support set of images. Based on the relationship analysis between the signal and measurement, an energy criterion is also proposed as a constraint. At the same time, the four-to-one rule is improved as an extra constraint. Comprehensive experimental results demonstrate that the proposed CBMP yields better performance and further stability than other greedy pursuit algorithms for image reconstruction.

scholarly journals Time- and Resource-Efficient Time-to-Collision Forecasting for Indoor Pedestrian Obstacles Avoidance

2021 ◽  
Vol 7 (4) ◽  
pp. 61
Author(s):  
David Urban ◽  
Alice Caplier
Keyword(s):  
Neural Network ◽  
Autonomous Vehicles ◽  
Depth Estimation ◽  
Video Camera ◽  
Obstacle Detection ◽  
Navigation Systems ◽  
Time To Collision ◽  
Static Data ◽  
Monocular Depth ◽  
Fully Connected

As difficult vision-based tasks like object detection and monocular depth estimation are making their way in real-time applications and as more light weighted solutions for autonomous vehicles navigation systems are emerging, obstacle detection and collision prediction are two very challenging tasks for small embedded devices like drones. We propose a novel light weighted and time-efficient vision-based solution to predict Time-to-Collision from a monocular video camera embedded in a smartglasses device as a module of a navigation system for visually impaired pedestrians. It consists of two modules: a static data extractor made of a convolutional neural network to predict the obstacle position and distance and a dynamic data extractor that stacks the obstacle data from multiple frames and predicts the Time-to-Collision with a simple fully connected neural network. This paper focuses on the Time-to-Collision network’s ability to adapt to new sceneries with different types of obstacles with supervised learning.

scholarly journals A Dynamic Calibration Method of Installation Misalignment Angles between Two Inertial Navigation Systems

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2947
Author(s):  
Ming Hua ◽  
Kui Li ◽  
Yanhong Lv ◽  
Qi Wu
Keyword(s):  
Autonomous Navigation ◽  
Inertial Navigation ◽  
Calibration Method ◽  
Measurement Information ◽  
Dynamic Calibration ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Additional Information ◽  
Two Systems ◽  
Dynamic Calibration Method

Generally, in order to ensure the reliability of Navigation system, vehicles are usually equipped with two or more sets of inertial navigation systems (INSs). Fusion of navigation measurement information from different sets of INSs can improve the accuracy of autonomous navigation effectively. However, due to the existence of misalignment angles, the coordinate axes of different systems are usually not in coincidence with each other absolutely, which would lead to serious problems when integrating the attitudes information. Therefore, it is necessary to precisely calibrate and compensate the misalignment angles between different systems. In this paper, a dynamic calibration method of misalignment angles between two systems was proposed. This method uses the speed and attitude information of two sets of INSs during the movement of the vehicle as measurements to dynamically calibrate the misalignment angles of two systems without additional information sources or other external measuring equipment, such as turntable. A mathematical model of misalignment angles between two INSs was established. The simulation experiment and the INSs vehicle experiments were conducted to verify the effectiveness of the method. The results show that the calibration accuracy of misalignment angles between the two sets of systems can reach to 1″ while using the proposed method.

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