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Robotics
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Motion Trajectory Prediction for Mobile Robots
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Motion Trajectory Prediction for Mobile Robots

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 8831

Special Issue Editors

Dr. Bruno Brito

E-Mail Website
Guest Editor
Department of Cognitive Robotics, TU Delft, Delft, The Netherlands
Interests: robotics; motion planning; autonomous vehicles; artificial intelligence; deep learning and reinforcement learning
Dr. Giorgos Mamakoukas

E-Mail Website
Guest Editor
Mitsubishi Electric Research Laboratories, Cambridge, MA, USA
Interests: nonlinear optimization; control theory; dynamics; machine learning; and numerical methods

Special Issue Information

Dear Colleagues,

Autonomous robot systems will profoundly impact on our society as transportation and delivery service providers and assistants in our households and hospitals. For the first time, robot taxi companies have begun commercial operations without safety drivers. They have started to test their autonomous last mile delivery systems on the streets. Additionally, Amazon introduced its first household robot for home monitoring and companionship. Nevertheless, current autonomous robot systems are limited to controlled settings, low speeds, and clutter-free environments, despite all of the recent technological advancements. Human environments are intrinsically dynamic, and the robots must be able to navigate them to avoid collisions. Therefore, trajectory prediction models are essential to enable safe and efficient autonomous transportation.

The literature on trajectory prediction problems is vast, but several problems remain. Firstly, the number of parameters used in state-of-the-art prediction models is growing exponentially. Consequently, training these models is costly. Moreover, these models typically have high latency and cannot be used in real time. Secondly, modeling the interactions and predicting the trajectories based on the other agents' actions is still a challenge.

This Special Issue invites all research articles on trajectory prediction and interaction modeling applied to autonomous robots. Topics of interest include (but are not limited to):

  • Trajectory prediction;
  • Grid prediction;
  • Interaction modeling;
  • Real-time trajectory prediction models;
  • Robotic systems employing trajectory prediction models;
  • Motion planning;
  • Path planning and obstacle avoidance;
  • Trajectory prediction for multi-robot systems.

Dr. Bruno Brito
Dr. Giorgos Mamakoukas
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.

Published Papers (4 papers)

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Research

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22 pages, 6561 KiB  
Article
Dual-Quaternion-Based SLERP MPC Local Controller for Safe Self-Driving of Robotic Wheelchairs
by Daifeng Wang, Wenjing Cao and Atsuo Takanishi
Robotics 2023, 12(6), 153; https://doi.org/10.3390/robotics12060153 - 13 Nov 2023
Viewed by 1898
Abstract
In this work, the motion control of a robotic wheelchair to achieve safe and intelligent movement in an unknown scenario is proposed. The primary objective is to develop a comprehensive framework for a robotic wheelchair that combines a global path planner and a [...] Read more.
In this work, the motion control of a robotic wheelchair to achieve safe and intelligent movement in an unknown scenario is proposed. The primary objective is to develop a comprehensive framework for a robotic wheelchair that combines a global path planner and a model predictive control (MPC) local controller. The A* algorithm is employed to generate a global path. To ensure safe and directional motion for the wheelchair user, an MPC local controller is implemented taking into account the via points generated by an approach combined with dual quaternions and spherical linear interpolation (SLERP). Dual quaternions are utilized for their simultaneous handling of rotation and translation, while SLERP enables smooth and continuous rotation interpolation by generating intermediate orientations between two specified orientations. The integration of these two methods optimizes navigation performance. The system is built on the Robot Operating System (ROS), with an electric wheelchair equipped with 3D-LiDAR serving as the hardware foundation. The experimental results reveal the effectiveness of the proposed method and demonstrate the ability of the robotic wheelchair to move safely from the initial position to the destination. This work contributes to the development of effective motion control for robotic wheelchairs, focusing on safety and improving the user experience when navigating in unknown environments. Full article
(This article belongs to the Special Issue Motion Trajectory Prediction for Mobile Robots)
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15 pages, 2750 KiB  
Article
Online Motion Planning for Safe Human–Robot Cooperation Using B-Splines and Hidden Markov Models
by Giovanni Braglia, Matteo Tagliavini, Fabio Pini and Luigi Biagiotti
Robotics 2023, 12(4), 118; https://doi.org/10.3390/robotics12040118 - 18 Aug 2023
Cited by 1 | Viewed by 1300
Abstract
When humans and robots work together, ensuring safe cooperation must be a priority. This research aims to develop a novel real-time planning algorithm that can handle unpredictable human movements by both slowing down task execution and modifying the robot’s path based on the [...] Read more.
When humans and robots work together, ensuring safe cooperation must be a priority. This research aims to develop a novel real-time planning algorithm that can handle unpredictable human movements by both slowing down task execution and modifying the robot’s path based on the proximity of the human operator. To achieve this, an efficient method for updating the robot’s motion is developed using a two-fold control approach that combines B-splines and hidden Markov models. This allows the algorithm to adapt to a changing environment and avoid collisions. The proposed framework is thus validated using the Franka Emika Panda robot in a simple start–goal task. Our algorithm successfully avoids collision with the moving hand of an operator monitored by a fixed camera. Full article
(This article belongs to the Special Issue Motion Trajectory Prediction for Mobile Robots)
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Review

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29 pages, 1504 KiB  
Review
A Survey of Machine Learning Approaches for Mobile Robot Control
by Monika Rybczak, Natalia Popowniak and Agnieszka Lazarowska
Robotics 2024, 13(1), 12; https://doi.org/10.3390/robotics13010012 - 09 Jan 2024
Cited by 1 | Viewed by 2182
Abstract
Machine learning (ML) is a branch of artificial intelligence that has been developing at a dynamic pace in recent years. ML is also linked with Big Data, which are huge datasets that need special tools and approaches to process them. ML algorithms make [...] Read more.
Machine learning (ML) is a branch of artificial intelligence that has been developing at a dynamic pace in recent years. ML is also linked with Big Data, which are huge datasets that need special tools and approaches to process them. ML algorithms make use of data to learn how to perform specific tasks or make appropriate decisions. This paper presents a comprehensive survey of recent ML approaches that have been applied to the task of mobile robot control, and they are divided into the following: supervised learning, unsupervised learning, and reinforcement learning. The distinction of ML methods applied to wheeled mobile robots and to walking robots is also presented in the paper. The strengths and weaknesses of the compared methods are formulated, and future prospects are proposed. The results of the carried out literature review enable one to state the ML methods that have been applied to different tasks, such as the following: position estimation, environment mapping, SLAM, terrain classification, obstacle avoidance, path following, learning to walk, and multirobot coordination. The survey allowed us to associate the most commonly used ML algorithms with mobile robotic tasks. There still exist many open questions and challenges such as the following: complex ML algorithms and limited computational resources on board a mobile robot; decision making and motion control in real time; the adaptability of the algorithms to changing environments; the acquisition of large volumes of valuable data; and the assurance of safety and reliability of a robot’s operation. The development of ML algorithms for nature-inspired walking robots also seems to be a challenging research issue as there exists a very limited amount of such solutions in the recent literature. Full article
(This article belongs to the Special Issue Motion Trajectory Prediction for Mobile Robots)
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39 pages, 3639 KiB  
Review
A Review of Trajectory Prediction Methods for the Vulnerable Road User
by Erik Schuetz and Fabian B. Flohr
Robotics 2024, 13(1), 1; https://doi.org/10.3390/robotics13010001 - 19 Dec 2023
Viewed by 2577
Abstract
Predicting the trajectory of other road users, especially vulnerable road users (VRUs), is an important aspect of safety and planning efficiency for autonomous vehicles. With recent advances in Deep-Learning-based approaches in this field, physics- and classical Machine-Learning-based methods cannot exhibit competitive results compared [...] Read more.
Predicting the trajectory of other road users, especially vulnerable road users (VRUs), is an important aspect of safety and planning efficiency for autonomous vehicles. With recent advances in Deep-Learning-based approaches in this field, physics- and classical Machine-Learning-based methods cannot exhibit competitive results compared to the former. Hence, this paper provides an extensive review of recent Deep-Learning-based methods in trajectory prediction for VRUs and autonomous driving in general. We review the state and context representations and architectural insights of selected methods, divided into categories according to their primary prediction scheme. Additionally, we summarize reported results on popular datasets for all methods presented in this review. The results show that conditional variational autoencoders achieve the best overall results on both pedestrian and autonomous driving datasets. Finally, we outline possible future research directions for the field of trajectory prediction in autonomous driving. Full article
(This article belongs to the Special Issue Motion Trajectory Prediction for Mobile Robots)
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