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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: closed (6 November 2023) | Viewed by 42511

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Special Issue Editors

Prof. Dr. Bai Li

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Guest Editor

College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
Interests: motion planning; computational optimal control; numerical optimization
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Youmin Zhang

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Guest Editor

Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, QC H3G 1M8, Canada
Interests: avionics and flight control for manned and unmanned aerial vehicles; monitoring; fault detection and diagnosis (FDD); fault-tolerant (flight) control systems; intelligent and hybrid control systems; UAVs and remote sensing techniques
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Xiaohui Li

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Guest Editor

College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: artificial intelligence; motion planning; control for intelligent systems

Prof. Dr. Tankut Acarman

E-Mail Website
Guest Editor

Department of Computer Engineering, Galatasaray University, Istanbul 34349, Turkey
Interests: intelligent vehicle technologies; driver assistance systems; performance evaluation of inter-vehicle communication

Special Issue Information

Dear Colleagues,

An autonomous vehicle refers to one that runs without a human driver. There has been rapid progress made in the applications of autonomous vehicles on a structured urban road or in an unstructured indoor scenario. Planning and control are two critical modules in an autonomous vehicle system. Concretely, the planning module is responsible for generating an open-loop trajectory, while the control module is to track the desired reference trajectory from the planning module in a closed-loop way and under all possible road, weather, disturbing driving conditions, including even abnormal conditions such as physical failures and cyberattacks. The planning and control modules are important as they directly reflect the intelligence level of an autonomous system. The purpose of this Special Issue is to present the most recent advances in the planning or control methodologies used for an autonomous vehicle. Submitted papers should focus on how the proposed planning and/or control method can solve real-world problems. The editorial board will maintain a high standard to prescreen the submissions which simply propose a generic method without sufficient discussions on its potential to address the real-world bottleneck problems in the community of autonomous driving. Note that we also welcome papers that discuss methods relevant to planning or control, as long as they can make the planning or control module perform better.

Topics of interest include but are not limited to:

Path/trajectory/motion planning and replanning;
Path/trajectory/motion control;
On-road/off-road planning and control;
Modeling and simulation method for planning and/or control;
Testing and validation method related to planning and/or control;
Safety-related issues with planning and control;
Security-related issues with planning and control;
Human–machine interaction related to planning and/or control;
Intelligent techniques/methods to planning and/or control;
Integration of planning and control;
Reviews of planning or control methodologies;
Data-driven/model-based planning or control;
Comparisons among different types of planning or control methods;
Fault-tolerant planning and control;
Cooperative planning and control;
Real-world applications of planning and control.

Prof. Dr. Bai Li
Prof. Dr. Youmin Zhang
Prof. Dr. Xiaohui Li
Prof. Dr. Tankut Acarman
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. Electronics 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 2400 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

motion planning
path planning
trajectory planning
motion control
path tracking
trajectory tracking
autonomous driving
unmanned system

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Recent Advances in Motion Planning and Control of Autonomous Vehicles, 2nd Edition in Electronics (4 articles)

Published Papers (12 papers)

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Editorial

Jump to: Research

6 pages, 182 KiB

Open AccessEditorial

Recent Advances in Motion Planning and Control of Autonomous Vehicles

by Bai Li, Xiaoming Chen, Tankut Acarman, Xiaohui Li and Youmin Zhang

Electronics 2023, 12(23), 4881; https://doi.org/10.3390/electronics12234881 - 4 Dec 2023

Viewed by 5583

Abstract

An autonomous vehicle operates without human intervention, marking advancements in navigating structured urban roads and unstructured environments [...] Full article

(This article belongs to the Special Issue Recent Advances in Motion Planning and Control of Autonomous Vehicles)

Research

Jump to: Editorial

19 pages, 2768 KiB

Open AccessArticle

Tube-Based Event-Triggered Path Tracking for AUV against Disturbances and Parametric Uncertainties

by Yuheng Chen and Yougang Bian

Electronics 2023, 12(20), 4248; https://doi.org/10.3390/electronics12204248 - 13 Oct 2023

Cited by 5 | Viewed by 1689

Abstract

In order to enhance the performance of disturbance rejection in AUV’s path tracking, this paper proposes a novel tube-based event-triggered path-tracking strategy. The proposed tracking strategy consists of a speed control law and an event-triggered tube model predictive control (tube MPC) scheme. Firstly, the speed control law using linear model predictive control (LMPC) technology is obtained to converge the nominal path-tracking deviation. Secondly, the event-triggered tube MPC scheme is used to calculate the optimal control input, which can enhance the performance of disturbance rejection. Considering the nonlinear hydrodynamic characteristics of AUV, a linear matrix inequality (LMI) is formulated to obtain tight constraints on the AUV and the feedback matrix. Moreover, to enhance real-time performance, tight constraints and the feedback matrix are all calculated offline. An event-triggering mechanism is used. When the surge speed change command does not exceed the upper bound, adaptive tight constraints are obtained. Finally, numerical simulation results show that the proposed tube-based event-triggered path-tracking strategy can enhance the performance of disturbance rejection and ensure good real-time performance. Full article

(This article belongs to the Special Issue Recent Advances in Motion Planning and Control of Autonomous Vehicles)

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26 pages, 6481 KiB

Open AccessArticle

Joint Dispatching and Cooperative Trajectory Planning for Multiple Autonomous Forklifts in a Warehouse: A Search-and-Learning-Based Approach

by Tantan Zhang, Hu Li, Yong Fang, Man Luo and Kai Cao

Electronics 2023, 12(18), 3820; https://doi.org/10.3390/electronics12183820 - 9 Sep 2023

Cited by 3 | Viewed by 1652

Abstract

Dispatching and cooperative trajectory planning for multiple autonomous forklifts in a warehouse is a widely applied research topic. The conventional methods in this domain regard dispatching and planning as isolated procedures, which render the overall motion quality of the forklift team imperfect. The dispatching and planning problems should be considered simultaneously to achieve optimal cooperative trajectories. However, this approach renders a large-scale nonconvex problem, which is extremely difficult to solve in real time. A joint dispatching and planning method is proposed to balance solution quality and speed. The proposed method is characterized by its fast runtime, light computational burden, and high solution quality. In particular, the candidate goals of each forklift are enumerated. Each candidate dispatch solution is measured after concrete trajectories are generated via an improved hybrid A* search algorithm, which is incorporated with an artificial neural network to improve the cost evaluation process. The proposed joint dispatching and planning method is computationally cheap, kinematically feasible, avoids collisions with obstacles/forklifts, and finds the global optimum quickly. The presented motion planning strategy demonstrates that the integration of a neural network with the dispatching approach leads to a warehouse filling/emptying mission completion time that is 2% shorter than the most efficient strategy lacking machine-learning integration. Notably, the mission completion times across these strategies vary by approximately 15%. Full article

(This article belongs to the Special Issue Recent Advances in Motion Planning and Control of Autonomous Vehicles)

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16 pages, 2750 KiB

Open AccessArticle

Micro-Factors-Aware Scheduling of Multiple Autonomous Trucks in Open-Pit Mining via Enhanced Metaheuristics

by Yong Fang and Xiaoyan Peng

Electronics 2023, 12(18), 3793; https://doi.org/10.3390/electronics12183793 - 7 Sep 2023

Cited by 8 | Viewed by 1910

Abstract

Traditional open-pit mineral transportation systems are typically subject to manual command, frequently leading to vehicular delays and traffic congestion. With the advancement of automation and electrification technologies, this study proposes a highly accurate scheduling method for multiple autonomous trucks in an open-pit mine. This model considers micro-level temporal and spatial factors to tackle the task of scheduling autonomous trucks within open-pit mines. The cost function of the concerned scheduling problem is a comprehensive evaluation of energy consumption, time, and output. Beyond the loading and unloading activities, the model also factors in the charging requirements of autonomous trucks in mining regions. The scheduling model integrates a Voronoi diagram search and optimal spatial path time matching, aiming to provide superior mission planning and decision-making solutions for autonomous trucks in mining regions. For an efficient solution to the scheduling problem, we propose an improved-evolution artificial bee colony (IE-ABC) algorithm. This algorithm improves the global search and re-initialization processes and conducts algorithm ablation experiments to closely examine their impact on optimization. Simulation results across various algorithms, cost function definition strategy, and encoding strategy show that our method can improve scheduling performance in energy consumption and time. Experimental results demonstrate that the proposed model and algorithm can effectively solve the scheduling decision-making problem in an unmanned open-pit mine. Full article

(This article belongs to the Special Issue Recent Advances in Motion Planning and Control of Autonomous Vehicles)

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19 pages, 9103 KiB

Open AccessArticle

GIS-Data-Driven Efficient and Safe Path Planning for Autonomous Ships in Maritime Transportation

by Xiao Hu, Kai Hu, Datian Tao, Yi Zhong and Yi Han

Electronics 2023, 12(10), 2206; https://doi.org/10.3390/electronics12102206 - 12 May 2023

Cited by 7 | Viewed by 2268

Abstract

Maritime transportation is vital to the global economy. With the increased operating and labor costs of maritime transportation, autonomous shipping has attracted much attention in both industry and academia. Autonomous shipping can not only reduce the marine accidents caused by human factors but also save labor costs. Path planning is one of the key technologies to enable the autonomy of ships. However, mainstream ship path planning focuses on searching for the shortest path and controlling the vehicle in order to track it. Such path planning methods may lead to a dynamically infeasible trajectory that fails to avoid obstacles or reduces fuel efficiency. This paper presents a data-driven, efficient, and safe path planning (ESP) method that considers ship dynamics to provide a real-time optimal trajectory generation. The optimization objectives include fuel consumption and trajectory smoothness. Furthermore, ESP is capable of fast replanning when encountering obstacles. ESP consists of three components: (1) A path search method that finds an optimal search path with the minimum number of sharp turns from the geographic data collected by the geographic information system (GIS); (2) a minimum-snap trajectory optimization formulation with dynamic ship constraints to provide a smooth and collision-free trajectory with minimal fuel consumption; (3) a local trajectory replanner based on B-spline to avoid unexpected obstacles in real time. We evaluate the performance of ESP by data-driven simulations. The geographical data have been collected and updated from GIS. The results show that ESP can plan a global trajectory with safety, minimal turning points, and minimal fuel consumption based on the maritime information provided by nautical charts. With the long-range perception of onboard radars, the ship can avoid unexpected obstacles in real time on the planned global course. Full article

(This article belongs to the Special Issue Recent Advances in Motion Planning and Control of Autonomous Vehicles)

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21 pages, 14945 KiB

Open AccessArticle

Trajectory Planning for an Articulated Tracked Vehicle and Tracking the Trajectory via an Adaptive Model Predictive Control

by Kangle Hu and Kai Cheng

Electronics 2023, 12(9), 1988; https://doi.org/10.3390/electronics12091988 - 24 Apr 2023

Cited by 9 | Viewed by 2633

Abstract

This paper focuses on the trajectory planning and trajectory tracking control of articulated tracked vehicles (ATVs). It utilizes the path planning method based on the Hybrid A-star and the minimum snap smoothing method to obtain the feasible kinematic trajectory. To overcome the highly non-linearity of ATVs, we proposed a linear-parameter-varying (LPV) kinematic tracking-error model. Then, the kinematic controller was formulated as the adaptive model predictive controller (AMPC). The simulation of the path planning algorithm showed that the proposed planning strategy could provide a feasible trajectory for the ATVs passing through the obstacles. Moreover, we compared the AMPC controller with the developed controller in four scenarios. The comparison showed that the AMPC controller achieved satisfactory tracking errors regarding the lateral position and orientation angle errors. The maximum lateral distance error by the AMPC controller has been reduced by 72.4% compared to the standard-MPC controller. The maximum orientation angle error has been reduced by 55.53%. The simulation results confirmed that the proposed trajectory planning and tracking control system could effectively perform the automated driving behaviors for ATVs. Full article

(This article belongs to the Special Issue Recent Advances in Motion Planning and Control of Autonomous Vehicles)

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19 pages, 1270 KiB

Open AccessArticle

Space Discretization-Based Optimal Trajectory Planning for Automated Vehicles in Narrow Corridor Scenes

by Biao Xu, Shijie Yuan, Xuerong Lin, Manjiang Hu, Yougang Bian and Zhaobo Qin

Electronics 2022, 11(24), 4239; https://doi.org/10.3390/electronics11244239 - 19 Dec 2022

Cited by 1 | Viewed by 2834

Abstract

The narrow corridor is a common working scene for automated vehicles, where it is pretty challenging to plan a safe, feasible, and smooth trajectory due to the narrow passable area constraints. This paper presents a space discretization-based optimal trajectory planning method for automated vehicles in a narrow corridor scene with the consideration of travel time minimization and boundary collision avoidance. In this method, we first design a mathematically-described driving corridor model. Then, we build a space discretization-based trajectory optimization model in which the objective function is travel efficiency, and the vehicle-kinematics constraints, collision avoidance constraints, and several other constraints are proposed to ensure the feasibility and comfortability of the planned trajectory. Finally, the proposed method is verified with both simulations and field tests. The experimental results demonstrate the trajectory planned by the proposed method is smoother and more computationally efficient compared with the baseline methods while significantly reducing the tracking error indicating the proposed method has huge application potential in trajectory planning in the narrow corridor scenario for automated vehicles. Full article

(This article belongs to the Special Issue Recent Advances in Motion Planning and Control of Autonomous Vehicles)

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12 pages, 2487 KiB

Open AccessArticle

A Hybrid Asynchronous Brain–Computer Interface Based on SSVEP and Eye-Tracking for Threatening Pedestrian Identification in Driving

by Jianxiang Sun and Yadong Liu

Electronics 2022, 11(19), 3171; https://doi.org/10.3390/electronics11193171 - 2 Oct 2022

Cited by 6 | Viewed by 2330

Abstract

A brain–computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has achieved remarkable performance in the field of automatic driving. Prolonged SSVEP stimuli can cause driver fatigue and reduce the efficiency of interaction. In this paper, a multi-modal hybrid asynchronous BCI system combining eye-tracking and EEG signals is proposed for dynamic threatening pedestrian identification in driving. Stimuli arrows of different frequencies and directions are randomly superimposed on pedestrian targets. Subjects scan the stimuli according to the direction of arrows until the threatening pedestrian is selected. The thresholds determined by offline experiments are used to distinguish between working and idle states of the asynchronous online experiments. Subjects need to judge and select potentially threatening pedestrians in online experiments according to their own subjective experience. The three proposed decisions filter out the results with low confidence and effectively improve the selection accuracy of hybrid BCI. The experimental results of six subjects show that the proposed hybrid asynchronous BCI system achieves better performance compared with a single SSVEP-BCI, with an average selection time of 1.33 s, an average selection accuracy of 95.83%, and an average information transfer rate (ITR) of 67.50 bits/min. These results indicate that our hybrid asynchronous BCI has great application potential in dynamic threatening pedestrian identification in driving. Full article

(This article belongs to the Special Issue Recent Advances in Motion Planning and Control of Autonomous Vehicles)

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17 pages, 10203 KiB

Open AccessArticle

Real-Time Drift-Driving Control for an Autonomous Vehicle: Learning from Nonlinear Model Predictive Control via a Deep Neural Network

by Taekgyu Lee, Dongyoon Seo, Jinyoung Lee and Yeonsik Kang

Electronics 2022, 11(17), 2651; https://doi.org/10.3390/electronics11172651 - 24 Aug 2022

Cited by 8 | Viewed by 6629

Abstract

A drift-driving maneuver is a control technique used by an expert driver to control a vehicle along a sharply curved path or slippery road. This study develops a nonlinear model predictive control (NMPC) method for the autonomous vehicle to perform a drift maneuver and generate the datasets necessary for training the deep neural network(DNN)-based drift controller. In general, the NMPC method is based on numerical optimization which is difficult to run in real-time. By replacing the previously designed NMPC method with the proposed DNN-based controller, we avoid the need for complex numerical optimization of the vehicle control, thereby reducing the computational load. The performance of the developed data-driven drift controller is verified through realistic simulations that included drift scenarios. Based on the results of the simulations, the DNN-based controller showed similar tracking performance to the original nonlinear model predictive controller; moreover, the DNN-based controller can demonstrate stable computation time, which is very important for the safety critical control objective such as drift maneuver. Full article

(This article belongs to the Special Issue Recent Advances in Motion Planning and Control of Autonomous Vehicles)

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22 pages, 3799 KiB

Open AccessArticle

A Hybrid and Hierarchical Approach for Spatial Exploration in Dynamic Environments

by Qi Zhang, Yukai Song, Peng Jiao and Yue Hu

Electronics 2022, 11(4), 574; https://doi.org/10.3390/electronics11040574 - 14 Feb 2022

Cited by 2 | Viewed by 2202

Abstract

Exploration in unknown dynamic environments is a challenging problem in an AI system, and current techniques tend to produce irrational exploratory behaviours and fail in obstacle avoidance. To this end, we present a three-tiered hierarchical and modular spatial exploration model that combines the intrinsic motivation integrated deep reinforcement learning (DRL) and rule-based real-time obstacle avoidance approach. We address the spatial exploration problem in two levels on the whole. On the higher level, a DRL based global module learns to determine a distant but easily reachable target that maximizes the current exploration progress. On the lower level, another two-level hierarchical movement controller is used to produce locally smooth and safe movements between targets based on the information of known areas and free space assumption. Experimental results on diverse and challenging 2D dynamic maps show that the proposed model achieves almost 90% coverage and generates smoother trajectories compared with a state-of-the-art IM based DRL and some other heuristic methods on the basis of avoiding obstacles in real time. Full article

(This article belongs to the Special Issue Recent Advances in Motion Planning and Control of Autonomous Vehicles)

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18 pages, 5920 KiB

Open AccessArticle

A Path Planning Method for Underground Intelligent Vehicles Based on an Improved RRT* Algorithm

by Hao Wang, Guoqing Li, Jie Hou, Lianyun Chen and Nailian Hu

Electronics 2022, 11(3), 294; https://doi.org/10.3390/electronics11030294 - 18 Jan 2022

Cited by 59 | Viewed by 5963

Abstract

Path planning is one of the key technologies for unmanned driving of underground intelligent vehicles. Due to the complexity of the drift environment and the vehicle structure, some improvements should be made to adapt to underground mining conditions. This paper proposes a path planning method based on an improved RRT* (Rapidly-Exploring Random Tree Star) algorithm for solving the problem of path planning for underground intelligent vehicles based on articulated structure and drift environment conditions. The kinematics of underground intelligent vehicles are realized by vectorized map and dynamic constraints. The RRT* algorithm is selected for improvement, including dynamic step size, steering angle constraints, and optimal tree reconnection. The simulation case study proves the effectiveness of the algorithm, with a lower path length, lower node count, and 100% steering angle efficiency. Full article

(This article belongs to the Special Issue Recent Advances in Motion Planning and Control of Autonomous Vehicles)

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16 pages, 6273 KiB

Open AccessArticle

Occlusion-Aware Path Planning to Promote Infrared Positioning Accuracy for Autonomous Driving in a Warehouse

by Bai Li, Shiqi Tang, Youmin Zhang and Xiang Zhong

Electronics 2021, 10(24), 3093; https://doi.org/10.3390/electronics10243093 - 13 Dec 2021

Cited by 2 | Viewed by 3999

Abstract

Infrared positioning is a critical module in an indoor autonomous vehicle platform. In an infrared positioning system, the ego vehicle is equipped with an infrared emitter while the infrared receivers are fixed onto the ceiling. The infrared positioning result is accurate only when the number of valid infrared receivers is more than three. An infrared receiver easily becomes invalid if it does not receive light from the infrared emitter due to indoor occlusions. This study proposes an occlusion-aware path planner that enables an autonomous vehicle to navigate toward the occlusion-free part of the drivable area. The planner consists of four layers. In layer one, a homotopic A* path is searched for in the 2D grid map to roughly connect the initial and goal points. In layer two, a curvature-continuous reference line is planned close to the A* path using numerical optimal control. In layer three, a Frenet frame is constructed along the reference line, followed by a search for an occlusion-aware path within that frame via dynamic programming. In layer four, a curvature-continuous path is optimized via quadratic programming within the Frenet frame. A path planned within the Frenet frame may violate the curvature bounds in a real-world Cartesian frame; thus, layer four is implemented through trial and error. Simulation results in CarSim software show that the derived paths reduce the poor positioning risk and are easily tracked by a controller. Full article

(This article belongs to the Special Issue Recent Advances in Motion Planning and Control of Autonomous Vehicles)

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