Robot Motion Planning

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 1480

Special Issue Editors

Department of Computer Science, University of Colorado Springs, Colorado Springs, CO 80918, USA
Interests: swarm and cognitive robotics; autonomous mission planning and management systems; cognitive and computational neuroscience; autism biomarker discovery; brain–computer interface (BCI); image processing and signal processing; machine learning; deep learning and big data analytics; computational intelligence
School of Computer Sciences, Universiti Sains Malaysia, Minden 11800, Malaysia
Interests: artificial intelligence; computer vision; deep learning; machine learning; mobile robotics; optimization; path planning
School of Information Technology, Faculty of Science Engineering & Built Environment, Deakin University, Geelong, VIC 3220, Australia
Interests: autonomous arial, underwater and surface vehicles; autonomy and situational awareness; mission and path planning; AI-based decision-making frameworks
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Special Issue Information

Dear Colleagues,

Advancements in robot hardware and software technologies have made robots an excellent choice for sophisticated operations to support or substitute human activities in different sectors. Robot motion planning has been proposed for a large spectrum of applications, ranging from automation and conventional industrial applications to autonomous robots, such as industrial and factory robots, self-driving cars, as well as unmanned aerial, underwater, surface, and ground vehicles. Motion planning is a general term in robotics referring to single/multi-vehicle path planning, route planning, and trajectory tracking, and is often defined as a process of breaking down the motion into discrete moves that satisfy a set of constraints and different aspects of the movement.

A significant hurdle in robots’ autonomous motion planning is devising an intelligent mechanism enabling the robots to make plans and human-like decisions in various situations. Regardless of the specific mechanical setup, a robot’s motion is restricted by various factors in performing generic/specific tasks when interacting with the known or unknown and uncertain environments.

This Special Issue focuses on the frontier research in intelligent motion planning, which constitutes single- and multi-robot path planning, route planning, and trajectory tracking to attain persistent autonomy in navigation and motion planning. This Special Issue aims to present the latest advances in this area, with particular emphasis on the planning, design, dynamics, kinematics, sensing capabilities, communication strategies, navigation, control, and application of autonomous robots.

Relevant areas covered in this Special Issue include (but are not limited to):

  • Planning and decision making;
  • Multi-agent motion planning;
  • Path formation (formation planning);
  • Modular, hybrid, and reconfigurable systems for motion planning;
  • Bioinspired path/route planning;
  • Modern artificial intelligence technologies for robot path planning;
  • Single- and multi-robot trajectory tracking;
  • Vision, sensing, kinematics, and dynamics;
  • Novel system design for mapping and navigation;
  • Situational awareness and responsiveness.

Applications of interest include:

  • Unmanned ground, aerial, and surface vehicles (UGVs, UAVs, USVs);
  • Autonomous underwater vehicles (AUVs);
  • Industrial and factory robots;
  • Healthcare, medical, surgical, and assistive robotics;
  • Service robotics;
  • Soft robotics;
  • Multi-agent systems.

Dr. Adham Atyabi
Dr. Mohd Nadhir Ab Wahab
Dr. Somaiyeh MahmoudZadeh
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.

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Keywords

  • planning and decision making
  • multi-agent motion planning
  • path formation (formation planning)
  • modular, hybrid, and reconfigurable systems for motion planning
  • bioinspired path/route planning
  • modern artificial intelligence technologies for robot path planning
  • single- and multi-robot trajectory tracking
  • vision, sensing, kinematics, dynamics
  • novel system design for mapping and navigation
  • situational awareness and responsiveness
  • unmanned ground, aerial, and surface vehicles (UGVs, UAVs, USVs)
  • autonomous underwater vehicles (AUVs)
  • multi-agent systems
  • localization

Published Papers (1 paper)

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Research

15 pages, 1820 KiB  
Article
Voronoi Tessellation for Efficient Sampling in Gaussian Process-Based Robotic Motion Planning
by Jee-Yong Park, Hoosang Lee, Changhyeon Kim and Jeha Ryu
Electronics 2023, 12(19), 4122; https://doi.org/10.3390/electronics12194122 - 02 Oct 2023
Viewed by 830
Abstract
On-line motion planning in dynamically changing environments poses a significant challenge in the design of autonomous robotic system. Conventional methods often require intricate design choices, while modern deep reinforcement learning (DRL) approaches demand vast amounts of robot motion data. Gaussian process (GP) regression-based [...] Read more.
On-line motion planning in dynamically changing environments poses a significant challenge in the design of autonomous robotic system. Conventional methods often require intricate design choices, while modern deep reinforcement learning (DRL) approaches demand vast amounts of robot motion data. Gaussian process (GP) regression-based imitation learning approaches address such issues by harnessing the GP’s data-efficient learning capabilities to infer generalized policies from a limited number of demonstrations, which can intuitively be generated by human operators. GP-based methods, however, are limited in data scalability as computation becomes cubically expensive as the amount of learned data increases. This issue is addressed by proposing Voronoi tessellation sampling, a novel data sampling strategy for learning GP-based robotic motion planning, where spatial correlation between input features and the output of the trajectory prediction model is exploited to select the data to be learned that are informative yet learnable by the model. Where the baseline is set by an imitation learning framework that uses GP regression to infer trajectories that learns policies optimized via a stochastic, reward-based optimization algorithm, experimental results demonstrate that the proposed method can learn optimal policies spanning over all of feature space using fewer data compared to the baseline method. Full article
(This article belongs to the Special Issue Robot Motion Planning)
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