scholarly journals Implementation of Slam, Navigation, Obstacle Avoidance, and Path Planning of a Robust Mobile Robot Using 2D Laser Scanner

2021 ◽  
Author(s):  
Omid Karimpour
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Low Cost ◽  
Laser Scanner ◽  
Memory Consumption ◽  
Localization And Mapping ◽  
Reliable Performance ◽  
Cost Efficient ◽  
Office Environments ◽  
Robotic Applications

Over the last decade, navigation and Simultaneous Localization and Mapping (SLAM) have become key players in developing robust mobile robots. Several SLAM approaches utilizing camera, laser scan, sonar and fusion of sensors were developed and improved by a number of researchers. In this thesis, comparisons of these methods were evaluated, especially those offering low cost benefits, and low computation and memory consumption. The aim of this thesis was to select the most reliable and cost-efficient approach for indoor autonomous robotic applications. Currently, there are numerous studies that have optimized these SLAM methods; however, they still suffer from various complications such as scale drifting and excessive computation. This study performed different experiments to observe these challenges in realworld environments. A modified Pioneer robot was used to implement the selected SLAM system and furthermore, perform obstacle avoidance and path planning in indoor office environments. The results and tests show the reliable performance of Gmapping after tuning its parameter and set right configurations.

scholarly journals Implementation of Slam, Navigation, Obstacle Avoidance, and Path Planning of a Robust Mobile Robot Using 2D Laser Scanner

2021 ◽  
Author(s):  
Omid Karimpour
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Low Cost ◽  
Laser Scanner ◽  
Memory Consumption ◽  
Localization And Mapping ◽  
Reliable Performance ◽  
Cost Efficient ◽  
Office Environments ◽  
Robotic Applications

Over the last decade, navigation and Simultaneous Localization and Mapping (SLAM) have become key players in developing robust mobile robots. Several SLAM approaches utilizing camera, laser scan, sonar and fusion of sensors were developed and improved by a number of researchers. In this thesis, comparisons of these methods were evaluated, especially those offering low cost benefits, and low computation and memory consumption. The aim of this thesis was to select the most reliable and cost-efficient approach for indoor autonomous robotic applications. Currently, there are numerous studies that have optimized these SLAM methods; however, they still suffer from various complications such as scale drifting and excessive computation. This study performed different experiments to observe these challenges in realworld environments. A modified Pioneer robot was used to implement the selected SLAM system and furthermore, perform obstacle avoidance and path planning in indoor office environments. The results and tests show the reliable performance of Gmapping after tuning its parameter and set right configurations.

The Secant Method: Global Trajectory Planning With Variable Radius, Solid Obstacles

2018 ◽  
Author(s):  
Konrad J. Ahlin ◽  
Nader Sadegh ◽  
Ai-Ping Hu
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Global Minimum ◽  
Dimensional Space ◽  
Variable Radius ◽  
Control Scheme ◽  
Solid Obstacles ◽  
Global Trajectory ◽  
Unique Potential ◽  
Robotic Applications

Artificial Potential Field (APF) theory is a unique branch of robotic path planning, which could be capable of handling the need for high dimensional robotic obstacle avoidance. However, APF theories have general performance issues which often make them undesirable in application. This research analyzes the Secant Approach; an algorithm developed to follow the APF style of path planning, but which has guaranteed convergence and obstacle avoidance properties in n-dimensional space. Using a unique potential function, the Secant Approach can guarantee a global minimum at the target while provably eliminating local minimums at other locations. Also, a control scheme has been developed which has guaranteed convergence properties. The Secant Approach is therefore capable of guiding various forms of robotic applications to target positions in n-dimensional space, making the theory a powerful path planning tool. This analysis examines the structure of the Secant Approach and extends the theory to include variable radius, solid obstacles.

FEASIBILITY ANALYSIS OF 2D-SLAM USING COMBINATION OF KINECT AND LASER SCANNER

2015 ◽  
Vol 76 (12) ◽  
Author(s):  
Ahmad Shakaff Ali Yeon ◽  
Kamarulzaman Kamarudin ◽  
Retnam Visvanathan ◽  
Syed Muhammad Mamduh ◽  
Latifah Munirah Kamarudin ◽  
...  
Keyword(s):  
Obstacle Avoidance ◽  
Laser Scanner ◽  
Simultaneous Localization And Mapping ◽  
Field Of View ◽  
Feasibility Analysis ◽  
Localization And Mapping ◽  
Wide Range ◽  
Different Types ◽  
Robotic Application

Both laser scanner and Kinect has been widely used in robotic application for simultaneous localization and mapping (SLAM). However, each sensor has its own limitations. For example, Kinect does not have a wide range field of view and laser scanner could not detect obstacles beyond its scanning plane. The paper proposes a method to combine the data from Kinect and laser scanner to perform a 2D-SLAM. The sensors will be mounted in different types of configurations; both facing forward and facing in opposite directions. This system is able to detect complex surrounding features for better mapping and obstacle avoidance.

Development of an Indoor Positioning and Navigation System using Wi-Fi network and BLE beacons for the Smart Campus: A case study

2020 ◽  
Author(s):  
Paweł Trybała
Keyword(s):  
Navigation System ◽  
Low Cost ◽  
Access Point ◽  
Indoor Positioning ◽  
Ultra Wideband ◽  
Future Research ◽  
Localization And Mapping ◽  
Cost Efficient ◽  
Gis Software

<p>Nowadays, powerful hand-held devices, like smartphones, tablets and smartwatches, are ordinary things, which many people take anywhere they go. One of the major advantages of this technology is the ability to locate its user by means of GNSS or cellular positioning. Paired with popular, free mobile mapping applications, it greatly simplifies the problem of finding oneself in the unknown place, calculating the best route to one’s destination by various means of transport or tracking one’s movement. For this reason, outdoor navigation is a well-established and widespread technology. The problem arises, when positioning and wayfinding are needed in a GNSS-denied environment, e.g. a building or a mine. In a complex, large or multi-floor constructions modern techniques for easing the navigation through them are rarely applied. Recent years brought numerous new, promising approaches and algorithms for solving a problem of indoor positioning and navigation, but many of them can’t be easily implemented on a typical smartphone or conveniently used. This includes Simultaneous Localization and Mapping (SLAM) and algorithms based on Augmented Reality (AR). It seems that the most feasible and cost-efficient methods are those based on Wi-Fi Access Point (AP), low-cost Bluetooth Low Energy (BLE) or Ultra-Wideband (UWB) beacons. This research aims to describe the process of developing such an Indoor Positioning and Navigation System in one of the buildings, located on the campus of the Wroclaw University of Science and Technology, and identify the main challenges that have to be overcome during this process. Feasibility of available GIS software solutions for this application is analyzed. Directions for future research and development are discussed.</p>

Design and Fabrication of an Autonomous Mobile Robot for Obstacle Avoidance and Path Planning in Partially Known Environment

2011 ◽  
Vol 403-408 ◽  
pp. 3917-3924
Author(s):  
Deep Sharma ◽  
S. K. Dwivedy
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Low Cost ◽  
Dc Motor ◽  
Outdoor Environment ◽  
Autonomous Mobile Robot ◽  
Solar Panels ◽  
Dc Motors ◽  
Servo Controller

In this paper, an autonomous mobile robot has been designed and fabricated which can be used in both indoor and outdoor for industrial and household applications. Here using six servo motors and four DC motors with their controllers (servo controller and L293D DC Motor controller) the mobile robot can pick any object from its workspace and by avoiding collision it can place the object in the desired location. ASCII ultrasonic sensor and motion sensor are used along with ATmega 2560 microcontroller which is programmed to take the sensors output as its input and controls the dc motor and servo motors to pick and place objects and avoid obstacle during motion of the mobile robot. Here low-cost solar panels have been used to recharge the Li-ion batteries used for the motors and microcontroller in case of outdoor environment. The obstacle avoidance and path planning algorithms have been developed and a case study has been presented in this paper.

scholarly journals Learning and SLAM Based Decision Support Platform for Sewer Inspection

2020 ◽  
Vol 12 (6) ◽  
pp. 968 ◽  
Author(s):  
Tzu-Yi Chuang ◽  
Cheng-Che Sung
Keyword(s):  
Low Cost ◽  
Autonomous Systems ◽  
Laser Scanner ◽  
Geometric Information ◽  
Depth Cameras ◽  
Safety Issues ◽  
Pipeline Inspection ◽  
Localization And Mapping ◽  
Additional Control ◽  
Sewer Inspection

Routine maintenance of drainage systems, including structure inspection and dredging, plays an essential role in disaster prevention and reduction. Autonomous systems have been explored to assist in pipeline inspection due to safety issues in unknown underground environments. Most of the existing systems merely rely on video records for visual examination since sensors such as a laser scanner or sonar are costly, and the data processing requires expertise. This study developed a compact platform for sewer inspection, which consisted of low-cost components such as infrared and depth cameras with a g-sensor. Except for visual inspection, the platform not only identifies internal faults and obstacles but also evaluates their geometric information, geo-locations, and the block ratio of a pipeline in an automated fashion. As the platform moving, the g-sensor reflects the pipeline flatness, while an integrated simultaneous localization and mapping (SLAM) strategy reconstructs the 3D map of the pipeline conditions simultaneously. In the light of the experimental results, the reconstructed moving trajectory achieved a relative accuracy of 0.016 m when no additional control points deployed along the inspecting path. The geometric information of observed defects accomplishes an accuracy of 0.9 cm in length and width estimation and an accuracy of 1.1% in block ratio evaluation, showing promising results for practical sewer inspection. Moreover, the labeled deficiencies directly increase the automation level of documenting irregularity and facilitate the understanding of pipeline conditions for management and maintenance.

scholarly journals Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2244
Author(s):  
S. M. Yang ◽  
Y. A. Lin
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Random Trees ◽  
Average Deviation ◽  
Lane Changes ◽  
Path Smoothing ◽  
Rapidly Exploring Random Trees

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

scholarly journals A survey: which features are required for dynamic visual simultaneous localization and mapping?

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Zewen Xu ◽  
Zheng Rong ◽  
Yihong Wu
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Dynamic Environments ◽  
Visual Features ◽  
Advantages And Disadvantages ◽  
Intelligent Robots ◽  
Localization And Mapping ◽  
High Level ◽  
Static World ◽  
Robotic Applications ◽  
Significant Attention

AbstractIn recent years, simultaneous localization and mapping in dynamic environments (dynamic SLAM) has attracted significant attention from both academia and industry. Some pioneering work on this technique has expanded the potential of robotic applications. Compared to standard SLAM under the static world assumption, dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly. Therefore, dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments. Additionally, to meet the demands of some high-level tasks, dynamic SLAM can be integrated with multiple object tracking. This article presents a survey on dynamic SLAM from the perspective of feature choices. A discussion of the advantages and disadvantages of different visual features is provided in this article.

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