在當(dāng)今時(shí)代,無(wú)人駕駛地面車輛（UGV）在不同領(lǐng)域的應(yīng)用逐漸增加,這些領(lǐng)域包括災(zāi)害管理,工業(yè),軍事和應(yīng)用在日常生活中幫助人們提高他們安全性和舒適性的領(lǐng)域。在這種情況下,UGV需要進(jìn)一步提高其智能決策能力,以便在靜態(tài)和動(dòng)態(tài)約束環(huán)境中順利完成所需任務(wù)。其中,路徑規(guī)劃被認(rèn)為是UGV運(yùn)動(dòng)規(guī)劃中的一項(xiàng)具有挑戰(zhàn)性的任務(wù)�；诃h(huán)境復(fù)雜性,路徑規(guī)劃被分為全局路徑規(guī)劃（GPP）和本地路徑規(guī)劃（LPP）的兩種方法。GPP方法在靜態(tài)約束環(huán)境中生成從起始位置到目的地的合適路徑,在此過(guò)程中可能出現(xiàn)許多意外的約束。LPP方法則是為了車輛能夠避免這些障礙并能繼續(xù)在參考全局軌跡上移動(dòng)。本文提供了一種集合算法,該算法通過(guò)生成具有局部規(guī)劃能力的平滑全局路徑,避免了不同復(fù)雜度地圖中的意外障礙,提高了UGV的安全性,路徑一致性和,改善了軌跡規(guī)劃的能力。1.蟻群優(yōu)化（ACO）是一種進(jìn)化方法。由于強(qiáng)大的魯棒性,良好的反饋信息和更好的分布式計(jì)算等特性,它已成為一種著名的方法,它可以很容易地與不同的技術(shù)相結(jié)合,以提供所需的最佳解決方案。本文利用A*算法的評(píng)估函數(shù)進(jìn)一步改進(jìn)了ACO,利用更好的啟發(fā)式信息,加快了蟻群算法搜索策略的收斂速度... 

【文章來(lái)源】：電子科技大學(xué)四川省 211工程院校 985工程院校 教育部直屬院校

【文章頁(yè)數(shù)】：66 頁(yè)

【學(xué)位級(jí)別】：碩士

【文章目錄】：
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



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