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Robotics
Special Issues
Decision-Making and Control under Uncertainties for Robotic and Autonomous Systems
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Decision-Making and Control under Uncertainties for Robotic and Autonomous Systems

A special issue of Robotics (ISSN 2218-6581). This special issue belongs to the section "AI in Robotics".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 10444

Special Issue Editors

Dr. Pan Zhao

E-Mail Website
Guest Editor
Department of Aerospace Engineering and Mechanics, University of Alabama, Tuscaloosa, AL, USA
Interests: robust, adaptive, and nonlinear control; safe learning-based control; robust integrated planning and control for robotic and autonomous systems; optimal and predictive control under uncertainty
Dr. Minghui Zheng

E-Mail Website
Guest Editor
J. Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University, College Station, TX 77807, USA
Interests: iterative learning control; high precision control; nonlinear/robust control with applications to robotics; autonomous systems; manufacturing
Dr. Yu Zhang

E-Mail Website
Guest Editor
College of Control Science and Engineering, Zhejiang University, Hangzhou, China
Interests: multi-source information fusion based intelligent navigation and localization; intelligent control theory and technology; intelligent autonomous system; artificial intelligence
Dr. Naira Hovakimyan

E-Mail Website
Guest Editor
Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Room 223, 1206 West Green Street, Urbana, IL 61801, USA
Interests: control and optimization; autonomous systems; machine learning; neural networks, game theory, and their applications in aerospace, robotics, mechanical, agricultural, electrical, petroleum, biomedical engineering, and elderly care
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of robotic and autonomous systems across various industries, from transportation and manufacturing to healthcare and agriculture, has marked significant technological advancement. These systems are designed to undertake complex tasks autonomously by making decisions and executing actions in real time. However, the ever-present uncertainties inherent in dynamics, environmental conditions, and interactions with other agents pose significant challenges to the seamless functioning of these systems. Reliable and effective decision-making and control mechanisms in the face of uncertainties are crucial for the safety and efficiency of these systems. In light of these imperatives, this Special Issue endeavors to delve into the forefront of advancements in decision-making and control techniques tailored for robotic and autonomous systems operating within uncertain contexts.

This Special Issue cordially invites original research articles, comprehensive reviews, and illuminating case studies that revolve around the pivotal theme of decision-making and control for robotic and autonomous systems operating under uncertainties. The topics encompassed will encompass a wide spectrum, including but not limited to:

  1. Robust, adaptive, and data-driven control;
  2. Risk-aware decision-making under perceptual and/or dynamics uncertainties;
  3. Safe learning in robotics;
  4. Deep learning approaches for decision-making in uncertain environments;
  5. Sensor fusion and perception algorithms for reliable decision-making;
  6. Fault detection and diagnosis techniques;
  7. Human-robot interactions and collaboration in uncertain environments;
  8. Decision-making under resource constraints and limited information;
  9. Decision-making and control in real-world robotic and autonomous systems.

Dr. Pan Zhao
Dr. Minghui Zheng
Dr. Yu Zhang
Dr. Naira Hovakimyan
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

  • assured autonomous systems
  • robot safety
  • robust and adaptive control
  • safe learning
  • risk-aware decision-making
  • decision-making under uncertainties
  • fault detection and diagnosis

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Published Papers (5 papers)

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Research

27 pages, 9595 KiB  
Article
A Control System Design and Implementation for Autonomous Quadrotors with Real-Time Re-Planning Capability
by Yevhenii Kovryzhenko, Nan Li and Ehsan Taheri
Robotics 2024, 13(9), 136; https://doi.org/10.3390/robotics13090136 - 9 Sep 2024
Viewed by 1692
Abstract
Real-time (re-)planning is crucial for autonomous quadrotors to navigate in uncertain environments where obstacles may be detected and trajectory plans must be adjusted on-the-fly to avoid collision. In this paper, we present a control system design for autonomous quadrotors that has real-time re-planning [...] Read more.
Real-time (re-)planning is crucial for autonomous quadrotors to navigate in uncertain environments where obstacles may be detected and trajectory plans must be adjusted on-the-fly to avoid collision. In this paper, we present a control system design for autonomous quadrotors that has real-time re-planning capability, including the hardware pipeline for the hardware–software integration to realize the proposed real-time re-planning algorithm. The framework is based on a modified version of the PX4 Autopilot and a Raspberry Pi 5 companion computer. The planning algorithm utilizes minimum-snap trajectory generation, taking advantage of the differential flatness property of quadrotors, to realize computationally light, real-time re-planning using an onboard computer. We first verify the control system and the planning algorithm through simulation experiments, followed by implementing and demonstrating the system on hardware using a quadcopter. Full article
(This article belongs to the Special Issue Decision-Making and Control under Uncertainties for Robotic and Autonomous Systems)
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29 pages, 7421 KiB  
Article
Continuous Online Semantic Implicit Representation for Autonomous Ground Robot Navigation in Unstructured Environments
by Quentin Serdel, Julien Marzat and Julien Moras
Robotics 2024, 13(7), 108; https://doi.org/10.3390/robotics13070108 - 18 Jul 2024
Viewed by 1412
Abstract
While mobile ground robots have now the physical capacity of travelling in unstructured challenging environments such as extraterrestrial surfaces or devastated terrains, their safe and efficient autonomous navigation has yet to be improved before entrusting them with complex unsupervised missions in such conditions. [...] Read more.
While mobile ground robots have now the physical capacity of travelling in unstructured challenging environments such as extraterrestrial surfaces or devastated terrains, their safe and efficient autonomous navigation has yet to be improved before entrusting them with complex unsupervised missions in such conditions. Recent advances in machine learning applied to semantic scene understanding and environment representations, coupled with modern embedded computational means and sensors hold promising potential in this matter. This paper therefore introduces the combination of semantic understanding, continuous implicit environment representation and smooth informed path-planning in a new method named COSMAu-Nav. It is specifically dedicated to autonomous ground robot navigation in unstructured environments and adaptable for embedded, real-time usage without requiring any form of telecommunication. Data clustering and Gaussian processes are employed to perform online regression of the environment topography, occupancy and terrain traversability from 3D semantic point clouds while providing an uncertainty modeling. The continuous and differentiable properties of Gaussian processes allow gradient based optimisation to be used for smooth local path-planning with respect to the terrain properties. The proposed pipeline has been evaluated and compared with two reference 3D semantic mapping methods in terms of quality of representation under localisation and semantic segmentation uncertainty using a Gazebo simulation, derived from the 3DRMS dataset. Its computational requirements have been evaluated using the Rellis-3D real world dataset. It has been implemented on a real ground robot and successfully employed for its autonomous navigation in a previously unknown outdoor environment. Full article
(This article belongs to the Special Issue Decision-Making and Control under Uncertainties for Robotic and Autonomous Systems)
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20 pages, 5377 KiB  
Article
Guaranteed Trajectory Tracking under Learned Dynamics with Contraction Metrics and Disturbance Estimation
by Pan Zhao, Ziyao Guo, Yikun Cheng, Aditya Gahlawat, Hyungsoo Kang and Naira Hovakimyan
Robotics 2024, 13(7), 99; https://doi.org/10.3390/robotics13070099 - 30 Jun 2024
Viewed by 1467
Abstract
This paper presents a contraction-based learning control architecture that allows for using model learning tools to learn matched model uncertainties while guaranteeing trajectory tracking performance during the learning transients. The architecture relies on a disturbance estimator to estimate the pointwise value of the [...] Read more.
This paper presents a contraction-based learning control architecture that allows for using model learning tools to learn matched model uncertainties while guaranteeing trajectory tracking performance during the learning transients. The architecture relies on a disturbance estimator to estimate the pointwise value of the uncertainty, i.e., the discrepancy between a nominal model and the true dynamics, with pre-computable estimation error bounds, and a robust Riemannian energy condition for computing the control signal. Under certain conditions, the controller guarantees exponential trajectory convergence during the learning transients, while learning can improve robustness and facilitate better trajectory planning. Simulation results validate the efficacy of the proposed control architecture. Full article
(This article belongs to the Special Issue Decision-Making and Control under Uncertainties for Robotic and Autonomous Systems)
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15 pages, 2715 KiB  
Article
Real-Time Multi-Robot Mission Planning in Cluttered Environment
by Zehui Lu, Tianyu Zhou and Shaoshuai Mou
Robotics 2024, 13(3), 40; https://doi.org/10.3390/robotics13030040 - 28 Feb 2024
Cited by 2 | Viewed by 2355
Abstract
Addressing a collision-aware multi-robot mission planning problem, which involves task allocation and path-finding, poses a significant difficulty due to the necessity for real-time computational efficiency, scalability, and the ability to manage both static and dynamic obstacles and tasks within a complex environment. This [...] Read more.
Addressing a collision-aware multi-robot mission planning problem, which involves task allocation and path-finding, poses a significant difficulty due to the necessity for real-time computational efficiency, scalability, and the ability to manage both static and dynamic obstacles and tasks within a complex environment. This paper introduces a parallel real-time algorithm aimed at overcoming these challenges. The proposed algorithm employs an approximation-based partitioning mechanism to partition the entire unassigned task set into several subsets. This approach decomposes the original problem into a series of single-robot mission planning problems. To validate the effectiveness of the proposed method, both numerical and hardware experiments are conducted, involving dynamic obstacles and tasks. Additionally, comparisons in terms of optimality and scalability against an existing method are provided, showcasing its superior performance across both metrics. Furthermore, a computational burden analysis is conducted to demonstrate the consistency of our method with the observations derived from these comparisons. Finally, the optimality gap between the proposed method and the global optima in small-size problems is demonstrated. Full article
(This article belongs to the Special Issue Decision-Making and Control under Uncertainties for Robotic and Autonomous Systems)
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39 pages, 7128 KiB  
Article
A Two Stage Nonlinear I/O Decoupling and Partially Wireless Controller for Differential Drive Mobile Robots
by Nikolaos D. Kouvakas, Fotis N. Koumboulis and John Sigalas
Robotics 2024, 13(2), 26; https://doi.org/10.3390/robotics13020026 - 31 Jan 2024
Cited by 1 | Viewed by 2008
Abstract
Differential drive mobile robots, being widely used in several industrial and domestic applications, are increasingly demanding when concerning precision and satisfactory maneuverability. In the present paper, the problem of independently controlling the velocity and orientation angle of a differential drive mobile robot is [...] Read more.
Differential drive mobile robots, being widely used in several industrial and domestic applications, are increasingly demanding when concerning precision and satisfactory maneuverability. In the present paper, the problem of independently controlling the velocity and orientation angle of a differential drive mobile robot is investigated by developing an appropriate two stage nonlinear controller embedded on board and also by using the measurements of the speed and accelerator of the two wheels, as well as taking remote measurements of the orientation angle and its rate. The model of the system is presented in a nonlinear state space form that includes unknown additive terms arising from external disturbances and actuator faults. Based on the nonlinear model of the system, the respective I/O relation is derived, and a two-stage nonlinear measurable output feedback controller, analyzed into an internal and an external controller, is designed. The internal controller aims to produce a decoupled inner closed-loop system of linear form, regulating the linear velocity and angular velocity of the mobile robot independently. The internal controller is of the nonlinear PD type and uses real time measurements of the angular velocities of the active wheels of the vehicle, as well as the respective accelerations. The external controller aims toward the regulation of the orientation angle of the vehicle. It is of a linear, delayed PD feedback form, offering feedback from the remote measurements of the orientation angle and angular velocity of the vehicle, which are transmitted to the controller through a wireless network. Analytic formulae are derived for the parameters of the external controller to ensure the stability of the closed-loop system, even in the presence of the wireless transmission delays, as well as asymptotic command following for the orientation angle. To compensate for measurement noise, external disturbances, and actuator faults, a metaheuristic algorithm is proposed to evaluate the remaining free controller parameters. The performance of the proposed control scheme is evaluated through a series of computational experiments, demonstrating satisfactory behavior. Full article
(This article belongs to the Special Issue Decision-Making and Control under Uncertainties for Robotic and Autonomous Systems)
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