Emerging Applications of AI and Robotics in Smart Grid and Energy Sectors

Topic Information

Dear Colleagues,

With the increasing decentralization and digitization of the power grid, it is becoming more challenging to manage large numbers of grid participants while maintaining the grid balance. This requires evaluation and analysis of enormous amount of data. Artificial intelligence (AI), especially machine learning and deep learning, help the energy industry to be more efficient and secure in examining and assessing the data. AI applications in energy will benefit and assist this sector in various aspects e.g., in power theft and energy fraud detection, energy trading, energy storage, predictive analytics, grid management and efficiency, microgrids and smart grids, and customer engagement. AI can also facilitate and speed up the integration of renewables. Robotics has embarked upon its transformative journey and will help to improvise AI usage in the energy industry in ways that have great potential for foreseeable future designs of the energy system. This Topic covers AI-related new technologies that create broad scope for the energy field to improve efficiency, optimise performance, drive innovation, and accelerate growth.

Potential topics include but are not limited to the following:

• Big data
• Artificial intelligence and IoT
• Intelligent sensors
• Robot sensing systems
• Signal processing
• Machine learning and deep learning
• Micro-synchrophasor measurement units
• Smart grid
• Electrical power distribution system
• Renewables
• Smart infrastructure
• Low carbon and net zero

Dr. Maitreyee Dey
Dr. Soumya Prakash Rana
Prof. Dr. Sandra Dudley
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	-	-	2020	20.8 Days	CHF 1600
Buildings buildings	3.8	3.1	2011	14.6 Days	CHF 2600
Infrastructures infrastructures	2.6	4.3	2016	16.9 Days	CHF 1800
Robotics robotics	3.7	5.9	2012	17.3 Days	CHF 1800
Sensors sensors	3.9	6.8	2001	17 Days	CHF 2600
Smart Cities smartcities	6.4	8.5	2018	20.2 Days	CHF 2000
Energies energies	3.2	5.5	2008	16.1 Days	CHF 2600

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Published Papers (1 paper)

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All AI Buildings Infrastructures Robotics Sensors Smart Cities Energies

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22 pages, 6355 KiB  

Open AccessArticle

A Novel Sampling-Based Optimal Motion Planning Algorithm for Energy-Efficient Robotic Pick and Place

by Md Moktadir Alam, Tatsushi Nishi, Ziang Liu and Tomofumi Fujiwara

Energies 2023, 16(19), 6910; https://doi.org/10.3390/en16196910 - 30 Sep 2023

Cited by 1 | Viewed by 884

Abstract

Energy usage in robotic applications is rapidly increasing as industrial robot installations grow. This research introduces a novel approach, using the rapidly exploring random tree (RRT)-based scheme for optimizing the robot’s motion planning and minimizing energy consumption. Sampling-based algorithms for path planning, such [...] Read more.

Energy usage in robotic applications is rapidly increasing as industrial robot installations grow. This research introduces a novel approach, using the rapidly exploring random tree (RRT)-based scheme for optimizing the robot’s motion planning and minimizing energy consumption. Sampling-based algorithms for path planning, such as RRT and its many other variants, are widely used in robotic motion planning due to their efficiency in solving complex high-dimensional problems efficiently. However, standard versions of these algorithms cannot guarantee that the generated trajectories are always optimum and mostly ignore the energy consumption in robotic applications. This paper proposes an energy-efficient industrial robotics motion planning approach using the novel flight cost-based RRT (FC-RRT*) algorithm in pick-and-place operation to generate nodes in a predetermined direction and then calculate energy consumption using the circle point method. After optimizing the motion trajectory, power consumption is computed for the rotary axes of a six degree of freedom (6DOF) serial type of industrial robot using the work–energy hypothesis for the rotational motion of a rigid body. The results are compared to the traditional RRT and RRT* (RRT-star) algorithm as well as the kinematic solutions. The experimental results of axis indexing tests indicate that by employing the sampling-based FC-RRT* algorithm, the robot joints consume less energy (1.6% to 16.5% less) compared to both the kinematic solution and the conventional RRT* algorithm. Full article

(This article belongs to the Topic Emerging Applications of AI and Robotics in Smart Grid and Energy Sectors)

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