Indoor navigation for the visually impaired : enhancements through utilisation of the Internet of Things and deep learning

Wayfinding and navigation are essential aspects of independent living that heavily rely on the sense of vision. Walking in a complex building requires knowing exact location to find a suitable path to the desired destination, avoiding obstacles and monitoring orientation and movement along the route. People who do not have access to sight-dependent information, such as that provided by signage, maps and environmental cues, can encounter challenges in achieving these tasks independently. They can rely on assistance from others or maintain their independence by using assistive technologies and the resources provided by smart environments. Several solutions have adapted technological innovations to combat navigation in an indoor environment over the last few years. However, there remains a significant lack of a complete solution to aid the navigation requirements of visually impaired (VI) people. The use of a single technology cannot provide a solution to fulfil all the navigation difficulties faced. A hybrid solution using Internet of Things (IoT) devices and deep learning techniques to discern the patterns of an indoor environment may help VI people gain confidence to travel independently. This thesis aims to improve the independence and enhance the journey of VI people in an indoor setting with the proposed framework, using a smartphone. The thesis proposes a novel framework, Indoor-Nav, to provide a VI-friendly path to avoid obstacles and predict the user s position. The components include Ortho-PATH, Blue Dot for VI People (BVIP), and a deep learning-based indoor positioning model. The work establishes a novel collision-free pathfinding algorithm, Orth-PATH, to generate a VI-friendly path via sensing a grid-based indoor space. Further, to ensure correct movement, with the use of beacons and a smartphone, BVIP monitors the movements and relative position of the moving user. In dark areas without external devices, the research tests the feasibility of using sensory information from a smartphone with a pre-trained regression-based deep learning model to predict the user s absolute position. The work accomplishes a diverse range of simulations and experiments to confirm the performance and effectiveness of the proposed framework and its components. The results show that Indoor-Nav is the first type of pathfinding algorithm to provide a novel path to reflect the needs of VI people. The approach designs a path alongside walls, avoiding obstacles, and this research benchmarks the approach with other popular pathfinding algorithms. Further, this research develops a smartphone-based application to test the trajectories of a moving user in an indoor environment

A comprehensive study on pathfinding techniques for robotics and video games

This survey provides an overview of popular pathfinding algorithms and techniques based on graph generation problems. We focus on recent developments and improvements in existing techniques and examine their impact on robotics and the video games industry. We have categorized pathfinding algorithms based on a 2D/3D environment search. The aim of this paper is to provide researchers with a thorough background on the progress made in the last 10 years in this field, summarize the principal techniques, and describe their results. We also give our expectations for future trends in this field and discuss the possibility of using pathfinding techniques in more extensive areas

Robotic 3D Plant Perception and Leaf Probing with Collision-Free Motion Planning for Automated Indoor Plant Phenotyping

Various instrumentation devices for plant physiology study such as chlorophyll fluorimeter and Raman spectrometer require leaf probing with accurate probe positioning and orientation with respect to leaf surface. In this work, we aimed to automate this process with a Kinect V2 sensor, a high-precision 2D laser profilometer, and a 6-axis robotic manipulator in a high-throughput manner. The relatively wide field of view and high resolution of Kinect V2 allowed rapid capture of the full 3D environment in front of the robot. Given the number of plants, the location and size of each plant were estimated by K-means clustering. A real-time collision-free motion planning framework based on Probabilistic Roadmap was adopted to maneuver the robotic manipulator without colliding with the plants. Each plant was scanned from top with the short-range profilometer to obtain a high-precision point cloud where potential leaf clusters were extracted by region growing segmentation. Each leaf segment was further partitioned into small patches by Voxel Cloud Connectivity Segmentation. Only the small patches with low root mean square values of plane fitting were used to compute probing poses. To evaluate probing accuracy, a square surface was scanned at various angles and its centroid was probed perpendicularly with a probing position error of 1.5 mm and a probing angle error of 0.84 degrees on average. Our growth chamber leaf probing experiment showed that the average motion planning time was 0.4 seconds and the average traveled distance of tool center point was 1 meter

Collision-free Navigation System for Robotic Helicopter

Tato práce je zaměřena na vytvoření bezkolízního navigačního systému pro robotickou helikopteru. Během této práce je odvozen a linearizován matematický model kvadrakoptéry. Regulátor pro UAV je navržen na základě tohoto modelu. Řešení problému s lokalizací je poskytnuto ve formě Kalmanova filtru. Pro uložení konfiguračního prostoru robota bude navržena prostorově efektivní struktura octree a pro navigaci v tomto prostředí je použit algoritmus A*. Implementace navržených algoritmů je provedena v programovacím jazyce C++ a testována v simulačním prostředí Webots.This work is focused on the creation of a collision-free navigation system for a robotic helicopter. During this work the matematical model of the quadracopter is derived and linearized. The regulator for the UAV is designed based on this model. The solution for the localization problem is provided in the form of Kalman filter. Space-efficient octree structure is proposed to store robot configuration space and A* algorithm is used for navigation in this environment. The implementation of the proposed algorithms is done in programming language C++ and tested in simulation environment Webots

Assessment of local path planners in a indoor and outdoor robot

Modern mobile robotics are entering commercial use in a variety of non-controlled environments. One such robot is the Roboguide service and guide robot for the visually impaired. For the smooth operation of a service robot in the daily life of its users, it is imperative that the paths along which the robot travels are intuitive, comfortable, and above all, safe. The goal of this thesis is to assess the viability of the Elastic Band, Timed Elastic Band and Dynamic Window Approach path-planners in a dynamic environment. This is accomplished through testing various scenarios typical in dynamic environments, including outright collisions and near-miss scenarios. The testing is done on a simulated platform. In addition to assessing the current viability of the path-planners in question, this thesis also aims to identify challenges and problems caused by the dynamic nature of the environment. The results suggest the Timed Elastic Band is the superior path-planner. Dynamic obstacles create problems for all the tested path-planners, and a future approach to cost-efficient dynamic prediction is suggested. The tests within this thesis are implemented using Robotic Operating System(ROS) and the robot simulation environment Gazebo. Implementations are based on real products and software modules.Nykyaikaista autonomista robotiikkaa on alettu soveltaa kaupallisessa käytössä erilaisissa kontrolloimattomissa toimintaympäristöissä. Yksi näistä sovelluskohteista on Roboguide, näkövammaisille suunnattu opasrobotti. Jotta robottia olisi intuitiivista ja turvallista käyttää, on oleellista, että robotti pystyy toimimaan arkipäivän eri tilanteissa helppokäyttöisesti, mukavasti ja ennen kaikkea turvallisesti. Tämän diplomityön tavoite on arvioida Elastic Band, Timed Elastic Band ja Dynamic Window Approach reittisuunnitelualgoritmien soveltuvuutta dynaamisessa ympäristössä. Tätä varten on toteutettu testisarja, jossa simuloidaan tyypillisiä dynaamisen ympäristön haasteita, kuten törmäys- ja läheltä-ohitustilanteita. Testaus toteutettiin simuloidulla alustalla. Eri reittisuunnittelualgoritmien soveltuvuuden arvioinnin lisäksi diplomityö pyrkii tunnistamaan dynaamisessa ympäristössä liikkumiseen liittyviä haasteita ja uhkakuvia. Testatuista algoritmeista Timed Elastic Band soveltuu selvästi parhaiten dynaamiseen ympäristöön. Lisäksi työssä ehdotetaan lähestymistapaa dynaamisten esteiden sijainnin ennustamiseen laskennallisesti tehokkaasti. Testaus on toteutettu ROS-pohjaisella robotilla ja testit on suoritettu Gazebo-simulointiympäristössä. Testaus ja simuloitu robotti perustuu aitoon tuotteeseen ja sen komponentteihin

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

Camera Marker Networks for Pose Estimation and Scene Understanding in Construction Automation and Robotics.

The construction industry faces challenges that include high workplace injuries and fatalities, stagnant productivity, and skill shortage. Automation and Robotics in Construction (ARC) has been proposed in the literature as a potential solution that makes machinery easier to collaborate with, facilitates better decision-making, or enables autonomous behavior. However, there are two primary technical challenges in ARC: 1) unstructured and featureless environments; and 2) differences between the as-designed and the as-built. It is therefore impossible to directly replicate conventional automation methods adopted in industries such as manufacturing on construction sites. In particular, two fundamental problems, pose estimation and scene understanding, must be addressed to realize the full potential of ARC. This dissertation proposes a pose estimation and scene understanding framework that addresses the identified research gaps by exploiting cameras, markers, and planar structures to mitigate the identified technical challenges. A fast plane extraction algorithm is developed for efficient modeling and understanding of built environments. A marker registration algorithm is designed for robust, accurate, cost-efficient, and rapidly reconfigurable pose estimation in unstructured and featureless environments. Camera marker networks are then established for unified and systematic design, estimation, and uncertainty analysis in larger scale applications. The proposed algorithms' efficiency has been validated through comprehensive experiments. Specifically, the speed, accuracy and robustness of the fast plane extraction and the marker registration have been demonstrated to be superior to existing state-of-the-art algorithms. These algorithms have also been implemented in two groups of ARC applications to demonstrate the proposed framework's effectiveness, wherein the applications themselves have significant social and economic value. The first group is related to in-situ robotic machinery, including an autonomous manipulator for assembling digital architecture designs on construction sites to help improve productivity and quality; and an intelligent guidance and monitoring system for articulated machinery such as excavators to help improve safety. The second group emphasizes human-machine interaction to make ARC more effective, including a mobile Building Information Modeling and way-finding platform with discrete location recognition to increase indoor facility management efficiency; and a 3D scanning and modeling solution for rapid and cost-efficient dimension checking and concise as-built modeling.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113481/1/cforrest_1.pd