An Improved Autonomous Emergency Braking Algorithm for AGVs: Enhancing Operational Smoothness Through Multi-Stage Deceleration
The automated guided vehicle (AGV) is widely used in industrial environments for goods transportation. However, issues such as mechanical wear, reduced battery life, navigation error accumulation, and decreased operational efficiency caused by frequent starts and stops need to be addressed. This pap...
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| Veröffentlicht in: | Sensors (Basel, Switzerland) Jg. 25; H. 7; S. 2041 |
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| Abstract | The automated guided vehicle (AGV) is widely used in industrial environments for goods transportation. However, issues such as mechanical wear, reduced battery life, navigation error accumulation, and decreased operational efficiency caused by frequent starts and stops need to be addressed. This paper proposes an improved Autonomous Emergency Braking (AEB) algorithm to tackle these problems. The algorithm employs a stepwise deceleration strategy, effectively reducing the frequency of sudden stops and enhancing the system’s operational smoothness. The AEB algorithm not only considers straight-line driving scenarios but also optimizes deceleration strategies for turning scenarios, adjusting the deceleration detection range according to the turning trajectory. Additionally, a velocity smoothing algorithm is designed to ensure that speed changes during deceleration are gradual, avoiding abrupt speed variations that could impact the system. The feasibility of the AEB algorithm is validated through testing on actual equipment, and its performance is compared to that of a conventional emergency stop strategy. Experimental results show that the AEB algorithm significantly reduces the number of sudden stops, improves the AGV’s operational smoothness and safety, and demonstrates excellent adaptability and robustness across different operational conditions. |
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| AbstractList | The automated guided vehicle (AGV) is widely used in industrial environments for goods transportation. However, issues such as mechanical wear, reduced battery life, navigation error accumulation, and decreased operational efficiency caused by frequent starts and stops need to be addressed. This paper proposes an improved Autonomous Emergency Braking (AEB) algorithm to tackle these problems. The algorithm employs a stepwise deceleration strategy, effectively reducing the frequency of sudden stops and enhancing the system’s operational smoothness. The AEB algorithm not only considers straight-line driving scenarios but also optimizes deceleration strategies for turning scenarios, adjusting the deceleration detection range according to the turning trajectory. Additionally, a velocity smoothing algorithm is designed to ensure that speed changes during deceleration are gradual, avoiding abrupt speed variations that could impact the system. The feasibility of the AEB algorithm is validated through testing on actual equipment, and its performance is compared to that of a conventional emergency stop strategy. Experimental results show that the AEB algorithm significantly reduces the number of sudden stops, improves the AGV’s operational smoothness and safety, and demonstrates excellent adaptability and robustness across different operational conditions. The automated guided vehicle (AGV) is widely used in industrial environments for goods transportation. However, issues such as mechanical wear, reduced battery life, navigation error accumulation, and decreased operational efficiency caused by frequent starts and stops need to be addressed. This paper proposes an improved Autonomous Emergency Braking (AEB) algorithm to tackle these problems. The algorithm employs a stepwise deceleration strategy, effectively reducing the frequency of sudden stops and enhancing the system's operational smoothness. The AEB algorithm not only considers straight-line driving scenarios but also optimizes deceleration strategies for turning scenarios, adjusting the deceleration detection range according to the turning trajectory. Additionally, a velocity smoothing algorithm is designed to ensure that speed changes during deceleration are gradual, avoiding abrupt speed variations that could impact the system. The feasibility of the AEB algorithm is validated through testing on actual equipment, and its performance is compared to that of a conventional emergency stop strategy. Experimental results show that the AEB algorithm significantly reduces the number of sudden stops, improves the AGV's operational smoothness and safety, and demonstrates excellent adaptability and robustness across different operational conditions.The automated guided vehicle (AGV) is widely used in industrial environments for goods transportation. However, issues such as mechanical wear, reduced battery life, navigation error accumulation, and decreased operational efficiency caused by frequent starts and stops need to be addressed. This paper proposes an improved Autonomous Emergency Braking (AEB) algorithm to tackle these problems. The algorithm employs a stepwise deceleration strategy, effectively reducing the frequency of sudden stops and enhancing the system's operational smoothness. The AEB algorithm not only considers straight-line driving scenarios but also optimizes deceleration strategies for turning scenarios, adjusting the deceleration detection range according to the turning trajectory. Additionally, a velocity smoothing algorithm is designed to ensure that speed changes during deceleration are gradual, avoiding abrupt speed variations that could impact the system. The feasibility of the AEB algorithm is validated through testing on actual equipment, and its performance is compared to that of a conventional emergency stop strategy. Experimental results show that the AEB algorithm significantly reduces the number of sudden stops, improves the AGV's operational smoothness and safety, and demonstrates excellent adaptability and robustness across different operational conditions. |
| Audience | Academic |
| Author | Qiu, Junting Li, Wenbo |
| AuthorAffiliation | 2 College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China 1 School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; 2235051120@st.usst.edu.cn |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40218555$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.aap.2022.106709 10.1016/j.jmsy.2019.12.002 10.3390/electronics8020159 10.1007/978-3-319-75677-6_32 10.1080/15389588.2023.2227304 10.3390/ijgi13050163 10.1080/13504851.2021.1917760 10.3390/electronics11244175 10.1016/j.cosrev.2024.100651 10.1016/j.jclepro.2016.10.057 10.1080/15389588.2022.2117983 10.1016/j.vrih.2019.09.001 10.1080/15389588.2018.1533247 10.1007/s12239-018-0098-9 10.1016/j.proeng.2017.06.085 10.1080/15389588.2024.2394975 10.1109/IV47402.2020.9304749 |
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| SubjectTerms | AEB algorithm AGV Algorithms Automatic guided vehicles Automation Batteries Design industrial automation Kinematics Localization obstacle avoidance algorithm path planning Vehicles Velocity velocity smoothing Wheels |
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| Title | An Improved Autonomous Emergency Braking Algorithm for AGVs: Enhancing Operational Smoothness Through Multi-Stage Deceleration |
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