Depth Camera based Autonomous Mobile Robot for Indoor Environments

2018 ◽  
Author(s):  
Akshay Krishnan ◽  
Sowrabh Nayak ◽  
Anup Rao U. ◽  
Sudarshan Patilkulkarni
Keyword(s):  
Mobile Robot ◽  
Depth Camera ◽  
Indoor Environments ◽  
Autonomous Mobile Robot

scholarly journals COVID surveillance robot: Monitoring social distancing constraints in indoor scenarios

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0259713
Author(s):  
Adarsh Jagan Sathyamoorthy ◽  
Utsav Patel ◽  
Moumita Paul ◽  
Yash Savle ◽  
Dinesh Manocha
Keyword(s):  
Mobile Robot ◽  
Closed Circuit ◽  
Healthcare Personnel ◽  
Depth Camera ◽  
Autonomous Mobile Robot ◽  
Thermal Camera ◽  
Social Distancing ◽  
Novel Method ◽  
Indoor Scenarios ◽  
Cctv Camera

Observing social/physical distancing norms between humans has become an indispensable precaution to slow down the transmission of COVID-19. We present a novel method to automatically detect pairs of humans in a crowded scenario who are not maintaining social distancing, i.e. about 2 meters of space between them using an autonomous mobile robot and existing CCTV (Closed-Circuit TeleVision) cameras. The robot is equipped with commodity sensors, namely an RGB-D (Red Green Blue—Depth) camera and a 2-D lidar to detect social distancing breaches within their sensing range and navigate towards the location of the breach. Moreover, it discreetly alerts the relevant people to move apart by using a mounted display. In addition, we also equip the robot with a thermal camera that transmits thermal images to security/healthcare personnel who monitors COVID symptoms such as a fever. In indoor scenarios, we integrate the mobile robot setup with a static wall-mounted CCTV camera to further improve the number of social distancing breaches detected, accurately pursuing walking groups of people etc. We highlight the performance benefits of our robot + CCTV approach in different static and dynamic indoor scenarios.

Robust Vision-Based Monitoring of Indoor Environments by an Autonomous Mobile Robot

2007 ◽  
Author(s):  
Donato Di Paola ◽  
Annalisa Milella ◽  
Grazia Cicirelli ◽  
Arcangelo Distante
Keyword(s):  
Mobile Robot ◽  
Indoor Environment ◽  
Experimental Tests ◽  
Indoor Environments ◽  
Autonomous Mobile Robot ◽  
Environment Monitoring ◽  
Inference System ◽  
Sift Algorithm ◽  
Matching Process ◽  
Logic Inference

This paper presents a novel vision-based approach for indoor environment monitoring by a mobile robot. The proposed system is based on computer vision methods to match the current scene with a stored one, looking for new or removed objects. The matching process uses both keypoint features and colour information. A PCA-SIFT algorithm is employed for feature extraction and matching. Colour-based segmentation is performed separately, using HSV coding. A fuzzy logic inference system is applied to fuse information from both steps and decide whether a significant variation of the scene has occurred. Results from experimental tests demonstrate the feasibility of the proposed method in robot surveillance applications.

scholarly journals Fuzzy Logic Based Control for Autonomous Mobile Robot Navigation

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Hajer Omrane ◽  
Mohamed Slim Masmoudi ◽  
Mohamed Masmoudi
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Fuzzy Controller ◽  
Mobile Robot Navigation ◽  
Infrared Range ◽  
Indoor Environments ◽  
Autonomous Mobile Robot ◽  
Ir Sensors ◽  
Navigation Algorithms ◽  
Trajectory Tracking Controller

This paper describes the design and the implementation of a trajectory tracking controller using fuzzy logic for mobile robot to navigate in indoor environments. Most of the previous works used two independent controllers for navigation and avoiding obstacles. The main contribution of the paper can be summarized in the fact that we use only one fuzzy controller for navigation and obstacle avoidance. The used mobile robot is equipped with DC motor, nine infrared range (IR) sensors to measure the distance to obstacles, and two optical encoders to provide the actual position and speeds. To evaluate the performances of the intelligent navigation algorithms, different trajectories are used and simulated using MATLAB software and SIMIAM navigation platform. Simulation results show the performances of the intelligent navigation algorithms in terms of simulation times and travelled path.

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