Review On: The Service Robot Mathematical Model

Moshayedi, Ata Jahangir; Roy, Atanu Shuvam; Sambo, Sithembiso Khaya; Zhong, Yangwan; Liao, Liefa

doi:10.4108/airo.v1i.20

Cited by 12 publications

(10 citation statements)

References 29 publications

(38 reference statements)

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“…These algorithms have a very high accuracy of recognition and detection, Especially the CNN, Faster R-CNN and YOLOv2. These Deep Learning methods and algorithms [51] make it easier for computer vision applications to recognize and detect any object, especially for detecting vehicle license plates.…”

Section: Figure 1 Object Detection Application Vs Methodsmentioning

confidence: 99%

The Object Detection, Perspective and Obstacles In Robotic: A Review

Khan

Moshayedi

et al. 2022

EAI Endorsed Trans AI Robotics

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A few years back, when the image processing hardware and software were created, it was limited, and most of the time, object detection would fail., But as with time, the advancement in technology has significantly changed the scenario. A lot of researchers worked in this field to carry out a solution through which they can detect objects in any field, especially in the robotic domain [1]. In today's world, with so much research in the field of deep learning, it is very easy to identify and detect any object using computer vision. This paper focuses on the various deep learning technologies and algorithms through which object detection can be done. A new and advanced deep learning technology known as salient object detection has been discussed. Also, the 3D object detection and the end-to-end approach for object detection are discussed. The existing methods of deep learning through which object detection can be done. The applications in which object detection is being used and the importance of object detection. It also reports; what the predecessors have done, what problems have been solved by them, how they solved these problems, the characteristics of the predecessors' methods and their future work.

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Section: Figure 1 Object Detection Application Vs Methodsmentioning

confidence: 99%

The Object Detection, Perspective and Obstacles In Robotic: A Review

Khan

Moshayedi

et al. 2022

EAI Endorsed Trans AI Robotics

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“…In this section, the angular velocity of the left and right wheels ( ω L , ω R ) in terms of radians per second (rad/s) is considered as the input and the V lin linear velocity in terms of meters per second (m/s) and ω the angular velocity of the robot in terms of radians per second (rad/s) assumed as the output. Based on the dual wheel model [ 3 ], as shown in Figure 5(b) , R and L represent the radius of the robot wheels and the distance between the two wheels, which are connected on both sides of the robot frame. In the mentioned model, forward and reverse kinematics are inspected.…”

Section: Automated Guided Vehicle (Agv) Robot's Constructionmentioning

confidence: 99%

“…AGVs consist of three main parts: the frame and its installed device, models and sizes, and the navigation and actuator systems. Based on their application and role as service robots, these robots can be categorized in various shapes and structures, from dual-wheel to worm shapes [ 3 ]. But other than the shape, the control section is known as one of the main issues investigated by various researchers over the years.…”

Section: Introductionmentioning

confidence: 99%

Simulation and Validation of Optimized PID Controller in AGV (Automated Guided Vehicles) Model Using PSO and BAS Algorithms

Moshayedi

Sina

et al. 2022

Computational Intelligence and Neuroscience

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Automated guided vehicles (AGVs) are popular subsets of robots that come in various shapes and sizes. The group’s use in the industry ranges from applications for carrying pallets, carts, and utensils to helping the elderly or transporting medicine to hospitals. Even recently, they have been used in libraries to carry books on shelves. The main part of an AGV includes its body, motor, driver, processor, and sensors, which are more or less the same in all types of AGVs, and addons vary depending on the application and the work environment. The part that affects AGV performance is the control strategy, to which researchers have shown different approaches. Using various techniques and simulations to obtain a model is the key and can help to improve and evaluate the performance of the strategy of the robot. In this study, based on the actual design of the AGV system, all data and components are described, and the simulation is performed in MATLAB software. Then, for controlling the platform based on the PID controller tuning, four methods of Ziegler Nichols, empirical, Particle Swarm Optimization (PSO), and Beetle Antennae Searching (BAS) (optimizer) are discussed, and the results are compared in the four paths including the circle, ellipse, Spiral and 8-shaped paths by observing and testing the tuned PID parameters. Finally, a series of subsequent experiences were carried out in CoppeliaSim (VREP) as a famous robot simulator to overcome the environmental constraints for the same paths that were used in Matlab based on the extracted PID values. Based on the results, the empirical methods, PSO, and BAS errors are very close together. But in general, the BAS algorithm is the fastest, and the PSO had better performance. In general, the maximum error is linked to the path of 8 shapes and the minimum is related to circle shape one. Finally, the analysis of results in different paths in both simulators shows the same results. Therefore, concerning the limited test on the real platform and using the PID coefficients obtained from the simulation shows the model’s ability for the researchers in robotic research.

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“…And for attendance management system the most feasible design seems to be when cloud technologies [31][32][33][34][35][36][37][38][39] and service robots, deep learning etc. [40][41][42][43][44][45][46][47][48][49][50][51][52][53] are used in conjunction. Attendance management systems are not just required for schools, colleges, or educational institutions.…”

Section: Embedded Systems-based Attendance Systemsmentioning

confidence: 99%

Automation Attendance Systems Approaches: A Practical Review

Jahangir¹,

Atanu²,

Liefa³

et al. 2021

BIJIAM

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Accounting for people is the first step of every manpower-based organization in today's world. Hence, it takes up a signification amount of energy and value in the form of money from respective organizations for both implementing a suitable system for manpower management as well as maintaining that same system. Although this amount of expenditure for big organizations is near to nothing, rather just a formality, it does not hold as much truth for small organizations such as schools, colleges, and even universities to a certain degree. This is the first point. The second point for discussion is that much work has been done to solve this issue. Various technologies like Biometrics, RFID, Bluetooth, GPS, QR Code, etc., have been used to tackle the issues of attendance collection. This study paves the path for researchers by reviewing practical methods and technologies used for existing attendance systems.

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Review On: The Service Robot Mathematical Model

Cited by 12 publications

References 29 publications

The Object Detection, Perspective and Obstacles In Robotic: A Review

The Object Detection, Perspective and Obstacles In Robotic: A Review

Simulation and Validation of Optimized PID Controller in AGV (Automated Guided Vehicles) Model Using PSO and BAS Algorithms

Automation Attendance Systems Approaches: A Practical Review

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