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Artificial Intelligence and Machine Learning in Energy-Optimized Robotic Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (10 December 2023) | Viewed by 2295

Special Issue Editors

Institute of Automatic Control and Robotics, Warsaw University of Technology, 02-525 Warsaw, Poland
Interests: mobile robotics; computer vision; artificial intelligence
Institute of Automatic Control and Robotics, Warsaw University of Technology, 02-525 Warsaw, Poland
Interests: sensors; measurement systems; control and instrumentation; inertial navigation sensors; MEMS accelerometers; mechatronics; system analysis
Special Issues, Collections and Topics in MDPI journals
Faculty of Cybernetics, Military University of Technology, 00-908 Warsaw, Poland
Interests: operations research; discrete optimization; path planning for UAVs

Special Issue Information

Dear Colleagues,

Currently, the field of mobile robotics is developing rapidly, and there has been great interest in the practical application of mobile robots. Carts that function in hospitals, as well as autonomous hoovers, lawnmowers and cars, are being built. New robot designs are being created, and very sophisticated sensory systems are being implemented.

It is a significant challenge for robots to work in unfamiliar, unstructured and dynamic environments. Therefore, it is necessary to build new architectures for control and navigation systems in order to help realize safety and optimal performance. In the case of social robots, the method of communication is of great importance. Robots should also, like living organisms, create a semantic model of the surrounding world, localize themselves and plan optimal actions.

Optimal action planning and localization are also critical challenges in energy-centric robotic systems. Action planning involves determining the most efficient sequence of actions for a robotic system to achieve its goals while considering energy constraints. Localization refers to the ability of the robotic system to perceive and understand its position in the environment accurately. By integrating energy-centric knowledge representation and decision support into these processes, robotic systems can dynamically adjust their action plans and optimize their movements to conserve energy.

At the same time, the development of artificial intelligence methods is currently underway. The use of deep learning methods, reinforcement learning methods, and image analysis methods has been highly successful. Artificial intelligence methods are an essential component of mobile robot navigation systems. These methods are used in algorithms for data analysis, object recognition, map construction, path planning, control systems, localization and energy-efficient control systems.

This Special Issue aims to contribute to the research on state-of-the-art artificial intelligence in energy-aware navigation systems and present the current applications of mobile robots.

We will look for various techniques, algorithms and methodologies that enable robots to effectively utilize energy resources, minimize energy wastage and adapt to changing energy conditions.

The Guest Editors invite papers related to the following topics, but the list is non-exhaustive:

  • Energy-aware robotics;
  • Energy-aware navigation;
  • Energy-efficient control systems;
  • Knowledge representation;
  • Decision support systems;
  • Action planning;
  • Localization;
  • Sensory systems;
  • Robot construction.

Prof. Dr. Barbara Siemiatkowska
Prof. Dr. Igor Korobiichuk
Dr. Wojciech Stecz
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mobile robot navigation
  • energy-efficient control strategies
  • optimization
  • action and path planning

Published Papers (2 papers)

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Research

16 pages, 14710 KiB  
Article
Semantic-Aware Path Planning with Hexagonal Grids and Vehicle Dynamic Constraints
Energies 2023, 16(13), 5127; https://doi.org/10.3390/en16135127 - 03 Jul 2023
Viewed by 786
Abstract
The article presents a navigation system that utilizes a semantic map created on a hexagonal grid. The system plans the path by incorporating semantic and metric information while considering the vehicle’s dynamic constraints. The article concludes by discussing a low-level control algorithm used [...] Read more.
The article presents a navigation system that utilizes a semantic map created on a hexagonal grid. The system plans the path by incorporating semantic and metric information while considering the vehicle’s dynamic constraints. The article concludes by discussing a low-level control algorithm used in the system. This solution’s advantages include using a semantic map on a hexagonal grid, which enables more efficient and accurate navigation. Creating a map of allowable speeds based on the semantic map provides an additional layer of information that can help optimize the vehicle’s trajectory. Incorporating both semantic and metric information in the path-planning process leads to a more precise and tailored navigation solution that accounts for the vehicle’s capabilities and the environment it is operating in. Finally, the low-level control algorithm ensures that the vehicle follows the planned trajectory while considering real-time sensor data and other factors affecting its movement. Through this article, we aim to provide insights into the cutting-edge advancements in path planning techniques and shed light on the potential of combining hexagonal grids, vehicle dynamics constraints, and semantic awareness. These innovations have the potential to revolutionize autonomous navigation systems, enabling vehicles to navigate complex environments with greater efficiency, safety, and adaptability. Full article
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27 pages, 10632 KiB  
Article
A New Energy-Efficient Approach to Planning Pick-and-Place Operations
Energies 2022, 15(23), 8795; https://doi.org/10.3390/en15238795 - 22 Nov 2022
Viewed by 1026
Abstract
Pick-and-place operations are basic, and are currently the most common for robots operating in the industry. Massive applications makes it reasonable to ask whether, and to what extent these operations are realised in a way that guarantees rational energy consumption. In many cases, [...] Read more.
Pick-and-place operations are basic, and are currently the most common for robots operating in the industry. Massive applications makes it reasonable to ask whether, and to what extent these operations are realised in a way that guarantees rational energy consumption. In many cases, the answer to such a question is neither positive nor known. Therefore, this paper attempts to present a rational and systematic approach to the low-energy pick-and-place operations performed by robots. This paper describes a new approach for the robot’s tool centre point path planning, which enables the minimisation of energy consumption wherein productivity in preserved, and where care is taken for the persistence of the critical mechanical components of the robot cooperating with the autonomous mobile platform. The effectiveness of the described approach has been proven from the results of the theoretical, simulation, experimental and implementation tests carried out using an industrial articulated robot with six degrees of freedom. Full article
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