Machine Learning for Data-Driven Control of Robots

Givigi, Sidney N.; Jardine, Peter T.

doi:10.1109/mpot.2018.2824398

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…Second, colossal amounts of data are available as the fuels for model training [37,38]. The quality and the scale of the datasets are the determinants of the robust performance of prediction/classification [39,40].…”

Section: The Rise Of Aimentioning

confidence: 99%

Quo vadis artificial intelligence?

Jiang

Luo

et al. 2022

Discov Artif Intell

274

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The study of artificial intelligence (AI) has been a continuous endeavor of scientists and engineers for over 65 years. The simple contention is that human-created machines can do more than just labor-intensive work; they can develop human-like intelligence. Being aware or not, AI has penetrated into our daily lives, playing novel roles in industry, healthcare, transportation, education, and many more areas that are close to the general public. AI is believed to be one of the major drives to change socio-economical lives. In another aspect, AI contributes to the advancement of state-of-the-art technologies in many fields of study, as helpful tools for groundbreaking research. However, the prosperity of AI as we witness today was not established smoothly. During the past decades, AI has struggled through historical stages with several winters. Therefore, at this juncture, to enlighten future development, it is time to discuss the past, present, and have an outlook on AI. In this article, we will discuss from a historical perspective how challenges were faced on the path of revolution of both the AI tools and the AI systems. Especially, in addition to the technical development of AI in the short to mid-term, thoughts and insights are also presented regarding the symbiotic relationship of AI and humans in the long run.

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Section: The Rise Of Aimentioning

confidence: 99%

Quo vadis artificial intelligence?

Jiang

Luo

et al. 2022

Discov Artif Intell

274

View full text Add to dashboard Cite

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“…It is even more challenging when navigating multiple UAVs safely and efficiently in a large-scale airspace with both static and dynamic obstacles under wind disturbances. Data-driven based control methods (e.g., [122]) may have a great potential to enable safe and efficient UAV operations in a large-scale dynamic civilian environment. However, accidents may happen when the training data is noisy or the training process does not fit the real-world scenarios [123] and rigorous stability proof is usually unaccessible [124].…”

Section: B Continuing Researchmentioning

confidence: 99%

Unmanned Aerial Vehicles: Control Methods and Future Challenges

Zuo

Liu

Han

et al. 2022

IEEE/CAA J. Autom. Sinica

128

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“…In this work, we propose the addition of deep learning (17) from machine learning. (18) With the repeated calls made by the user to the iTrashCan, the paths from each call can be stored to the cloud database with four input parameters, namely, the execution time, start point, end point and path. Using the LSTM approach, (19) an optimal output can be derived, that is, a predicted optimal traversal path, (20) as shown in Fig.…”

Section: Deep Learning For Optimized Pathmentioning

confidence: 99%

Smart Trash Can Robot System with Integration of Internet of Things and Mobile Applications

Chang¹,

Tsai²,

Lyu³

et al. 2019

Sensors and Materials

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positioning, smart trash can robot, intelligent path planning People have become busier in the modern world with both work and housework. In addition to the different types of robotic cleaner, it would be quite helpful to have a smart robotic trash collection and dumping system that provides on-call services so that a user does not need to physically get up and put trash into a trash can. As the bin reaches its maximum capacity, it also dumps the trash automatically without the user's instructions. Available smart trash cans are usually focused on determining if the trash is recyclable. Most trash cans also do not have the capability to move autonomously. In this research, we utilize fingerprint mapping for wireless indoor positioning towards the implementation of an autonomous vehicle with a mounted trash can. The user may make an indoor call to the trash-can-mounted vehicle via a mobile application under an IoT and cloud computing environment. The vehicle can position itself in front of the user for trash collection through automatic obstacle avoidance navigation with smart path planning from deep learning. The system can also monitor the amount of accumulated trash and dump trash at a fixed location before returning to the start point. This research on a smart trash-collecting robot can provide significant assistance to people who are busy, those with impaired or limited movements, and the elderly.

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Machine Learning for Data-Driven Control of Robots

Cited by 6 publications

References 3 publications

Quo vadis artificial intelligence?

Quo vadis artificial intelligence?

Unmanned Aerial Vehicles: Control Methods and Future Challenges

Smart Trash Can Robot System with Integration of Internet of Things and Mobile Applications

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