Optimal Robot Motion Planning

A special issue of Robotics (ISSN 2218-6581).

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 4319

Special Issue Editors


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Guest Editor
Mechanical Engineering and Aeronautics Department, University of Patras, Patras, Greece
Interests: robot mission planning; robot control; design and mechatronics; modelling of electromechanical systems

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Guest Editor
Department of Product and Systems Design Engineering, University of the Aegean, 8110 Mytilini, Greece
Interests: mechatronics design; gripper design; robot kinematics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Product and Systems Design Engineering, University of the Aegean , Mytilene, Greece
Interests: robot motion planning; intelligent transportation systems; robotic logistics

Special Issue Information

Dear colleague,

Τhis Special Issue presents an important topic in robotics, focusing on optimal robot motion planning of mobile robots and manipulators as well as its applications such as on autonomous driving and on marine vehicles.

Today, robots are requested to operate in real-world problems in complicated, dynamic, unstructured, and open environments and their motion planning becomes more challenging. A manipulator or a mobile robot needs to avoid obstacles and move along the appropriate path for each task in an optimal way. However, to deploy novel robotic applications in practice, the traditional approach to robotic development does not suffice. Thus, there is an increasing need for advanced algorithmic tools enabling powerful capabilities for the robots. In most applications, the key challenge is to plan the optimal motion that still guarantees constraint satisfaction. Optimal robot motion planning is not a brand new topic but there is a recent renewed interest and a growing variety of methods for motion planning, which vary in terms of their optimality properties.


This Special Issue fits within the scope of Robotics and aims to present and discuss major research challenges, latest developments, and recent advances in optimal motion planning for robots (such as unmanned underwater or surface vehicles, unmanned ground and aerial vehicles, autonomous vehicles, manipulators, etc.) which are requested to operate in a real world environment.

Prof. Dr. Nikolaos Aspragathos
Assist. Prof. Dr. Vassilis C. Moulianitis
Assist. Prof. Dr. Elias K. Xidias
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. Robotics is an international peer-reviewed open access monthly 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 1800 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

  • robot motion planning
  • unmanned vehicles
  • industrial robots
  • microparts motion planning
  • evolutionary algorithms
  • machine learning methods
  • reinforcement learning
  • computational geometry

Published Papers (1 paper)

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Research

17 pages, 1879 KiB  
Article
Multi-Agent Collaborative Path Planning Based on Staying Alive Policy
by Anton Koval, Sina Sharif Mansouri and George Nikolakopoulos
Robotics 2020, 9(4), 101; https://doi.org/10.3390/robotics9040101 - 28 Nov 2020
Cited by 6 | Viewed by 3441
Abstract
Modern mobile robots tend to be used in numerous exploration and search and rescue applications. Essentially they are coordinated by human operators and collaborate with inspection or rescue teams. Over the time, robots became more advanced and capable for various autonomous collaborative scenarios. [...] Read more.
Modern mobile robots tend to be used in numerous exploration and search and rescue applications. Essentially they are coordinated by human operators and collaborate with inspection or rescue teams. Over the time, robots became more advanced and capable for various autonomous collaborative scenarios. Recent advances in the field of collaborative exploration and coverage provide different approaches to solve this objective. Thus scope of this article is to present a novel collaborative approach for multi-agent coordination in exploration and coverage of unknown complex indoor environments. Fundamentally, the task of collaborative exploration can be divided into two core components. The principal one is a sensor based exploration scheme that aims to guarantee complete area exploration and coverage. The second core component proposed is a staying alive policy that takes under consideration the battery charge level limitation of the agents. From this perspective the path planner assigns feasible tasks to each of the agents, including the capability of providing reachable, collision free paths. The overall efficacy of the proposed approach was extensively evaluated by multiple simulation results in a complex unknown environments. Full article
(This article belongs to the Special Issue Optimal Robot Motion Planning)
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