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Advances in Mobile Robot Perceptions, Planning, Control and Learning: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 5429

Special Issue Editors

Dr. Yingbai Hu

E-Mail Website
Guest Editor
Department of Informatics, Technical University of Munich, 85748 Munich, Germany
Interests: optimization control; imitation learning; reinforcement learning; motion planning
Special Issues, Collections and Topics in MDPI journals
Dr. Chao Zeng

E-Mail Website
Guest Editor
Department of Informatics, University of Hamburg, 22527 Hamburg, Germany
Interests: learning from demonstration; compliant manipulation; robot learning and control
Special Issues, Collections and Topics in MDPI journals
Dr. Shu Li

E-Mail Website1 Website2
Guest Editor
School of Electrical Engineering, Liaoning University of Technology, Jinzhou 121001, China
Interests: intelligent control of wheeled mobile robots; intelligent control theory and applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Alois Christian Knoll

E-Mail Website
Guest Editor
Department of Informatics, Technical University of Munich, 85748 Munich, Germany
Interests: cognitive, medical, and sensor-based robotics; multiagent systems; data fusion; adaptive systems; multimedia information retrieval; model-driven development of embedded systems with applications of automotive software and electric transportation; simulation systems for robotics and traffic
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This publication is a continuation of our previous Special Issue on the same topic, entitled “Advances in Mobile Robot Perceptions, Planning, Control and Learning”.

With the increasing demand for mobile robots, such as lunar rovers, unmanned driving vehicles, rescue robots, and delivery robots, in the fields of aerospace, terrain, surface, and underwater, there is a growing interest in the development of new technologies that can be used to advance state-of-the-art mobile robots. A reliable mobile manipulation consists of at least three core parts: the perception of the environment, motion planning, and control. When detecting the environment through sensing (LiDAR, radar, camera, GPS, IMU, etc.), it is important to design a planner through various theoretical approaches (machine learning, reinforcement learning, convex/nonconvex optimization, evolutionary computation, potential field methods, etc.) to find the optimal trajectory of a mobile robot while avoiding static and/or dynamic obstacles. Additionally, it is necessary to design a robust controller (sliding mode control, model predictive control, adaptive neural network control, etc.) for the robot in perturbed environments, such as complex terrains and external contact forces.

It is expected that mobile robots can tackle the designed tasks (grasping, autonomous driving, etc.) under diverse and unstructured environmental conditions, but this brings challenges for sensing, planning, and control. For this reason, the perception, implementation, modeling, control, and learning of mobile robots have become urgent issues. We welcome original research contributions and state-of-the-art reviews of both theoretical and experimental studies which promote further research activities in this area.

The main topics of this Special Issue include, but are not limited to, the following:

  • Mobile robot intelligent perception and control;
  • Visual or haptic control with sensor feedback;
  • Human–robot interaction or teleoperation control;
  • Motion planning and navigation indoors or outdoors;
  • Machine learning for object detection, recognition, and tracking;
  • Reinforcement/imitation/transfer learning for mobile robots;
  • Multimodal learning for mobile robots;
  • Advanced modeling and sensors for mobile manipulation;
  • Applications of mobile manipulation.

Dr. Yingbai Hu
Dr. Chao Zeng
Dr. Shu Li
Prof. Dr. Alois Christian Knoll
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. Sensors 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
  • human-robot interaction
  • object detection, recognition, and tracking
  • robot control
  • motion planning and navigation

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Related Special Issue

Published Papers (6 papers)

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Research

18 pages, 1376 KiB  
Article
Time Series Classification for Predicting Biped Robot Step Viability
by Jorge Igual, Pedro Parik-Americano, Eric Cito Becman and Arturo Forner-Cordero
Sensors 2024, 24(22), 7107; https://doi.org/10.3390/s24227107 - 5 Nov 2024
Viewed by 404
Abstract
The prediction of the stability of future steps taken by a biped robot is a very important task, since it allows the robot controller to adopt the necessary measures in order to minimize damages if a fall is predicted. We present a classifier [...] Read more.
The prediction of the stability of future steps taken by a biped robot is a very important task, since it allows the robot controller to adopt the necessary measures in order to minimize damages if a fall is predicted. We present a classifier to predict the viability of a given planned step taken by a biped robot, i.e., if it will be stable or unstable. The features of the classifier are extracted from a feature engineering process exploiting the useful information contained in the time series generated in the trajectory planning of the step. In order to state the problem as a supervised classification one, we need the ground truth class for each planned step. This is obtained using the Predicted Step Viability (PSV) criterion. We also present a procedure to obtain a balanced and challenging training/testing dataset of planned steps that contains many steps in the border between stable and non stable regions. Following this trajectory planning strategy for the creation of the dataset we are able to improve the robustness of the classifier. Results show that the classifier is able to obtain a 95% of ROC AUC for this demanding dataset using only four time series among all the signals required by PSV to check viability. This allows to replace the PSV stability criterion, which is safe, robust but impossible to apply in real-time, by a simple, fast and embeddable classifier that can run in real time consuming much less resources than the PSV. Full article
(This article belongs to the Special Issue Advances in Mobile Robot Perceptions, Planning, Control and Learning: 2nd Edition)
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20 pages, 1623 KiB  
Article
Adaptive Finite-Time-Based Neural Optimal Control of Time-Delayed Wheeled Mobile Robotics Systems
by Shu Li, Tao Ren, Liang Ding and Lei Liu
Sensors 2024, 24(17), 5462; https://doi.org/10.3390/s24175462 - 23 Aug 2024
Viewed by 689
Abstract
For nonlinear systems with uncertain state time delays, an adaptive neural optimal tracking control method based on finite time is designed. With the help of the appropriate LKFs, the time-delay problem is handled. A novel nonquadratic Hamilton–Jacobi–Bellman (HJB) function is defined, where finite [...] Read more.
For nonlinear systems with uncertain state time delays, an adaptive neural optimal tracking control method based on finite time is designed. With the help of the appropriate LKFs, the time-delay problem is handled. A novel nonquadratic Hamilton–Jacobi–Bellman (HJB) function is defined, where finite time is selected as the upper limit of integration. This function contains information on the state time delay, while also maintaining the basic information. To meet specific requirements, the integral reinforcement learning method is employed to solve the ideal HJB function. Then, a tracking controller is designed to ensure finite-time convergence and optimization of the controlled system. This involves the evaluation and execution of gradient descent updates of neural network weights based on a reinforcement learning architecture. The semi-global practical finite-time stability of the controlled system and the finite-time convergence of the tracking error are guaranteed. Full article
(This article belongs to the Special Issue Advances in Mobile Robot Perceptions, Planning, Control and Learning: 2nd Edition)
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17 pages, 1136 KiB  
Article
SPIN-Based Linear Temporal Logic Path Planning for Ground Vehicle Missions with Motion Constraints on Digital Elevation Models
by Manuel Toscano-Moreno, Anthony Mandow, María Alcázar Martínez and Alfonso José García-Cerezo
Sensors 2024, 24(16), 5166; https://doi.org/10.3390/s24165166 - 10 Aug 2024
Viewed by 826
Abstract
Linear temporal logic (LTL) formalism can ensure the correctness of mobile robot planning through concise, readable, and verifiable mission specifications. For uneven terrain, planning must consider motion constraints related to asymmetric slope traversability and maneuverability. However, even though model checker tools like the [...] Read more.
Linear temporal logic (LTL) formalism can ensure the correctness of mobile robot planning through concise, readable, and verifiable mission specifications. For uneven terrain, planning must consider motion constraints related to asymmetric slope traversability and maneuverability. However, even though model checker tools like the open-source Simple Promela Interpreter (SPIN) include search optimization techniques to address the state explosion problem, defining a global LTL property that encompasses both mission specifications and motion constraints on digital elevation models (DEMs) can lead to complex models and high computation times. In this article, we propose a system model that incorporates a set of uncrewed ground vehicle (UGV) motion constraints, allowing these constraints to be omitted from LTL model checking. This model is used in the LTL synthesizer for path planning, where an LTL property describes only the mission specification. Furthermore, we present a specific parameterization for path planning synthesis using a SPIN. We also offer two SPIN-efficient general LTL formulas for representative UGV missions to reach a DEM partition set, with a specified or unspecified order, respectively. Validation experiments performed on synthetic and real-world DEMs demonstrate the feasibility of the framework for complex mission specifications on DEMs, achieving a significant reduction in computation cost compared to a baseline approach that includes a global LTL property, even when applying appropriate search optimization techniques on both path planners. Full article
(This article belongs to the Special Issue Advances in Mobile Robot Perceptions, Planning, Control and Learning: 2nd Edition)
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17 pages, 5383 KiB  
Article
Model Predictive Control with Variational Autoencoders for Signal Temporal Logic Specifications
by Eunji Im, Minji Choi and Kyunghoon Cho
Sensors 2024, 24(14), 4567; https://doi.org/10.3390/s24144567 - 14 Jul 2024
Viewed by 996
Abstract
This paper presents a control strategy synthesis method for dynamical systems with differential constraints, emphasizing the prioritization of specific rules. Special attention is given to scenarios where not all rules can be simultaneously satisfied to complete a given task, necessitating decisions on the [...] Read more.
This paper presents a control strategy synthesis method for dynamical systems with differential constraints, emphasizing the prioritization of specific rules. Special attention is given to scenarios where not all rules can be simultaneously satisfied to complete a given task, necessitating decisions on the extent to which each rule is satisfied, including which rules must be upheld or disregarded. We propose a learning-based Model Predictive Control (MPC) method designed to address these challenges. Our approach integrates a learning method with a traditional control scheme, enabling the controller to emulate human expert behavior. Rules are represented as Signal Temporal Logic (STL) formulas. A robustness margin, quantifying the degree of rule satisfaction, is learned from expert demonstrations using a Conditional Variational Autoencoder (CVAE). This learned margin is then applied in the MPC process to guide the prioritization or exclusion of rules. In a track driving simulation, our method demonstrates the ability to generate behavior resembling that of human experts and effectively manage rule-based dilemmas. Full article
(This article belongs to the Special Issue Advances in Mobile Robot Perceptions, Planning, Control and Learning: 2nd Edition)
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23 pages, 6229 KiB  
Article
Autonomous Exploration Method of Unmanned Ground Vehicles Based on an Incremental B-Spline Probability Roadmap
by Xingyang Feng, Hua Cong, Yu Zhang, Mianhao Qiu and Xuesong Hu
Sensors 2024, 24(12), 3951; https://doi.org/10.3390/s24123951 - 18 Jun 2024
Viewed by 679
Abstract
Autonomous exploration in unknown environments is a fundamental problem for the practical application of unmanned ground vehicles (UGVs). However, existing exploration methods face difficulties when directly applied to UGVs due to limited sensory coverage, conservative exploration strategies, inappropriate decision frequencies, and the non-holonomic [...] Read more.
Autonomous exploration in unknown environments is a fundamental problem for the practical application of unmanned ground vehicles (UGVs). However, existing exploration methods face difficulties when directly applied to UGVs due to limited sensory coverage, conservative exploration strategies, inappropriate decision frequencies, and the non-holonomic constraints of wheeled vehicles. In this paper, we present IB-PRM, a hierarchical planning method that combines Incremental B-splines with a probabilistic roadmap, which can support rapid exploration by a UGV in complex unknown environments. We define a new frontier structure that includes both information-gain guidance and a B-spline curve segment with different arrival orientations to satisfy the non-holonomic constraint characteristics of UGVs. We construct and maintain local and global graphs to generate and store filtered frontiers. By jointly solving the Traveling Salesman Problem (TSP) using these frontiers, we obtain the optimal global path traversing feasible frontiers. Finally, we optimize the global path based on the Time Elastic Band (TEB) algorithm to obtain a smooth, continuous, and feasible local trajectory. We conducted comparative experiments with existing advanced exploration methods in simulation environments of different scenarios, and the experimental results demonstrate that our method can effectively improve the efficiency of UGV exploration. Full article
(This article belongs to the Special Issue Advances in Mobile Robot Perceptions, Planning, Control and Learning: 2nd Edition)
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40 pages, 20460 KiB  
Article
Crystallization-Inspired Design and Modeling of Self-Assembly Lattice-Formation Swarm Robotics
by Zebang Pan, Guilin Wen, Hanfeng Yin, Shan Yin and Zhao Tan
Sensors 2024, 24(10), 3081; https://doi.org/10.3390/s24103081 - 12 May 2024
Viewed by 1215
Abstract
Self-assembly formation is a key research topic for realizing practical applications in swarm robotics. Due to its inherent complexity, designing high-performance self-assembly formation strategies and proposing corresponding macroscopic models remain formidable challenges and present an open research frontier. Taking inspiration from crystallization, this [...] Read more.
Self-assembly formation is a key research topic for realizing practical applications in swarm robotics. Due to its inherent complexity, designing high-performance self-assembly formation strategies and proposing corresponding macroscopic models remain formidable challenges and present an open research frontier. Taking inspiration from crystallization, this paper introduces a distributed self-assembly formation strategy by defining free, moving, growing, and solid states for robots. Robots in these states can spontaneously organize into user-specified two-dimensional shape formations with lattice structures through local interactions and communications. To address the challenges posed by complex spatial structures in modeling a macroscopic model, this work introduces the structural features estimation method. Subsequently, a corresponding non-spatial macroscopic model is developed to predict and analyze the self-assembly behavior, employing the proposed estimation method and a stock and flow diagram. Real-robot experiments and simulations validate the flexibility, scalability, and high efficiency of the proposed self-assembly formation strategy. Moreover, extensive experimental and simulation results demonstrate the model’s accuracy in predicting the self-assembly process under different conditions. Model-based analysis indicates that the proposed self-assembly formation strategy can fully utilize the performance of individual robots and exhibits strong self-stability. Full article
(This article belongs to the Special Issue Advances in Mobile Robot Perceptions, Planning, Control and Learning: 2nd Edition)
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