Designing and Prototyping Surveillance Robot with Self-Protection Using Nail Gun

Sebastian, Novandri; Erwin, Erwin; Listijorini, Erny; Dwinanto,

doi:10.4028/www.scientific.net/amm.493.401

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…• Novandri et al [5] presented the design and development of a surveillance robot with the ability to defend itself by deploying nail guns. This robot can also use Wi-Fi protocol to send audio and visual data.…”

Section: Previous Researchesmentioning

confidence: 99%

Intelligent Surveillance Using Robot Eye and Sensor System

Swamy,

Reddy,

Chandrasekhar

2023

AMM

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Majority of the advancements, which have been made in the field of robotics are employed in household applications. Surveillance systems are used in guarding and security applications of household and workplaces. This paper presents an advanced surveillance system which integrates sensor system, robot technology and intelligent programming. Robotic surveillance system can be used to replace human guards in dangerous surveillance conditions. Several robots have been developed to assist in these operations. But the full scale employment of robots in crucial fields such as surveillance, security in defence industries and military bases is very limited. The system's overall structure is described, along with descriptions of each component. The robotic system consists of robot eyes, which are capable of sensing an object through IR sensor capability. This robotic system helps in the identification of human presence and alerting respective owners. These operations, performed by the robotic system are achieved using sensors, several number of servo motors, Arduino microcontroller. These components are placed in their respective locations, within a body which is fabricated using the 3D printing technology. The sensors in the robotic system capture the movements of objects. The robotic eyes are fitted with cameras which capture the movements. The robotic system is programmed in such a way that the robotic eyes, move in the direction of object which has been sensed by the sensors and focus on it. If there are multiple moving objects, then one robotic eye focuses on one object, while the other robotic eye focuses on another object. The captured surveillance footage can be viewed through a monitor connected to the robotic system.

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Section: Previous Researchesmentioning

confidence: 99%

Intelligent Surveillance Using Robot Eye and Sensor System

Swamy,

Reddy,

Chandrasekhar

2023

AMM

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“…Recently, vision system plays an important role in advanced robotic tasks as an irreplaceable sensor. It applies to many autonomous platforms, such as unmanned vehicle [1], mobile robot [2], surveillance platform [3][4][5], UAV [6], etc. With clearly and stably acquired vision information, those platforms can detect, recognize, measure and track the objects in the image domain.…”

Section: Introductionmentioning

confidence: 99%

A fast video stabilization algorithm with unexpected motion prediction strategy

Zhang

Leng

2015

2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)

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The vision systems mounted on mobile robots, which are moving on the rugged environment or enduring mechanical vibration, usually suffer from instable video during acquisition. In this paper, a fast video stabilization technique for robot application is presented. The proposed approach takes full advantage of intrinsic characteristics of the unwanted camera motion. There are four main steps involved in the proposed approach: feature extraction, motion estimation, motion compensation and undefined region mosaicing. We began with extracting the global features in 9 sub-regions of the current frame. The blocks with extracted features are matched in the reference frame from predicted start position. The computed motion consists of both expected and unexpected components. They are distinguished by motion smooth process and followed by motion compensation. As the result of frame shifting, some border regions are undefined. To solve this problem, inter-frames data is taken in consideration to fill those regions. A series of experiments with vivid data have been conducted. The results have revealed the effectiveness of the proposed technique.

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