QoS and Energy-Aware Run-Time Adaptation for Mobile Robotic Missions: A Learning Approach

2019 ◽  
Author(s):  
Dinh-Khanh Ho ◽  
Karim Ben Chehida ◽  
Benoit Miramond ◽  
Michel Auguin
Keyword(s):  
Learning Approach ◽  
Energy Aware ◽  
Run Time ◽  
Robotic Missions

Energy-Aware Motion and Disparity Estimation System for 3D-HEVC With Run-Time Adaptive Memory Hierarchy

2019 ◽  
Vol 29 (6) ◽  
pp. 1878-1892 ◽  
Author(s):  
Vladimir Afonso ◽  
Ruhan Conceicao ◽  
Mario Saldanha ◽  
Luciano Braatz ◽  
Murilo Perleberg ◽  
...  
Keyword(s):  
Memory Hierarchy ◽  
Disparity Estimation ◽  
Adaptive Memory ◽  
Energy Aware ◽  
Run Time ◽  
Estimation System

Energy-aware run-time mapping for homogeneous NoC

2010 ◽  
Author(s):  
Guang Sun ◽  
Yong Li ◽  
Yuanyuan Zhang ◽  
Li Su ◽  
Depeng Jin ◽  
...  
Keyword(s):  
Energy Aware ◽  
Run Time

Learning-Based Adaptive Management of QoS and Energy for Mobile Robotic Missions

2019 ◽  
Vol 13 (04) ◽  
pp. 513-539
Author(s):  
Dinh-Khanh Ho ◽  
Karim Ben Chehida ◽  
Benoit Miramond ◽  
Michel Auguin
Keyword(s):  
Autonomous Navigation ◽  
Robotic Systems ◽  
Energy Aware ◽  
Management Framework ◽  
Performance Requirements ◽  
Parameter Configuration ◽  
Mapping Parameter ◽  
Promising Solution ◽  
Robotic Missions

Mobile robotic systems are normally confronted with the shortage of on-board resources such as computing capabilities and energy, as well as significantly influenced by the dynamics of surrounding environmental conditions. This context requires adaptive decisions at run-time that react to the dynamic and uncertain operational circumstances for guaranteeing the performance requirements while respecting the other constraints. In this paper, we propose a reinforcement learning (RL)-based approach for Quality of Service QoS and energy-aware autonomous robotic mission manager. The mobile robotic mission manager leverages the idea of (RL) by monitoring actively the state of performance and energy consumption of the mission and then selecting the best mapping parameter configuration by evaluating an accumulative reward feedback balancing between QoS and energy. As a case study, we apply this methodology to an autonomous navigation mission. Our simulation results demonstrate the efficiency of the proposed management framework and provide a promising solution for the real mobile robotic systems.

Energy‐aware mobility for aerial networks: A reinforcement learning approach

2021 ◽  
Author(s):  
Elif Bozkaya ◽  
Yusuf Özçevik ◽  
Mertkan Akkoç ◽  
Muhammed Raşit Erol ◽  
Berk Canberk
Keyword(s):  
Reinforcement Learning ◽  
Learning Approach ◽  
Energy Aware

scholarly journals Machine Learning Approach for Graphical Model-Based Analysis of Energy-Aware Growth Control in Plant Factories

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 32183-32196 ◽  
Author(s):  
Yu Fujimoto ◽  
Saya Murakami ◽  
Nanae Kaneko ◽  
Hideki Fuchikami ◽  
Toshirou Hattori ◽  
...  
Keyword(s):  
Machine Learning ◽  
Graphical Model ◽  
Growth Control ◽  
Learning Approach ◽  
Energy Aware ◽  
Model Based ◽  
Machine Learning Approach ◽  
Model Based Analysis
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