Control of Multiple Robots Using Vision Sensors

2017 ◽  
Author(s):  
Miguel Aranda ◽  
Gonzalo López-Nicolás ◽  
Carlos Sagüés
Keyword(s):  
Multiple Robots ◽  
Vision Sensors

Cognitive analysis methods for control of multiple robots: Robotics on $5 a day

2004 ◽  
Author(s):  
Roger A. Chadwick ◽  
Douglas J. Gillan ◽  
Dominic Simon ◽  
Skye Pazuchanics
Keyword(s):  
Multiple Robots ◽  
Cognitive Analysis ◽  
Analysis Methods

Control and Random Searching with Multiple Robots

2005 ◽  
Author(s):  
A. J. Healey ◽  
Y. Kim
Keyword(s):  
Multiple Robots

scholarly journals Night vision obstacle detection and avoidance based on Bio-Inspired Vision Sensors

2020 ◽  
Author(s):  
Jawad N. Yasin ◽  
Sherif A. S. Mohamed ◽  
Mohammad-hashem Haghbayan ◽  
Jukka Heikkonen ◽  
Hannu Tenhunen ◽  
...  
Keyword(s):  
Obstacle Detection ◽  
Night Vision ◽  
Vision Sensors

scholarly journals Autonomous Quadrotor Flight despite Rotor Failure with Onboard Vision Sensors: Frames vs. Events

2021 ◽  
pp. 1-1
Author(s):  
Sihao Sun ◽  
Giovanni Cioffi ◽  
Coen De Visser ◽  
Davide Scaramuzza
Keyword(s):  
Vision Sensors

scholarly journals Dynamic Resource-Aware Corner Detection for Bio-Inspired Vision Sensors

2021 ◽  
Author(s):  
Sherif A.S. Mohamed ◽  
Jawad N. Yasin ◽  
Mohammad-Hashem Haghbayan ◽  
Antonio Miele ◽  
Jukka Heikkonen ◽  
...  
Keyword(s):  
Corner Detection ◽  
Vision Sensors ◽  
Dynamic Resource ◽  
Resource Aware

scholarly journals Collision Avoidance on Unmanned Aerial Vehicles Using Neural Network Pipelines and Flow Clustering Techniques

2021 ◽  
Vol 13 (13) ◽  
pp. 2643
Author(s):  
Dário Pedro ◽  
João P. Matos-Carvalho ◽  
José M. Fonseca ◽  
André Mora
Keyword(s):  
Collision Avoidance ◽  
Autonomous Vehicles ◽  
High Demand ◽  
Novel Approach ◽  
Vision Sensors ◽  
Learning Techniques ◽  
Stationary Objects ◽  
Computationally Intensive ◽  
Unmanned Autonomous Vehicles ◽  
Positive Results

Unmanned Autonomous Vehicles (UAV), while not a recent invention, have recently acquired a prominent position in many industries, and they are increasingly used not only by avid customers, but also in high-demand technical use-cases, and will have a significant societal effect in the coming years. However, the use of UAVs is fraught with significant safety threats, such as collisions with dynamic obstacles (other UAVs, birds, or randomly thrown objects). This research focuses on a safety problem that is often overlooked due to a lack of technology and solutions to address it: collisions with non-stationary objects. A novel approach is described that employs deep learning techniques to solve the computationally intensive problem of real-time collision avoidance with dynamic objects using off-the-shelf commercial vision sensors. The suggested approach’s viability was corroborated by multiple experiments, firstly in simulation, and afterward in a concrete real-world case, that consists of dodging a thrown ball. A novel video dataset was created and made available for this purpose, and transfer learning was also tested, with positive results.

scholarly journals Rotation Estimation: A Closed-Form Solution Using Spherical Moments

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4958
Author(s):  
Hicham Hadj-Abdelkader ◽  
Omar Tahri ◽  
Houssem-Eddine Benseddik
Keyword(s):  
Image Processing ◽  
Closed Form ◽  
Synthetic Data ◽  
Closed Form Solution ◽  
Form Solution ◽  
Unified Model ◽  
Experimental Results ◽  
Motion Information ◽  
Vision Sensors ◽  
Spherical Projection

Photometric moments are global descriptors of an image that can be used to recover motion information. This paper uses spherical photometric moments for a closed form estimation of 3D rotations from images. Since the used descriptors are global and not of the geometrical kind, they allow to avoid image processing as features extraction, matching, and tracking. The proposed scheme based on spherical projection can be used for the different vision sensors obeying the central unified model: conventional, fisheye, and catadioptric. Experimental results using both synthetic data and real images in different scenarios are provided to show the efficiency of the proposed method.

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