High-precision and robust localization system for mobile robots in complex and large-scale indoor scenes

2021 ◽  
Vol 18 (5) ◽  
pp. 172988142110476
Author(s):  
Jibo Wang ◽  
Chengpeng Li ◽  
Bangyu Li ◽  
Chenglin Pang ◽  
Zheng Fang
Keyword(s):  
Mobile Robots ◽  
Real Time ◽  
High Precision ◽  
Autonomous Navigation ◽  
Target Area ◽  
Global Localization ◽  
Localization System ◽  
Current Localization ◽  
Robust Localization ◽  
Indoor Scenes

High-precision and robust localization is the key issue for long-term and autonomous navigation of mobile robots in industrial scenes. In this article, we propose a high-precision and robust localization system based on laser and artificial landmarks. The proposed localization system is mainly composed of three modules, namely scoring mechanism-based global localization module, laser and artificial landmark-based localization module, and relocalization trigger module. Global localization module processes the global map to obtain the map pyramid, thus improve the global localization speed and accuracy when robots are powered on or kidnapped. Laser and artificial landmark-based localization module is employed to achieve robust localization in highly dynamic scenes and high-precision localization in target areas. The relocalization trigger module is used to monitor the current localization quality in real time by matching the current laser scan with the global map and feeds it back to the global localization module to improve the robustness of the system. Experimental results show that our method can achieve robust robot localization and real-time detection of the current localization quality in indoor scenes and industrial environment. In the target area, the position error is less than 0.004 m and the angle error is less than 0.01 rad.

scholarly journals Autonomous and Safe Navigation of Mobile Robots in Vineyard with Smooth Collision Avoidance

Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 954
Author(s):  
Abhijeet Ravankar ◽  
Ankit A. Ravankar ◽  
Arpit Rawankar ◽  
Yohei Hoshino
Keyword(s):  
Mobile Robots ◽  
Real Time ◽  
Collision Avoidance ◽  
Autonomous Navigation ◽  
Autonomous Robots ◽  
Robot Navigation ◽  
Indoor Environments ◽  
Safe Distance ◽  
Field Robots ◽  
Safe Navigation

In recent years, autonomous robots have extensively been used to automate several vineyard tasks. Autonomous navigation is an indispensable component of such field robots. Autonomous and safe navigation has been well studied in indoor environments and many algorithms have been proposed. However, unlike structured indoor environments, vineyards pose special challenges for robot navigation. Particularly, safe robot navigation is crucial to avoid damaging the grapes. In this regard, we propose an algorithm that enables autonomous and safe robot navigation in vineyards. The proposed algorithm relies on data from a Lidar sensor and does not require a GPS. In addition, the proposed algorithm can avoid dynamic obstacles in the vineyard while smoothing the robot’s trajectories. The curvature of the trajectories can be controlled, keeping a safe distance from both the crop and the dynamic obstacles. We have tested the algorithm in both a simulation and with robots in an actual vineyard. The results show that the robot can safely navigate the lanes of the vineyard and smoothly avoid dynamic obstacles such as moving people without abruptly stopping or executing sharp turns. The algorithm performs in real-time and can easily be integrated into robots deployed in vineyards.

Navigation of a wheeled mobile robotic agent using modified grey wolf optimization controller

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chittaranjan Paital ◽  
Saroj Kumar ◽  
Manoj Kumar Muni ◽  
Dayal R. Parhi ◽  
Prasant Ranjan Dhal
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Real Time ◽  
Autonomous Navigation ◽  
Simulation Software ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Content Type ◽  
Cluttered Environment

PurposeSmooth and autonomous navigation of mobile robot in a cluttered environment is the main purpose of proposed technique. That includes localization and path planning of mobile robot. These are important aspects of the mobile robot during autonomous navigation in any workspace. Navigation of mobile robots includes reaching the target from the start point by avoiding obstacles in a static or dynamic environment. Several techniques have already been proposed by the researchers concerning navigational problems of the mobile robot still no one confirms the navigating path is optimal.Design/methodology/approachTherefore, the modified grey wolf optimization (GWO) controller is designed for autonomous navigation, which is one of the intelligent techniques for autonomous navigation of wheeled mobile robot (WMR). GWO is a nature-inspired algorithm, which mainly mimics the social hierarchy and hunting behavior of wolf in nature. It is modified to define the optimal positions and better control over the robot. The motion from the source to target in the highly cluttered environment by negotiating obstacles. The controller is authenticated by the approach of V-REP simulation software platform coupled with real-time experiment in the laboratory by using Khepera-III robot.FindingsDuring experiments, it is observed that the proposed technique is much efficient in motion control and path planning as the robot reaches its target position without any collision during its movement. Further the simulation through V-REP and real-time experimental results are recorded and compared against each corresponding results, and it can be seen that the results have good agreement as the deviation in the results is approximately 5% which is an acceptable range of deviation in motion planning. Both the results such as path length and time taken to reach the target is recorded and shown in respective tables.Originality/valueAfter literature survey, it may be said that most of the approach is implemented on either mathematical convergence or in mobile robot, but real-time experimental authentication is not obtained. With a lack of clear evidence regarding use of MGWO (modified grey wolf optimization) controller for navigation of mobile robots in both the environment, such as in simulation platform and real-time experimental platforms, this work would serve as a guiding link for use of similar approaches in other forms of robots.

Intelligent Robot Obstacle Avoidance Perception Method Using a High-Precision Laser Distance Measuring System

2021 ◽  
Vol 16 (3) ◽  
pp. 471-478
Author(s):  
Yuzhou Liu
Keyword(s):  
Mobile Robots ◽  
Real Time ◽  
Obstacle Avoidance ◽  
High Precision ◽  
Large Scale ◽  
Measuring System ◽  
Channel Modulation ◽  
Avoidance Control ◽  
Modulation Signal ◽  
Distance Measuring

Laser distance measuring is increasingly used in large-scale and real-time scanning measurement, including three-dimensional map construction, size measurement of large-scale buildings, and real-time surface information acquisition. Therefore, there are high-precision and high-efficiency requirements for the laser distance measuring system. Based on the phase laser distance measuring, a laser parallel distance measuring system is proposed as per the frequency division multiplexer principle. The system uses multi-channel modulation signals with different frequencies to drive multiple lasers to emit light intensity in parallel, and then the light wave is measured according to the change of the modulation signal. The single-channel modulation signal adopts the multi-scale composite wave to eliminate the uncertainty of the whole wavelength by using the combination of two scales and distance measurement. The multiple echo signals after diffuse reflection of the measured target are mixed and received by the same photodetector and go through signal amplification, clutter filtering, etc., and then are sent to the mixing unit together with the reference signal, followed by down-frequency processing, with the effective signal obtained through the low-frequency filter. In the experiment, the laser distance measuring system is used for obstacle avoidance control of mobile robots, and fuzzy control is used to design the corresponding obstacle avoidance controller. The robot lateral deviation and heading angle are used as the input of the fuzzy controller, and the track variation is the output. The test verifies that the obstacle avoidance control is effective, that is, the laser distance measuring system designed in this research can be used for the obstacle avoidance control of mobile robots.

Visual Navigation of Wheeled Mobile Robots Using Deep Reinforcement Learning: Simulation to Real-Time Implementation

2020 ◽  
Author(s):  
Ezebuugo Nwaonumah ◽  
Biswanath Samanta
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robots ◽  
Real Time ◽  
Autonomous Navigation ◽  
Real Life ◽  
Visual Navigation ◽  
Wheeled Mobile Robots ◽  
Depth Images ◽  
Gradient Based ◽  
Simulation Environments

Abstract A study is presented on applying deep reinforcement learning (DRL) for visual navigation of wheeled mobile robots (WMR), both in simulation and real-time implementation under dynamic and unknown environments. The policy gradient based asynchronous advantage actor critic (A3C), has been considered. RGB (red, green and blue) and depth images have been used as inputs in implementation of A3C algorithm to generate control commands for autonomous navigation of WMR. The initial A3C network was generated and trained progressively in OpenAI Gym Gazebo based simulation environments within robot operating system (ROS) framework for a popular target WMR, Kobuki TurtleBot2. A pre-trained deep neural network ResNet50 was used after further training with regrouped objects commonly found in laboratory setting for target-driven visual navigation of Turlebot2 through DRL. The performance of A3C with multiple computation threads (4, 6, and 8) was simulated and compared in three simulation environments. The performance of A3C improved with number of threads. The trained model of A3C with 8 threads was implemented with online learning using Nvidia Jetson TX2 on-board Turtlebot2 for mapless navigation in different real-life environments. Details of the methodology, results of simulation and real-time implementation through transfer learning are presented along with recommendations for future work.

Autonomous Navigation of Mobile Robot Based on Flood Fill Algorithm

2016 ◽  
Vol 12 (1) ◽  
pp. 79-84 ◽  
Author(s):  
Ayad Jabbar
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Video Streaming ◽  
High Precision ◽  
Autonomous Navigation ◽  
Human Interaction ◽  
Live Video ◽  
Point To Point ◽  
Flood Fill ◽  
Client Device

The autonomous navigation of robots is an important area of research. It can intelligently navigate itself from source to target within an environment without human interaction. Recently, algorithms and techniques have been made and developed to improve the performance of robots. It’s more effective and has high precision tasks than before. This work proposed to solve a maze using a Flood fill algorithm based on real time camera monitoring the movement on its environment. Live video streaming sends an obtained data to be processed by the server. The server sends back the information to the robot via wireless radio. The robot works as a client device moves from point to point depends on server information. Using camera in this work allows voiding great time that needs it to indicate the route by the robot.

UNIFORM SCATTERING OF AUTONOMOUS MOBILE ROBOTS IN A GRID

2011 ◽  
Vol 22 (03) ◽  
pp. 679-697 ◽  
Author(s):  
LALI BARRIÈRE ◽  
PAOLA FLOCCHINI ◽  
EDUARDO MESA-BARRAMEDA ◽  
NICOLA SANTORO
Keyword(s):  
Mobile Robots ◽  
Scattering Problem ◽  
Autonomous Mobile Robots ◽  
State Machines ◽  
Global Localization ◽  
Localization System ◽  
Constant Size ◽  
Finite State ◽  
Visibility Range ◽  
Explicit Communication

We consider the uniform scattering problem for a set of autonomous mobile robots deployed in a grid network: starting from an arbitrary placement in the grid, using purely localized computations, the robots must move so to reach in finite time a state of static equilibrium in which they cover uniformly the grid. The theoretical quest is on determining the minimal capabilities needed by the robots to solve the problem. We prove that uniform scattering is indeed possible even for very weak robots. The proof is constructive. We present a provably correct protocol for uniform self-deployment in a grid. The protocol is fully localized, collision-free, and it makes minimal assumptions; in particular: (1) it does not require any direct or explicit communication between robots; (2) it makes no assumption on robots synchronization or timing, hence the robots can be fully asynchronous in all their actions; (3) it requires only a limited visibility range; (4) it uses at each robot only a constant size memory, hence computationally the robots can be simple Finite-State Machines; (5) it does not need a global localization system but only orientation in the grid (e.g., a compass); (6) it does not require identifiers, hence the robots can be anonymous and totally identical.

An efficient approach to pose tracking based on odometric error modelling for mobile robots

Robotica ◽  
2014 ◽  
Vol 33 (6) ◽  
pp. 1231-1249 ◽  
Author(s):  
Jingdong Yang ◽  
Jinghui Yang ◽  
Zesu Cai
Keyword(s):  
Mobile Robots ◽  
Systematic Error ◽  
Real Time ◽  
Autonomous Navigation ◽  
Error Propagation ◽  
Motion Path ◽  
Control Input ◽  
Efficient Approach ◽  
Pose Tracking ◽  
Propagation Law

SUMMARYOdometric error modelling for mobile robots is the basis of pose tracking. Without bounds the odometric accumulative error decreases localisation precision after long-range movement, which is often not capable of being compensated for in real time. Therefore, an efficient approach to odometric error modelling is proposed in regard to different drive type mobile robots. This method presents a hypothesis that the motion path approximates a circular arc. The approximate functional expressions between the control input of odometry and non-systematic error as well as systematic error derived from odometric error propagation law. Further an efficient algorithm of pose tracking is proposed for mobile robots, which is able to compensate for the non-systematic and systematic error in real time. These experiments denote that the odometric error modelling reduces the accumulative error of odometry efficiently and improves the specific localisation process significantly during autonomous navigation.

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