Advanced Control Topics on Robotic Vehicles

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 85838

Special Issue Editors


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Guest Editor
Department of Digital Industry Technologies, School of Science, National and Kapodistrian University of Athens, Euripus Campus, 34400 Euboea, Greece
Interests: modeling and control of robotic systems; control of switching systems; control of networked systems; cyberphysical systems; motor control; vehicular control; autonomous systems; distributed control; process modeling

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Guest Editor
College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
Interests: array signal processing; analysis and control on sound and vibration; mechanical systems and signal processing; com-pressive sensing; machine learning
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Special Issue Information

Dear Colleagues,

Robotic vehicles have attracted significant attention in recent years due to their potential applications in various fields. To achieve the successful operation of such robotic systems, there are still many aspects that need to be examined and problems to be resolved. These aspects and problems are related to modeling, controller design, communications, and artificial intelligence. The aim of examining these types of problems is, among others, (a) to produce more accurate mathematical representations of robotic vehicles, (b) to design appropriate control schemes such that the vehicles perform more precise maneuvers despite the presence of uncertainties, disturbances and measurement noise as well as the influence of networks in communication-based controller implementations, and (c) to improve the functioning of robotic vehicles through the use of advanced algorithms in perception and sensing, navigation and path planning, control systems design, decision-making, and collaborative driving. The main development branch of such algorithms is based on the use of artificial intelligence and machine learning approaches, aiming toward the development of fully autonomous systems, and covering areas such as controller design and tuning, navigation and motion control, localization and mapping, and multimodal sensing. To achieve this goal, MDPI's journal "Information", having a leading role in information science and technology, data, knowledge, and communication systems, is introducing a Special Issue focusing on advanced control topics for robotic vehicles. The purpose of this Special Issue is to draw in new research and discuss problems that are directly related to the performance and operation of robotic vehicles. This Special Issue covers all aspects of advanced control on robotic vehicles including:

  • AI- and machine learning-based methods;
  • Computational intelligence;
  • Intelligent and cognitive robotic systems;
  • Control techniques;
  • Network controlled systems;
  • Remote control;
  • Communication systems and networks;
  • Sensors and wireless sensor networks in robotic applications;
  • Signal processing;
  • Swarm robotics and consensus;
  • Multi-robot systems;
  • Cooperating systems;
  • Dynamics and kinematics;
  • Navigation, path planning, and localization;
  • Visual perception;
  • Autonomous navigation;
  • Human–robot interaction and social robotics;
  • Simulation;
  • Robotic vehicle applications;
  • UAVs, UGVs, USVs, and UUVs.

Dr. Nikolaos D. Kouvakas
Prof. Dr. Zhigang Chu
Guest Editors

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Keywords

  • robot control
  • communications and network controlled systems
  • emerging applications
  • artificial intelligence
  • information extraction
  • autonomy, mobility, and manipulation
  • multi-agent systems and consensus
  • human–robot interaction
  • robotic vision

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Published Papers (2 papers)

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Research

18 pages, 6694 KiB  
Article
Multi-Robot Navigation System Design Based on Proximal Policy Optimization Algorithm
by Ching-Chang Wong, Kun-Duo Weng and Bo-Yun Yu
Information 2024, 15(9), 518; https://doi.org/10.3390/info15090518 - 26 Aug 2024
Viewed by 83840
Abstract
The more path conflicts between multiple robots, the more time it takes to avoid each other, and the more navigation time it takes for the robots to complete all tasks. This study designs a multi-robot navigation system based on deep reinforcement learning to [...] Read more.
The more path conflicts between multiple robots, the more time it takes to avoid each other, and the more navigation time it takes for the robots to complete all tasks. This study designs a multi-robot navigation system based on deep reinforcement learning to provide an innovative and effective method for global path planning of multi-robot navigation. It can plan paths with fewer path conflicts for all robots so that the overall navigation time for the robots to complete all tasks can be reduced. Compared with existing methods of global path planning for multi-robot navigation, this study proposes new perspectives and methods. It emphasizes reducing the number of path conflicts first to reduce the overall navigation time. The system consists of a localization unit, an environment map unit, a path planning unit, and an environment monitoring unit, which provides functions for calculating robot coordinates, generating preselected paths, selecting optimal path combinations, robot navigation, and environment monitoring. We use topological maps to simplify the map representation for multi-robot path planning so that the proposed method can perform path planning for more robots in more complex environments. The proximal policy optimization (PPO) is used as the algorithm for deep reinforcement learning. This study combines the path selection method of deep reinforcement learning with the A* algorithm, which effectively reduces the number of path conflicts in multi-robot path planning and improves the overall navigation time. In addition, we used the reciprocal velocity obstacles algorithm for local path planning in the robot, combined with the proposed global path planning method, to achieve complete and effective multi-robot navigation. Some simulation results in NVIDIA Isaac Sim show that for 1000 multi-robot navigation tasks, the maximum number of path conflicts that can be reduced is 60,375 under nine simulation conditions. Full article
(This article belongs to the Special Issue Advanced Control Topics on Robotic Vehicles)
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20 pages, 18713 KiB  
Article
Safe Coverage Control of Multi-Agent Systems and Its Verification in ROS/Gazebo Environment
by Fidelia Chaitra Siri, Jie Song and Mikhail Svinin
Information 2024, 15(8), 462; https://doi.org/10.3390/info15080462 - 2 Aug 2024
Viewed by 1278
Abstract
This paper presents safe coverage control algorithms for multi-agent systems, integrating Centroidal Voronoi Tessellation (CVT) and control barrier functions (CBFs). This study aims to ensure safety and spatial optimization by combining CVT and CBFs for obstacle avoidance, testing the controller through simulations, and [...] Read more.
This paper presents safe coverage control algorithms for multi-agent systems, integrating Centroidal Voronoi Tessellation (CVT) and control barrier functions (CBFs). This study aims to ensure safety and spatial optimization by combining CVT and CBFs for obstacle avoidance, testing the controller through simulations, and verifying the results with RT mobile robots. This development of safe coverage control algorithms for multi-agent systems achieves a synergy that addresses both safety and spatial optimization, which are crucial for multi-agent systems. The proposed CVT-CBF-based controller has been validated through extensive simulations in the ROS/Gazebo environment and physical experiments with RT robots, demonstrating its effectiveness in achieving collision-free coverage. This study provides a comprehensive understanding of the integration of CVT and CBFs for safe coverage control with obstacle avoidance in multi-agent systems, highlighting both its potential and the necessary considerations for practical deployment. Full article
(This article belongs to the Special Issue Advanced Control Topics on Robotic Vehicles)
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