Path Planning of Unmanned Ground Vehicle in Static and Dynam
发布时间：2021-12-16 00:19
在当今时代,无人驾驶地面车辆（UGV）在不同领域的应用逐渐增加,这些领域包括灾害管理,工业,军事和应用在日常生活中帮助人们提高他们安全性和舒适性的领域。在这种情况下,UGV需要进一步提高其智能决策能力,以便在静态和动态约束环境中顺利完成所需任务。其中,路径规划被认为是UGV运动规划中的一项具有挑战性的任务。基于环境复杂性,路径规划被分为全局路径规划（GPP）和本地路径规划（LPP）的两种方法。GPP方法在静态约束环境中生成从起始位置到目的地的合适路径,在此过程中可能出现许多意外的约束。LPP方法则是为了车辆能够避免这些障碍并能继续在参考全局轨迹上移动。本文提供了一种集合算法,该算法通过生成具有局部规划能力的平滑全局路径,避免了不同复杂度地图中的意外障碍,提高了UGV的安全性,路径一致性和,改善了轨迹规划的能力。1.蚁群优化（ACO）是一种进化方法。由于强大的鲁棒性,良好的反馈信息和更好的分布式计算等特性,它已成为一种著名的方法,它可以很容易地与不同的技术相结合,以提供所需的最佳解决方案。本文利用A*算法的评估函数进一步改进了ACO,利用更好的启发式信息,加快了蚁群算法搜索策略的收敛速度...
【文章来源】：电子科技大学四川省 211工程院校 985工程院校 教育部直属院校
【文章页数】：66 页
【学位级别】：硕士
【文章目录】：
摘要
ABSTRACT
Chapter 1 Introduction
1.1 Research Background and Significance
1.2 Research Motivation
1.3 Contribution to the knowledge
1.4 Research applications
1.5 Structure of Thesis
Chapter 2 Literature Review
2.1 Introduction
2.2 Chapter Organization
2.3 Motion Planning
2.4 Path Planning
2.4.1 Global Path Planning(GPP)
2.4.2 Local Path Planning(LPP)
2.5 Problem statement
2.6 Related Work
2.7 Chapter Summary
Chapter 3 Global Path Planning
3.1 Introduction
3.2 Chapter Organization
3.3 Grid-Based Environment Model
3.4 Brief introduction of A* and Ant Colony Optimization(ACO) Algorithm
3.4.1 A* Algorithm
3.4.2 Ant Colony Algorithm
3.5 Improved Ant colony algorithm
3.5.1 Improved ACO with Initial constraints policy
3.5.2 Initial constraint policy
3.5.3.Heuristic information and path strategy information
3.5.4 MAX–MIN Ant System(MMAS)
3.6 Evaluation function and cost policy
3.6.1 Linear interpolation(LI)
3.6.2 Cost policy
3.6.3 Arc-length Parametrization with cubic spline
3.7 Simulation Results
3.8 Chapter summary
Chapter 4 Local Path Planning
4.1 Introduction
4.2 Chapter organization
4.3 Formation of lateral and longitudinal coordinates
4.4 Transformation of lateral and longitudinal coordinates into Cartesian coordinates along with Cost function and constraint check
4.5 Simulation results in dynamic environment
4.5.1 Simulation in static and dynamic constraints environment
4.6 Chapter Summary
Chapter 5 Conclusions
5.1 Concluding Remarks
Acknowledgement
References
Published Papers
本文编号：3537396
【文章来源】：电子科技大学四川省 211工程院校 985工程院校 教育部直属院校
【文章页数】：66 页
【学位级别】：硕士
【文章目录】：
摘要
ABSTRACT
Chapter 1 Introduction
1.1 Research Background and Significance
1.2 Research Motivation
1.3 Contribution to the knowledge
1.4 Research applications
1.5 Structure of Thesis
Chapter 2 Literature Review
2.1 Introduction
2.2 Chapter Organization
2.3 Motion Planning
2.4 Path Planning
2.4.1 Global Path Planning(GPP)
2.4.2 Local Path Planning(LPP)
2.5 Problem statement
2.6 Related Work
2.7 Chapter Summary
Chapter 3 Global Path Planning
3.1 Introduction
3.2 Chapter Organization
3.3 Grid-Based Environment Model
3.4 Brief introduction of A* and Ant Colony Optimization(ACO) Algorithm
3.4.1 A* Algorithm
3.4.2 Ant Colony Algorithm
3.5 Improved Ant colony algorithm
3.5.1 Improved ACO with Initial constraints policy
3.5.2 Initial constraint policy
3.5.3.Heuristic information and path strategy information
3.5.4 MAX–MIN Ant System(MMAS)
3.6 Evaluation function and cost policy
3.6.1 Linear interpolation(LI)
3.6.2 Cost policy
3.6.3 Arc-length Parametrization with cubic spline
3.7 Simulation Results
3.8 Chapter summary
Chapter 4 Local Path Planning
4.1 Introduction
4.2 Chapter organization
4.3 Formation of lateral and longitudinal coordinates
4.4 Transformation of lateral and longitudinal coordinates into Cartesian coordinates along with Cost function and constraint check
4.5 Simulation results in dynamic environment
4.5.1 Simulation in static and dynamic constraints environment
4.6 Chapter Summary
Chapter 5 Conclusions
5.1 Concluding Remarks
Acknowledgement
References
Published Papers
本文编号：3537396
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