Neural Network and ROS based Threat Detection and Patrolling Assistance

Maram, Sai Siddartha; Vishnoi, Tanuj; Pandey, Sachin

doi:10.1109/icaccp.2019.8883008

Cited by 5 publications

(2 citation statements)

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“…8 shows the messages for subscribe and publish for the adaptive nonlinear U-model based IMC in the ROS GIRONA 500 UUV architecture during simulation and real-time implementation. Rvis is a 3D ROS visualisation tool GUI for robotic platform simulation in the targeted environment such as an underwater scenario with sensor simulation [44]- [46]. Rvis can plot and display many types of data streams through ROS nodes such as cameras, underwater acoustic transducers, a manipulator arm, and other related sensors.…”

Section: Controller Node Header Programmentioning

confidence: 99%

Implementation of Nonlinear Adaptive U-Model Control Synthesis Using a Robot Operating System for an Unmanned Underwater Vehicle

et al. 2020

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Section: Controller Node Header Programmentioning

confidence: 99%

Implementation of Nonlinear Adaptive U-Model Control Synthesis Using a Robot Operating System for an Unmanned Underwater Vehicle

et al. 2020

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“…Therefore, to design an intelligent control system that replaces human labor and make the WMR able to independently assist human patrol in an outdoor environment is the primary research motivation of this study. Since the late 1990s, many papers have been presented regarding robot outdoor patrol [9][10][11][12][13]. These studies mainly focused on navigation, obstacle avoidance, path planning, road detection, and object recognition.…”

Section: Introductionmentioning

confidence: 99%

Automatic Outdoor Patrol Robot Based on Sensor Fusion and Face Recognition Methods

Chang

Juang

2021

Applied Sciences

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This study integrates path planning, fuzzy theory, neural networks, image processing, range sensors, webcam, global navigation satellite system (GNSS), and real-time kinematic (RTK) positioning system into an intelligent wheeled mobile robot (WMR) for outdoor patrolling. The robot system uses ultrasound sensors, laser sensors, and fuzzy controllers to detect obstacles and avoid them. The starting position and the goal position of the WMR in an outdoor environment are given by the GNSS RTK positioning system. Real-time position correction of the robot is performed through the differential global positioning system. The robot system applies the ant algorithm and the Dijkstra algorithm to find the shortest path for patrol tasks. The convolutional neural network image processing is utilized to identify intruders that are appearing in the patrol path. When the WMR detects an intruder by the face detection and recognition methods, the robot captures the photo of this person by the webcam and acquires the location information of this person by the RTK positioning system. Then the WMR sends the location and photo of the intruder to the control center by Wi-Fi and asks for help.

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