Advances in Autonomous Vehicles, Automation, and Robotics

Topic Information

Dear Colleagues,

In recent years, autonomous vehicles and robotic systems have become a major research focus with wide-ranging applications in civil and military domains, including transportation, manufacturing, agriculture, healthcare, logistics, surveillance, and smart infrastructure. The growing need to explore diverse environments—on land, in the air, and underwater—has driven significant progress in the development of advanced unmanned vehicle systems. These include autonomous underwater vehicles, remotely operated vehicles, robotic ground vehicles, drones, quadcopters, robotic manipulators, and multimodal platforms capable of operating across multiple domains.

Advances in modeling, control techniques, and decision-making are critical for the design of reliable, efficient, and intelligent autonomous systems and robotics. At the same time, artificial intelligence, machine learning, computer vision, and sensor fusion are accelerating innovations, enabling enhanced adaptability, safety, and autonomy. This Topic welcomes original research and review articles addressing theoretical developments, algorithmic methods, system design, and experimental demonstrations.

Topics of interest include, but are not limited to, the following:

• Vehicle design and multimodal operations;
• Kinematics, dynamics, and modeling of autonomous systems and robot manipulators;
• Maneuvering, navigation, and localization;
• Path planning and collision avoidance;
• Perception and sensor/actuator systems;
• Cooperative and swarm robotics;
• Human–robot interaction and safety;
• Artificial intelligence (AI)- and machine learning (ML)-based control strategies;
• Linear and nonlinear control synthesis;
• Applications in ground, aerial, and underwater domains;
• Fault diagnosis and fault tolerance;
• Autonomous vehicle navigation, guidance, control, and path planning;
• Architecture, concepts, methods, and technologies for autonomous vehicle communications and networks.

Dr. Mai The Vu
Dr. Anh Tuan Vo
Dr. Caoyang Yu
Dr. Gong Xiang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Drones drones	4.8	7.4	2017	20.8 Days	CHF 2600	Submit
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit
Future Transportation futuretransp	1.7	3.8	2021	21.7 Days	CHF 1200	Submit
Journal of Marine Science and Engineering jmse	2.8	5.0	2013	16.5 Days	CHF 2600	Submit
Machines machines	2.5	4.7	2013	17.6 Days	CHF 2400	Submit
Sensors sensors	3.5	8.2	2001	17.8 Days	CHF 2600	Submit
Vehicles vehicles	2.2	5.3	2019	21.4 Days	CHF 1800	Submit

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (11 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Sensors Machines Applied Sciences JMSE Electronics Drones Future Transportation Vehicles

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

29 pages, 5357 KB  

Open AccessArticle

A Bayesian Optimization-Based AUV Swarm Model in a Double-Gyre Flow Field

by Tengfei Yang, Ziwen Zhang, Guoqiang Tang, Yan Yang, Qiang Zhao, Hao Wang, Minyi Xu and Shuai Li

Drones 2026, 10(5), 340; https://doi.org/10.3390/drones10050340 - 2 May 2026

Viewed by 175

Abstract

Conventional cooperative control methods for multi-AUV systems typically rely on quasi-steady hydrodynamic assumptions and do not explicitly account for time-varying uncertainties in ocean dynamics. In addition, controller parameters are often tuned empirically. As a result, under complex disturbed flow fields and communication constraints, [...] Read more.

Conventional cooperative control methods for multi-AUV systems typically rely on quasi-steady hydrodynamic assumptions and do not explicitly account for time-varying uncertainties in ocean dynamics. In addition, controller parameters are often tuned empirically. As a result, under complex disturbed flow fields and communication constraints, AUV swarms are prone to group fragmentation and reduced polarization, which undermines stable cooperative navigation. To address these limitations, we propose a double-gyre-flow-optimized autonomous underwater vehicle swarm (DGF-OAS) model for coordinated operations in time-varying flow fields. The proposed model incorporates a heading-aware graph attention mechanism to adaptively adjust adjacency weights among agents with different roles. It further integrates the Lennard–Jones potential to preserve safe inter-vehicle spacing and embeds a periodically varying double-gyre flow field to characterize ocean disturbances. Bayesian optimization is then employed to automatically identify suitable weights for the alignment and attraction–repulsion terms, thereby improving swarm cohesion and environmental adaptability. Simulation results demonstrate that, under flow-field disturbances, DGF-OAS achieves group polarization of up to 96%, reduces the average task completion time by 15.84% compared with the baseline model, and attains a task completion rate of 97%, significantly outperforming the compared methods. These findings indicate that the proposed approach exhibits strong adaptability and stability in complex environments and offers an effective solution for AUV swarm control. Full article

(This article belongs to the Topic Advances in Autonomous Vehicles, Automation, and Robotics)

31 pages, 11170 KB  

Open AccessArticle

Digital Twin of Coal Mine Rescue Robot—Research on Intelligence and Visualization

by Shaoze You, Menggang Li, Baolei Wu, Jun Wang and Chaoquan Tang

Sensors 2026, 26(9), 2840; https://doi.org/10.3390/s26092840 - 1 May 2026

Viewed by 728

Abstract

Mine disasters require urgent lifeline setup in confined tunnels, but manual rescue in unstable accident zones carries huge safety risks. Coal mine rescue robots (CMRRs) have become key equipment to replace manual rescue. However, traditional remote-controlled CMRRs suffer from low autonomy and weak [...] Read more.

Mine disasters require urgent lifeline setup in confined tunnels, but manual rescue in unstable accident zones carries huge safety risks. Coal mine rescue robots (CMRRs) have become key equipment to replace manual rescue. However, traditional remote-controlled CMRRs suffer from low autonomy and weak environmental perception capability, which have become critical bottlenecks for field application. As an emerging technology in the mining field, digital twin enables high-precision virtual-real mapping and on-site operation guidance, providing a novel solution to the above problems. To realize autonomous navigation and digital twin visualization of the CMRR, this paper first carries out targeted hardware retrofits on the CMRR platform, upgrades environmental perception, communication transmission and motion control modules, and lays a solid hardware foundation for subsequent algorithm design and system implementation. Aiming at the complex post-disaster underground environment, a digital twin-integrated CMRR system is constructed. For intelligent autonomous navigation, this study investigates a 3D point cloud–based autonomous navigation framework and proposes a slope-fitting method as well as a maximum arrival probability obstacle avoidance method based on Bézier curve trajectories. For environmental visualization, a digital twin interactive interface is built to monitor gas and other environmental parameters in real time, and accurately reconstruct underground roadway structures based on point cloud data. This design not only ensures the robot’s autonomous obstacle avoidance but also helps rescuers grasp underground conditions in advance. Field tests in a simulated post-disaster mine with complex terrain show that the system can stably complete autonomous navigation tasks, maintain stable motion control under dynamic interference, and provide accurate and reliable environmental data for rescue decisions, verifying its feasibility and effectiveness in harsh mine rescue scenarios. Full article

(This article belongs to the Topic Advances in Autonomous Vehicles, Automation, and Robotics)

►▼ Show Figures

Figure 1

23 pages, 3484 KB  

Open AccessArticle

IFA-ICP: A Low-Complexity and Image Feature-Assisted Iterative Closest Point (ICP) Scheme for Odometry Estimation in SLAM, and Its FPGA-Based Hardware Accelerator Design

by Jia-En Li and Yin-Tsung Hwang

Sensors 2026, 26(8), 2326; https://doi.org/10.3390/s26082326 - 9 Apr 2026

Viewed by 319

Abstract

Odometry estimation, which calculates the trajectory of a moving object across timeframes, is a critical and time-consuming function in SLAM (Simultaneous Localization and Mapping) systems. Although LiDAR-based sensing is most popular for outdoor and long-range applications because of its ranging accuracy, the sparsity [...] Read more.

Odometry estimation, which calculates the trajectory of a moving object across timeframes, is a critical and time-consuming function in SLAM (Simultaneous Localization and Mapping) systems. Although LiDAR-based sensing is most popular for outdoor and long-range applications because of its ranging accuracy, the sparsity of laser point cloud poses a significant challenge to feature extraction and matching in odometry estimation. In this paper, we investigate odometry estimation from two aspects, i.e., algorithm optimization, and system design/implementation. In algorithm optimization, we present an image feature-assisted odometry estimation scheme that leverages the richness of image information captured by a companion camera to enhance the accuracy of laser point cloud matching. This also serves as a screening mechanism to reduce the matching size and lower the computing complexity for a higher estimation rate. In addition, various schemes, such as adaptive threshold in image feature point selection, principal component analysis (PCA)-based plane fitting for laser point interpolation, and Gauss–Newton optimization for calculating the transform matrix, are also employed to improve the accuracy of odometry estimation. The performance of improved odometry estimation is verified using an existing FLOAM (Fast Lidar Odometry and Mapping) framework. The KITTI dataset for autonomous vehicles with ground truth was used as the test bench. Simulation results indicate that the translation error and rotation error can be reduced by 16.6% and 1.3%, respectively. Computing complexity, measured as the software execution time, also reduced by 63%. In system implementation, a hardware/software (HW/SW) co-design strategy was adopted, where complexity profiling was first conducted to determine the task partitioning and time-consuming tasks are offloaded to a hardware accelerator. This facilitates real-time execution on a resource-constrained embedded platform consisting of a microprocessor module (Raspberry Pi) and an attached FPGA board (Pynq Z2). Efficient hardware designs for customized DSP functions (adaptive threshold and PCA) were developed in an FPGA capable of completing one data frame in 20ms. The final system implementation met the target throughput of 10 estimations per second, and can be scaled up further. Full article

(This article belongs to the Topic Advances in Autonomous Vehicles, Automation, and Robotics)

►▼ Show Figures

Figure 1

16 pages, 13362 KB  

Open AccessArticle

SemOD: Semantic-Enabled Object Detection Network Under Various Weather Conditions

by Aiyinsi Zuo and Zhaoliang Zheng

Sensors 2026, 26(6), 1820; https://doi.org/10.3390/s26061820 - 13 Mar 2026

Viewed by 437

Abstract

In the field of autonomous driving, camera-based perception models are mostly trained on clear weather data. Models designed to handle specific weather conditions often lack generalization to dynamically changing environments and primarily focus on weather removal rather than robust perception. This paper proposes [...] Read more.

In the field of autonomous driving, camera-based perception models are mostly trained on clear weather data. Models designed to handle specific weather conditions often lack generalization to dynamically changing environments and primarily focus on weather removal rather than robust perception. This paper proposes a semantic-enabled network for object detection under diverse weather conditions. Semantic information enables the model to generate plausible content in missing regions and accurately delineate object boundaries. It also preserves visual coherence and realism across both restored and original image areas, thereby facilitating image transformation and object recognition. Specifically, our architecture consists of a Preprocessing Unit (PPU) and a Detection Unit (DTU), where the PPU utilizes a U-shaped network enriched with semantics to refine degraded images, and the DTU integrates this semantic information for object detection using a modified YOLO network. Extensive experiments demonstrate that the proposed method achieves mAP improvements ranging from 1.49% to 8.78% compared with existing approaches across multiple benchmark datasets under various weather conditions. These results demonstrate the effectiveness of semantic guidance in image enhancement and object detection, providing a comprehensive framework for improving detection performance. The source code will be made publicly available. Full article

(This article belongs to the Topic Advances in Autonomous Vehicles, Automation, and Robotics)

►▼ Show Figures

Figure 1

17 pages, 2130 KB  

Open AccessArticle

FogGate-YOLO: Traffic Object Detection in Foggy Environments Using Channel Selection Mechanisms

by Yuhe Yang, Suilian You, Jinpeng Yu and Bo Lu

Sensors 2026, 26(6), 1811; https://doi.org/10.3390/s26061811 - 13 Mar 2026

Viewed by 424

Abstract

To address the challenges posed by foggy conditions in object detection tasks, we propose FogGate-YOLO, an enhanced YOLOv8 framework designed for robust and efficient detection in foggy environments. Unlike traditional methods that rely on image dehazing or preprocessing enhancements, our approach directly strengthens [...] Read more.

To address the challenges posed by foggy conditions in object detection tasks, we propose FogGate-YOLO, an enhanced YOLOv8 framework designed for robust and efficient detection in foggy environments. Unlike traditional methods that rely on image dehazing or preprocessing enhancements, our approach directly strengthens the model’s feature representation by introducing two novel modules: GroupGatedConv and C2fGated. These modules collaboratively mitigate fog-induced degradation, improving feature extraction and enhancing performance without additional inference overhead. The GroupGatedConv module focuses on coarse-grained channel selection in the early to mid-stages of the backbone, suppressing noise while preserving essential structural features. The C2fGated module refines the aggregated features in both the backbone and neck after multi-branch fusion, enhancing fine-grained feature recalibration. Together, these two modules provide a hierarchical coarse to fine channel selection strategy that significantly improves the model’s discriminative power in foggy conditions. Full article

(This article belongs to the Topic Advances in Autonomous Vehicles, Automation, and Robotics)

►▼ Show Figures

Figure 1

30 pages, 19923 KB  

Open AccessArticle

Curriculum-Based Reinforcement Learning for Autonomous UAV Navigation in Unknown Curved Tubular Conduits

by Zamirddine Mari, Jérôme Pasquet and Julien Seinturier

Sensors 2026, 26(4), 1236; https://doi.org/10.3390/s26041236 - 13 Feb 2026

Viewed by 508

Abstract

Autonomous drone navigation in confined tubular environments remains a major challenge due to the constraining geometry of the conduits, the proximity of the walls, and the perceptual limitations inherent to such scenarios. We propose a reinforcement learning (RL) approach enabling a drone to [...] Read more.

Autonomous drone navigation in confined tubular environments remains a major challenge due to the constraining geometry of the conduits, the proximity of the walls, and the perceptual limitations inherent to such scenarios. We propose a reinforcement learning (RL) approach enabling a drone to navigate unknown three-dimensional tubes without any prior knowledge of their geometry, relying solely on local observations from a Light Detection and Ranging (LiDAR) sensor and a conditional visual detection of the tube center. In contrast, the Pure Pursuit algorithm, used as a deterministic baseline, benefits from explicit access to the centerline, creating an information asymmetry designed to assess the ability of RL to compensate for the absence of a geometric model. The agent is trained through a progressive curriculum learning strategy that gradually exposes it to increasingly curved geometries, where the tube center frequently disappears from the visual field. A turning-negotiation mechanism, based on the combination of direct visibility, directional memory, and LiDAR symmetry cues, proves essential for ensuring stable navigation under such partial observability conditions. Experiments show that the Proximal Policy Optimization (PPO) policy acquires robust and generalizable behavior, consistently outperforming the deterministic controller despite its limited access to geometric information. Validation in a high-fidelity three-dimensional environment further confirms the transferability of the learned behavior to continuous physical dynamics. In particular, this work introduces an explicit formulation of the turn negotiation problem in tubular navigation, coupled with a reward design and evaluation metrics that make turn-handling behavior measurable and analyzable. This explicit focus on turn negotiation distinguishes our approach from prior learning-based and heuristic methods. The proposed approach thus provides a complete framework for autonomous navigation in unknown tubular environments and opens perspectives for industrial, underground, or medical applications where progressing through narrow and weakly perceptive conduits represents a central challenge. Full article

(This article belongs to the Topic Advances in Autonomous Vehicles, Automation, and Robotics)

►▼ Show Figures

Figure 1

23 pages, 4185 KB  

Open AccessArticle

Real-Time Axle-Load Sensing and AI-Enhanced Braking-Distance Prediction for Multi-Axle Heavy-Duty Trucks

by Duk Sun Yun and Byung Chul Lim

Appl. Sci. 2026, 16(3), 1547; https://doi.org/10.3390/app16031547 - 3 Feb 2026

Viewed by 653

Abstract

Accurate braking-distance prediction for heavy-duty multi-axle trucks remains challenging due to the large gross vehicle weight, tandem-axle interactions, and strong transient load transfer during emergency braking. Recent studies on tire–road friction estimation, commercial-vehicle braking control (EBS/AEBS), and weigh-in-motion (WIM) sensing have highlighted that [...] Read more.

Accurate braking-distance prediction for heavy-duty multi-axle trucks remains challenging due to the large gross vehicle weight, tandem-axle interactions, and strong transient load transfer during emergency braking. Recent studies on tire–road friction estimation, commercial-vehicle braking control (EBS/AEBS), and weigh-in-motion (WIM) sensing have highlighted that unmeasured vertical-load dynamics and time-varying friction are key sources of prediction uncertainty. To address these limitations, this study proposes an integrated sensing–simulation–AI framework that combines real-time axle-load estimation, full-scale robotic braking tests, fused road-friction sensing, and physics-consistent machine-learning modeling. A micro-electro-mechanical systems (MEMS)-based load-angle sensor was installed on the leaf-spring panel linking tandem axles, enabling the continuous estimation of dynamic vertical loads via a polynomial calibration model. Full-scale on-road braking tests were conducted at 40–60 km/h under systematically varied payloads (0–15.5 t) using an actuator-based braking robot to eliminate driver variability. A forward-looking optical friction module was synchronized with dynamic axle-load estimates and deceleration signals, and additional scenarios generated in a commercial ASM environment expanded the operational domain across a broader range of friction, grade, and loading conditions. A gradient-boosting regression model trained on the hybrid dataset reproduced measured stopping distances with a mean absolute error (MAE) of 1.58 m and a mean absolute percentage error (MAPE) of 2.46%, with most predictions falling within ±5 m across all test conditions. The results indicate that incorporating real-time dynamic axle-load sensing together with fused friction estimation improves braking-distance prediction compared with static-load assumptions and purely kinematic formulations. The proposed load-aware framework provides a scalable basis for advanced driver-assistance functions, autonomous emergency braking for heavy trucks, and infrastructure-integrated freight safety management. All full-scale braking tests were carried out at approximately 60% of the nominal service-brake pressure, representing non-panic but moderately severe braking conditions, and the proposed model is designed to accurately predict the resulting stopping distance under this prescribed braking regime rather than to minimize the absolute stopping distance itself. Full article

(This article belongs to the Topic Advances in Autonomous Vehicles, Automation, and Robotics)

►▼ Show Figures

Figure 1

43 pages, 8604 KB  

Open AccessArticle

Bibliometric and Visualization Analysis of Path Planning and Trajectory Tracking Research for Autonomous Vehicles from 2000 to 2025

by Bo Niu and Roman Y. Dobretsov

Sensors 2026, 26(3), 964; https://doi.org/10.3390/s26030964 - 2 Feb 2026

Cited by 1 | Viewed by 761

Abstract

With the rapid development of the automotive industry, autonomous driving has attracted growing research interest, among which path planning and trajectory tracking play a central role. To better understand the evolution, current status, and future directions of this field, this study conducts a [...] Read more.

With the rapid development of the automotive industry, autonomous driving has attracted growing research interest, among which path planning and trajectory tracking play a central role. To better understand the evolution, current status, and future directions of this field, this study conducts a comprehensive bibliometric analysis combined with latent Dirichlet allocation (LDA) topic modeling on publications related to autonomous vehicle path planning and trajectory tracking indexed in the Web of Science database. Multiple dimensions are examined, including publication trends, highly cited authors, leading institutions, research domains, and keyword co-occurrence patterns. The results reveal a sustained growth in research output, with trajectory planning, path optimization, trajectory tracking, and model predictive control (MPC) emerging as dominant topics, alongside a notable rise in learning-based approaches. In particular, reinforcement learning (RL) and deep reinforcement learning (DRL) have become increasingly prominent in complex decision-making and tracking control scenarios. The analysis further identifies core contributors and institutions, highlighting the leading roles of China and the United States in this research area. Overall, the findings provide a systematic overview of the knowledge structure and evolving research trends, offering valuable insights into key opportunities and challenges and supporting future research toward safer and more intelligent autonomous driving systems. Full article

(This article belongs to the Topic Advances in Autonomous Vehicles, Automation, and Robotics)

►▼ Show Figures

Figure 1

23 pages, 5835 KB  

Open AccessArticle

Stable and Smooth Trajectory Optimization for Autonomous Ground Vehicles via Halton-Sampling-Based MPPI

by Kang Xu, Lei Ye, Xiaohui Li, Zhenping Sun and Yafeng Bu

Drones 2026, 10(2), 96; https://doi.org/10.3390/drones10020096 - 29 Jan 2026

Viewed by 796

Abstract

Achieving safe and stable navigation for autonomous ground vehicles (AGVs) in complex environments remains a key challenge in intelligent robotics. Conventional Model Predictive Path Integral (MPPI) control relies on pseudo-random Gaussian sampling, which often results in non-uniform sample distributions and jitter-prone control sequences, [...] Read more.

Achieving safe and stable navigation for autonomous ground vehicles (AGVs) in complex environments remains a key challenge in intelligent robotics. Conventional Model Predictive Path Integral (MPPI) control relies on pseudo-random Gaussian sampling, which often results in non-uniform sample distributions and jitter-prone control sequences, thereby limiting both convergence efficiency and control stability. This paper proposes a trajectory optimization method: Halton-MPPI, which improves MPPI by employing low-discrepancy sampling and modeling temporally correlated perturbations. Specifically, it utilizes the Halton sequence as the sampling basis for control disturbances to enhance spatial coverage, while the Ornstein–Uhlenbeck (OU) process is introduced to impose temporal correlation on control perturbations. This time-consistent noise propagation allows perturbation effects to accumulate over time, thereby expanding trajectory coverage. Large-scale simulations on the BARN dataset demonstrate that the method significantly enhances both trajectory smoothness (MSCX) and control smoothness (MSCU) while maintaining high success rates. Moreover, field tests in outdoor environments validate the effectiveness and robustness of Halton-MPPI, underscoring its practical value for autonomous navigation in complex environments. Full article

(This article belongs to the Topic Advances in Autonomous Vehicles, Automation, and Robotics)

►▼ Show Figures

Figure 1

22 pages, 6827 KB  

Open AccessArticle

Control of Physically Connected Off-Road Skid-Steering Robotic Vehicles Based on Numerical Simulation and Neural Network Models

by Miša Tomić, Miloš Simonović, Vukašin Pavlović, Milan Banić and Miloš Milošević

Appl. Sci. 2026, 16(3), 1199; https://doi.org/10.3390/app16031199 - 23 Jan 2026

Viewed by 555

Abstract

The use of robots in various industries has increased significantly in recent years, with mobile robots playing a central role in automation. Their applications range from service robotics and automated material handling to bomb disposal and planetary exploration. A rapidly growing area of [...] Read more.

The use of robots in various industries has increased significantly in recent years, with mobile robots playing a central role in automation. Their applications range from service robotics and automated material handling to bomb disposal and planetary exploration. A rapidly growing area of mobile robotics involves coordinated groups of autonomous robots, commonly referred to as swarms. However, only a limited number of studies have addressed systems in which ropes or wires physically connect robots. Connecting multiple autonomous robotic vehicles with a tensioned wire can form a movable fence, enabling coordinated motion as a single dynamic entity. This paper presents a real-time control approach for the off-road motion of physically connected skid-steering robotic vehicles. A numerical-simulation-driven artificial neural network is employed as a surrogate model to estimate wheel–ground load distribution online, enabling stable steering control and accurate trajectory tracking on rough terrain while accounting for wire-induced coupling effects. Full article

(This article belongs to the Topic Advances in Autonomous Vehicles, Automation, and Robotics)

►▼ Show Figures

Figure 1

19 pages, 4525 KB  

Open AccessArticle

Path-Tracking Control for Agricultural Machinery by Integrating the Sideslip Angle into a Kinematic MPC

by Bingbo Cui, Hao Li, Ziyi Li, Zhen Ma and Yongyun Zhu

Electronics 2026, 15(2), 396; https://doi.org/10.3390/electronics15020396 - 16 Jan 2026

Viewed by 480

Abstract

Path tracking is a crucial part of agricultural machinery automatic navigation system (ANS) and has been extensively investigated in prior research. Although existing ANS designs perform satisfactorily under mild soil condition, path-tracking algorithms are often challenged by unknown disturbances arising from complicated field [...] Read more.

Path tracking is a crucial part of agricultural machinery automatic navigation system (ANS) and has been extensively investigated in prior research. Although existing ANS designs perform satisfactorily under mild soil condition, path-tracking algorithms are often challenged by unknown disturbances arising from complicated field environment and machine conditions. The current literature lacks a detailed analysis of the influence of the sideslip angle under specific operating speeds and path scenarios for agricultural machinery, which serves as the primary motivation for this study. In this paper, simulations are conducted for sprayers and harvesters across various paths, curvatures, and speeds to analyze the impact of sideslip on path-tracking performance. The results indicate that under the typical low-speed and large-curvature conditions of agricultural machinery, neglecting sideslip effects leads to a mismatch between the theoretical model and the actual vehicle motion. Compared to an MPC based on a kinematic model that disregards the sideslip angle, explicitly incorporating the sideslip angle into the kinematic model reduces the maximum lateral tracking error from 0.234 m to 0.174 m for a U-shaped path, and from 0.263 m to 0.194 m for a rectangular-shaped path. Simulation at different travel speeds further demonstrates that proposed algorithm achieves smaller sideslip amplitudes and faster attenuation after completing turns compared to conventional MPC. These findings offer valuable insights for the design of path-tracking algorithms in agricultural machinery autonomous driving systems. Full article

(This article belongs to the Topic Advances in Autonomous Vehicles, Automation, and Robotics)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-11