Safety-field-based Path Planning Algorithm of Lane Changing for Autonomous Vehicles

Lv, Xiaoxing; Li, Weihua; Wang, Jianfeng

doi:10.1007/s12555-020-0942-3

Cited by 11 publications

(6 citation statements)

References 22 publications

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“…This enables adaptive safety measures that can dynamically adjust to changing conditions. This is commonly used in the control of autonomous vehicles, as it supports adaptive safety measures, enabling vehicles to dynamically adjust their behaviour in response to changing traffic conditions [36][37][38].…”

Section: Related Workmentioning

confidence: 99%

Safe and Reconfigurable Manufacturing: Safety Aware Multi-Agent Control for Plug & Produce System.

Massouh,

Danielsson,

Lennartson

et al. 2024

Preprint

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Plug & Produce is a concept that is envisioned to revolutionise manufacturing by enabling the seamless integration of machines into production processes without extensive programming, offering increased flexibility, and faster production ramp-up times after reconfiguration. This concept implies a smart and advanced system control architecture typically achieved with Multi-Agent Systems (MAS). In the context of moving towards more human participation in smart manufacturing, the realization of the Plug & Produce concept depends on enabling the safe operation of the manufacturing system which is the focus of this paper. The main challenge to safe operation comes from autonomous decision-making, as agents in the multi-agent system are typically not aware of the risk consequence of their behaviour. Another challenge is the difficulty of perceiving the exact behaviour of the system which leads to the implementation of overly restrictive safety measures. This in turn limits the system’s flexibility and ability to make decisions for efficient production. This paper proposes a method utilizing multi-agent control of Plug & Produce to conduct automatic safety analysis and reason the task allocations to avoid risks. The benefit of this method is it enables generating control actions in compliance with safety requirements during operation. Also, the method eliminates the need for overly restrictive safety measures, this flexibility allows for more effective utilization of equipment while maintaining safety requirements. Illustrated through a manufacturing scenario with two different reconfigurations, safety requirements are proven satisfied by the formal verification of the proposed method using the model checker NuSMV. The formal verification results confirm the benefits of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Safe and Reconfigurable Manufacturing: Safety Aware Multi-Agent Control for Plug & Produce System.

Massouh,

Danielsson,

Lennartson

et al. 2024

Preprint

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show abstract

“…In recent years, learning-based algorithms were proposed [27]- [30]. The path planning problem is also widely studied in relation to ground vehicles [31]- [33] and unmanned aerial vehicles [34]- [37].…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Indoor Path Planning and Clearance-Based Path Optimization for Search and Rescue Operations

et al. 2023

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In this study a multi-level path planning system is proposed for indoor search and rescue operations. Requirements for the path planning system are derived based on the operational concept of the integrated indoor navigation system. Different aspects of various path planning algorithms are assessed, and their suitability to search and rescue operations in structured indoor environments is investigated. A fivestep path planning system is proposed, which consists of map pre-processing, segment path planning, graph processing, route optimization, and path post-processing. The proposed method addresses a multi-goal path planning problem in a multi-story building in a computationally efficient way by adopting a graph-based approach while satisfying such requirements as clearance conditions in the pre-and post-processing steps. Furthermore, a multi-query approach is adopted to exploit the response time and earn flexibility with respect to environmental changes. The effectiveness of the proposed path planning system is demonstrated through numerical simulations.INDEX TERMS Path planning, indoor navigation, path optimization, linear programming, dynamic environment.

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“…These applications require robots to execute complete coverage paths within the accessible workspace. Various recent approaches are presented in [1][2][3][4]. However, planning such paths in multi-robot scenarios presents several challenges, including task duplication, coordination between robots, and optimal task allocation.…”

Section: Introductionmentioning

confidence: 99%

Robust Online Multi-robot Simultaneous Exploration and Coverage Path Planning

Nair,

2023

Preprint

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In this paper, a robust online Multi-robot Simultaneous exploration and coveragepath planning problem is presented. The entire workspace is initially partitionedusing the Manhattan Voronoi partitioning method, and the robots execute Multi-Robot Simultaneous Exploration and Coverage (MRSimExCoverage) using theSpanning Tree Coverage (STC) algorithm and cover the workspace. Once the robot(s) failure is detected the uncovered portions of the Voronoi cell of the failedrobot will be shared between other eligible robots or a replacement strategy,ifavailable, is performed. Simulation experiments within the V-rep environment isused to demonstrate and validate the performance of the proposed algorithm.Though the authors used the STC algorithm for path planning for the purposeof demonstration, any suitable coverage algorithm may be used.

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Safety-field-based Path Planning Algorithm of Lane Changing for Autonomous Vehicles

Cited by 11 publications

References 22 publications

Safe and Reconfigurable Manufacturing: Safety Aware Multi-Agent Control for Plug & Produce System.

Safe and Reconfigurable Manufacturing: Safety Aware Multi-Agent Control for Plug & Produce System.

Multi-Level Indoor Path Planning and Clearance-Based Path Optimization for Search and Rescue Operations

Robust Online Multi-robot Simultaneous Exploration and Coverage Path Planning

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Safety-field-based Path Planning Algorithm of Lane Changing for Autonomous Vehicles

Cited by 11 publications

References 22 publications

Safe and Reconfigurable Manufacturing: Safety Aware Multi-Agent Control for Plug &amp; Produce System.

Safe and Reconfigurable Manufacturing: Safety Aware Multi-Agent Control for Plug &amp; Produce System.

Multi-Level Indoor Path Planning and Clearance-Based Path Optimization for Search and Rescue Operations

Robust Online Multi-robot Simultaneous Exploration and Coverage Path Planning

Contact Info

Product

Resources

About

Safe and Reconfigurable Manufacturing: Safety Aware Multi-Agent Control for Plug & Produce System.

Safe and Reconfigurable Manufacturing: Safety Aware Multi-Agent Control for Plug & Produce System.