Deep Learning-Based Stair Segmentation and Behavioral Cloning for Autonomous Stair Climbing

Panchi, Navid; Agrawal, Khush; Patil, Unmesh; Gujarathi, Aniket; Jain, Aman; Namdeo, Harsha; Chiddarwar, Shital S.

doi:10.1142/s1793351x1940021x

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…Currently, many robots are being used in industries [2], homes [3], military applications, disaster management [4], etc., all around the world. The advancements in robotics have made lives easier for humans in many aspects and it provides with a safer and more efficient alternative to perform tasks which are difficult or time consuming for humans.…”

Section: Related Workmentioning

confidence: 99%

Design of Sub-Systems for GPS-Guided Autonomous Delivery Robot System

Basri¹,

Sahrir²

2022

Elektrika

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The use of autonomous robots for delivery services is a new potential goldmine. Furthermore, since the e-commerce and delivery industry are growing at a rapid rate, it is recommended that a system that could handle the high-volume traffic as well serve as a new customer attraction, be implemented. Therefore, this work aims to develop the Autonomous Delivery Robot System (ADRS) that could be utilized for delivery services. The ADRS uses the Arduino microcontroller to run a program. The developed system consists of three main components (sub-systems), namely, mobile robot, cloud server and mobile application. The mobile robot is equipped with a tracking and an obstacle detection system to maneuver it autonomously. The ultrasonic sensors are used for obstacle detection, coupled with a Global Positioning System (GPS) to enable it deliver packages to customers in a typical outdoor environment. The ADRS ensures a human-contactless and secure delivery by using a password protected container to carry the delivery packages. Only the customer can unlock the container using the provided password via mobile application. The mobile application with customized map based on Google Maps is created to provide the user with an interactive interface. A camera is also attached on the robot for real-time monitoring purpose. The performance of the ADRS has been evaluated to determine the functionality of the developed system.

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Section: Related Workmentioning

confidence: 99%

Design of Sub-Systems for GPS-Guided Autonomous Delivery Robot System

Basri¹,

Sahrir²

2022

Elektrika

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“…Additionally, some state of the art staircase localization systems have been implemented using RGB images, deep learning and point clouds. Deep learning methods to detect staircases use neuronal networks such as YOLOv3-tiny [8,9], YOLOv4 [8] and SSD [10,11] or implement a stair step detection using YOLOv3-tiny [12] and staircase segmentation using AlbuNet [13]. The use of CNNs to detect an object in an image allows further processing in the detected area or region of interest (ROI).…”

Section: Introductionmentioning

confidence: 99%

Staircase Detection, Characterization and Approach Pipeline for Search and Rescue Robots

et al. 2021

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Currently, most rescue robots are mainly teleoperated and integrate some level of autonomy to reduce the operator’s workload, allowing them to focus on the primary mission tasks. One of the main causes of mission failure are human errors and increasing the robot’s autonomy can increase the probability of success. For this reason, in this work, a stair detection and characterization pipeline is presented. The pipeline is tested on a differential drive robot using the ROS middleware, YOLOv4-tiny and a region growing based clustering algorithm. The pipeline’s staircase detector was implemented using the Neural Compute Engines (NCEs) of the OpenCV AI Kit with Depth (OAK-D) RGB-D camera, which allowed the implementation using the robot’s computer without a GPU and, thus, could be implemented in similar robots to increase autonomy. Furthermore, by using this pipeline we were able to implement a Fuzzy controller that allows the robot to align itself, autonomously, with the staircase. Our work can be used in different robots running the ROS middleware and can increase autonomy, allowing the operator to focus on the primary mission tasks. Furthermore, due to the design of the pipeline, it can be used with different types of RGB-D cameras, including those that generate noisy point clouds from low disparity depth images.

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“…The aim of autonomous robots is to work alongside humans and try to make human life easier. Currently, many robots are being used in industries [2], homes [3], military applications, disaster management [4], etc., all around the world. The advancements in robotics has made lives easier for humans in many aspects and it provides with a safer and more e cient alternative to perform tasks which are di cult or time consuming for humans.…”

Section: Introductionmentioning

confidence: 99%

Design and Development of Autonomous Delivery Robot

Gujarathi,

Kulkarni,

Patil

et al. 2021

Preprint

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The eld of autonomous robotics is growing at a rapid rate. The trend to use increasingly more sensors in vehicles is driven both by legislation and consumer demands for higher safety and reliable service. Nowadays, robots are found everywhere, ranging from homes, hospitals to industries, and military operations. Autonomous robots are developed to be robust enough to work beside humans and to carry out jobs efciently. Humans have a natural sense of understanding of the physical forces acting around them like gravity, sense of motion, etc. which are not taught explicitly but are developed naturally. However, this is not the case with robots. To make the robot fully autonomous and competent to work with humans, the robot must be able to perceive the situation and devise a plan for smooth operation, considering all the adversities that may occur while carrying out the tasks. In this thesis, we present an autonomous mobile robot platform that delivers the package within the VNIT campus without any human intercommunication. From an initial user-supplied geographic target location, the system plans an optimized path and autonomously navigates through it. The entire pipeline of an autonomous robot working in outdoor environments is explained in detail in this thesis. We have addressed the problem of semantic segmentation for road and obstacle detection. The common networks used in the literature are reported, along with some motivation for each of them. A general requirement for autonomous navigation is the availability of a highde nition map. The di erent layers of maps are discussed and a map of the VNIT campus with required details is constructed. The issue of robust localization and sensor fusion is explained in detail in this thesis. The problem of the need of 360-degree vision to the autonomous vehicles is also discussed. Catadioptric cameras that output panoramic views images with very large elds of view. It turns out that the design of such cameras solves plenty of problems including creating a 3D point cloud and providing preliminary visual data to the obstacle detection and motion planning. The proposed solution is characterized by an intricate mixture of optics and geometry as exempli ed.

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Deep Learning-Based Stair Segmentation and Behavioral Cloning for Autonomous Stair Climbing

Cited by 6 publications

References 16 publications

Design of Sub-Systems for GPS-Guided Autonomous Delivery Robot System

Design of Sub-Systems for GPS-Guided Autonomous Delivery Robot System

Staircase Detection, Characterization and Approach Pipeline for Search and Rescue Robots

Design and Development of Autonomous Delivery Robot

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