A Review of the Motion Planning and Control Methods for Automated Vehicles

Su, Xiaohua; Gao, Huile; Ding, Tian; Gu, Ying; Liu, Jing; Tian, Kun

doi:10.3390/s23136140

Cited by 7 publications

(5 citation statements)

References 92 publications

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“…The output of the decision task is usually a trajectory to follow and provided to the control/act layer. The latter generates the actuator signals for path tracking, adopting the usual control techniques such as proportional integral derivative (PID) control, linear quadratic regulator (LQR) control, and model predictive control (MPC) [25].…”

Section: Cav Architecturementioning

confidence: 99%

Architecture and Potential of Connected and Autonomous Vehicles

Pipicelli,

Gimelli,

Sessa

et al. 2024

Vehicles

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The transport sector is under an intensive renovation process. Innovative concepts such as shared and intermodal mobility, mobility as a service, and connected and autonomous vehicles (CAVs) will contribute to the transition toward carbon neutrality and are foreseen as crucial parts of future mobility systems, as demonstrated by worldwide efforts in research and industry communities. The main driver of CAVs development is road safety, but other benefits, such as comfort and energy saving, are not to be neglected. CAVs analysis and development usually focus on Information and Communication Technology (ICT) research themes and less on the entire vehicle system. Many studies on specific aspects of CAVs are available in the literature, including advanced powertrain control strategies and their effects on vehicle efficiency. However, most studies neglect the additional power consumption due to the autonomous driving system. This work aims to assess uncertain CAVs’ efficiency improvements and offers an overview of their architecture. In particular, a combination of the literature survey and proper statistical methods are proposed to provide a comprehensive overview of CAVs. The CAV layout, data processing, and management to be used in energy management strategies are discussed. The data gathered are used to define statistical distribution relative to the efficiency improvement, number of sensors, computing units and their power requirements. Those distributions have been employed within a Monte Carlo method simulation to evaluate the effect on vehicle energy consumption and energy saving, using optimal driving behaviour, and considering the power consumption from additional CAV hardware. The results show that the assumption that CAV technologies will reduce energy consumption compared to the reference vehicle, should not be taken for granted. In 75% of scenarios, simulated light-duty CAVs worsen energy efficiency, while the results are more promising for heavy-duty vehicles.

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Section: Cav Architecturementioning

confidence: 99%

Architecture and Potential of Connected and Autonomous Vehicles

Pipicelli,

Gimelli,

Sessa

et al. 2024

Vehicles

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“…Let the coordinates of the current node be (x n , y n ) and the coordinates of the target point be (x m , y m ). Euclidean distance heuristic function is expressed as Equation (2).…”

Section: Traditional A* Algorithmmentioning

confidence: 99%

“…The Automated Guided Vehicle (AGV), serving as the primary tool for intelligent material transportation, has gained widespread utilization in modern production systems. The incorporation of AGVs offers several advantages, including enhanced productivity, reduced labor costs, improved energy utilization, and heightened safety [2]. Path planning stands as a pivotal aspect of AGVs, with key metrics for assessing path quality encompassing path length, smoothness, and safety.…”

Section: Introductionmentioning

confidence: 99%

An Automated Guided Vehicle Path Planning Algorithm Based on Improved A* and Dynamic Window Approach Fusion

Guo,

Sun,

Liu

et al. 2023

Applied Sciences

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Aimed at the problems of low search efficiency of the A* algorithm in global path planning, not considering the size of AGV and too many turns, and the DWA algorithm easily falling into local optimization, an AGV path planning algorithm based on improved A* and DWA fusion is proposed. To begin, the obstacle rate coefficient is added to the A* algorithm’s evaluation function to build an adaptive cost function; the search efficiency and path safety are increased by improving the search mode; by extracting key nodes, a global path containing only the starting point, key nodes, and endpoints is obtained. The DWA algorithm’s evaluation function is then optimized and the starting azimuth is optimized based on information from the first key node. The experimental results show that in a static environment, compared with the traditional A* algorithm and the improved A* algorithm, the path length is reduced by 1.3% and 5.6%, respectively, and the turning times are reduced by 62.5% and 70%, respectively; compared with the improved ant colony algorithm in the literature, the turning angle is reduced by 29%. In the dynamic environment, the running time of this fusion algorithm is reduced by 12.6% compared with the other hybrid algorithms.

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“…Automatic guided vehicles (AGV) are intelligent industrial transportation devices characterized by their high autonomy, flexibility, programmable control, and adaptability [1]. Currently, they serve as a crucial component in the intelligent and automated production of industries.…”

Section: Introductionmentioning

confidence: 99%

Grid-Based Non-Uniform Probabilistic Roadmap-Based AGV Path Planning in Narrow Passages and Complex Environments

Zhou,

Lu,

2024

Electronics

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In this paper, we propose a Grid-based Non-uniform Probability Density Sampling Probabilistic Roadmap algorithm (GN-PRM) in response to the challenges of difficult sampling in narrow passages and low-probability map generation in traditional Probabilistic Roadmap algorithms (PRM). The improved algorithm incorporates grid-based processing for map segmentation, employing non-uniform probability density sampling based on the different attributes of each block to enhance sampling probability in narrow passages. Additionally, considering the computational cost and frequent ineffective searches in traditional PRM algorithms during pathfinding, this paper optimizes the time required for query route planning by altering connection strategies to improve the algorithm’s runtime. Finally, the simulation results indicate that, with a reduction of over 50% in undirected line segments and a reduction of over 85% in runtime, the GN-PRM algorithm achieves a 100% success rate in complex planning scenarios with a sampling point value of K = 500. In comparison, the traditional PRM algorithm has a success rate of no more than 10%, with a sampling point value of K = 500.

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A Review of the Motion Planning and Control Methods for Automated Vehicles

Cited by 7 publications

References 92 publications

Architecture and Potential of Connected and Autonomous Vehicles

Architecture and Potential of Connected and Autonomous Vehicles

An Automated Guided Vehicle Path Planning Algorithm Based on Improved A* and Dynamic Window Approach Fusion

Grid-Based Non-Uniform Probabilistic Roadmap-Based AGV Path Planning in Narrow Passages and Complex Environments

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