Path planning and real time positioning algorithm of intelligent robot based on cloud computing

2022 ◽  
Author(s):  
Ran Zhao ◽  
Lingnan Zhou
Keyword(s):  
Cloud Computing ◽  
Path Planning ◽  
Real Time ◽  
Intelligent Robot ◽  
Positioning Algorithm

scholarly journals New Algorithm for Autonomous Dynamic Path Planning in Real-Time Intelligent Robot Car

2017 ◽  
Vol 14 (11) ◽  
pp. 5499-5507
Author(s):  
Aqeel A. Mohammed ◽  
Ishak Aris ◽  
Mohd Khair Hassan ◽  
Noor Ain Kamsani
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Intelligent Robot ◽  
Dynamic Path Planning ◽  
Dynamic Path

scholarly journals Indoor Path-Planning Algorithm for UAV-Based Contact Inspection

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 642
Author(s):  
Luis Miguel González de Santos ◽  
Ernesto Frías Nores ◽  
Joaquín Martínez Sánchez ◽  
Higinio González Jorge
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Execution Time ◽  
Point Cloud ◽  
Indoor Environments ◽  
Calculation Algorithm ◽  
Time Restrictions ◽  
Planning Algorithm ◽  
Inspection Tasks ◽  
Path Planning Algorithm

Nowadays, unmanned aerial vehicles (UAVs) are extensively used for multiple purposes, such as infrastructure inspections or surveillance. This paper presents a real-time path planning algorithm in indoor environments designed to perform contact inspection tasks using UAVs. The only input used by this algorithm is the point cloud of the building where the UAV is going to navigate. The algorithm is divided into two main parts. The first one is the pre-processing algorithm that processes the point cloud, segmenting it into rooms and discretizing each room. The second part is the path planning algorithm that has to be executed in real time. In this way, all the computational load is in the first step, which is pre-processed, making the path calculation algorithm faster. The method has been tested in different buildings, measuring the execution time for different paths calculations. As can be seen in the results section, the developed algorithm is able to calculate a new path in 8–9 milliseconds. The developed algorithm fulfils the execution time restrictions, and it has proven to be reliable for route calculation.

Real-Time Path Planning with Virtual Magnetic Fields

2021 ◽  
pp. 1-1
Author(s):  
Majda Moussa ◽  
Giovanni Beltrame
Keyword(s):  
Magnetic Fields ◽  
Path Planning ◽  
Real Time ◽  
Time Path

scholarly journals Addressing Real-Time Demands for Robotic Path Planning Systems: A Routing Protocol Approach

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mohammed Baziyad ◽  
Mohamed Saad ◽  
Raouf Fareh ◽  
Tamer Rabie ◽  
Ibrahim Kamel
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Routing Protocol ◽  
Planning Systems ◽  
Time Demands

scholarly journals Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

2021 ◽  
Vol 11 (7) ◽  
pp. 2925
Author(s):  
Edgar Cortés Gallardo Medina ◽  
Victor Miguel Velazquez Espitia ◽  
Daniela Chípuli Silva ◽  
Sebastián Fernández Ruiz de las Cuevas ◽  
Marco Palacios Hirata ◽  
...  
Keyword(s):  
Neural Networks ◽  
Cloud Computing ◽  
Path Planning ◽  
Object Detection ◽  
Short Term Memory ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Computational Time ◽  
End To End ◽  
On The Road

Autonomous vehicles are increasingly becoming a necessary trend towards building the smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection, amongst others. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that also considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper reviews the latest techniques towards creating our own end-to-end autonomous vehicle system, considering the state-of-the-art methods on object detection, and the possible incorporation of distributed systems and parallelization to deploy these methods. Our findings show that while techniques such as convolutional neural networks, recurrent neural networks, and long short-term memory can effectively handle the initial detection and path planning tasks, more efforts are required to implement cloud computing to reduce the computational time that these methods demand. Additionally, we have mapped different strategies to handle the parallelization task, both within and between the networks.

scholarly journals Real Time Path Planning of Robot using Deep Reinforcement Learning

2020 ◽  
Vol 53 (2) ◽  
pp. 15602-15607
Author(s):  
Jeevan Raajan ◽  
P V Srihari ◽  
Jayadev P Satya ◽  
B Bhikkaji ◽  
Ramkrishna Pasumarthy
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Real Time ◽  
Time Path

Improving latency in Internet-of-Things and cloud computing for real-time data transmission: a systematic literature review (SLR)

2021 ◽  
Author(s):  
Saurabh Shukla ◽  
Mohd. Fadzil Hassan ◽  
Duc Chung Tran ◽  
Rehan Akbar ◽  
Irving Vitra Paputungan ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Literature Review ◽  
Real Time ◽  
Systematic Literature Review ◽  
Data Transmission ◽  
Time Data ◽  
Real Time Data
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