自平衡機器人的移動穩(wěn)定性與路徑優(yōu)化研究
發(fā)布時間：2019-06-11 09:59
【摘要】：近年來,移動機器人技術(shù)水平有著飛躍式的發(fā)展,不僅在生產(chǎn)生活中被廣泛應用,而且對許多國家工業(yè)、國防以及國民經(jīng)濟產(chǎn)生了巨大的影響。它在實際應用中可代替人工在危險(有毒、輻射等)、復雜環(huán)境中作業(yè),可從事在人所不及(太空、深海等)的空間環(huán)境中。其中兩輪自平衡機器人是移動機器人中應用較為廣泛的一種,它是集環(huán)境感知、動態(tài)決策與規(guī)劃等多功能于一體的智能化機器系統(tǒng),它融合了多級倒立擺的不穩(wěn)定性、多變量、非線性和強耦合等特點。由于兩輪機器人可以實現(xiàn)多輪機器人無法實現(xiàn)的復雜動作,因此對它們的深入研究具有重要的理論及現(xiàn)實意義。本文針對自平衡機器人在實際應用中存在穩(wěn)定性不佳、人工經(jīng)驗選擇控制器權(quán)值結(jié)果較差等問題,展開了系統(tǒng)研究,在基于線性二次性最優(yōu)控制器和自平衡機器人模型的基礎上,提出了一個基于蟻群算法優(yōu)化機器人穩(wěn)定性的模型。之后,在保證了機器人安全性、平穩(wěn)性的基礎上,針對機器人路徑規(guī)劃中前期環(huán)境地圖信息采用劃分方式不理想和后期單一算法優(yōu)化路徑不佳等問題,提出一種改進蜂巢柵格法來優(yōu)化前期環(huán)境信息并采用一種基于遺傳-蟻群的混合算法來解決后期最優(yōu)路徑的問題,使系統(tǒng)平穩(wěn)且無碰撞的完成路徑尋優(yōu)。本文研究工作主要包括:(1)本文提出一個基于蟻群算法優(yōu)化機器人穩(wěn)定性的模型,用以選擇適合控制器的參數(shù)權(quán)值,克服了經(jīng)驗選擇參數(shù)的不確定性和耗時性。此模型對最優(yōu)控制器的性能指標函數(shù)對于狀態(tài)量的權(quán)陣參數(shù)進行優(yōu)化,通過最優(yōu)的權(quán)陣結(jié)果得到控制器解值,最終決定移動機器人的穩(wěn)定指標結(jié)果。該優(yōu)化方式更優(yōu)于傳統(tǒng)人工選擇參數(shù)的方法,不僅優(yōu)化了機器人的穩(wěn)定性,還降低了實踐中相關(guān)系統(tǒng)應用的危險系數(shù)。(2)本文提出一種改進蜂巢柵格法來處理路徑優(yōu)化前期環(huán)境信息,它結(jié)合了自然生物現(xiàn)象和幾何學理論,克服了傳統(tǒng)柵格法在處理地圖信息時占用過多可行空間、增加機器人碰撞率的缺點,并且改善了蜂巢柵格法編碼時不連續(xù)、不能根據(jù)起始點和終點的位置控制編碼規(guī)則的問題。該方法采用自適應編碼方式,靈活操作地圖信息的設定,使柵格具有連續(xù)性,更利于最優(yōu)路徑的搜索,有效的減少機器人行進的路徑長度和路徑搜索時間,提高機器人的安全性。(3)本文在處理路徑規(guī)劃后期搜索最優(yōu)路徑的問題中,選定蟻群和遺傳算法作為混合方法的研究基礎,通過實驗對比分析,結(jié)合二者的優(yōu)缺點,采用了一種基于遺傳-蟻群的混合算法對路徑進行尋優(yōu),克服了傳統(tǒng)路徑規(guī)劃采用單一優(yōu)化算法搜索路徑時穩(wěn)態(tài)迭代次數(shù)和穩(wěn)態(tài)時路徑長度不理想的問題,并通過設置30次、80次和150次的迭代次數(shù),可以得到多混合算法均優(yōu)于單一算法的處理結(jié)果。
[Abstract]:In recent years, the level of mobile robot technology has been developed by leaps and bounds, which is not only widely used in production and life, but also has a great impact on industry, national defense and national economy in many countries. It can be used instead of artificial in dangerous (toxic, radiation, etc.), complex environment, can be engaged in the space environment (space, deep sea, etc.). Two-wheeled self-balancing robot is widely used in mobile robots. It is an intelligent machine system which integrates environmental perception, dynamic decision-making and planning. It combines the instability and multivariable of multi-stage inverted pendulum. Nonlinear and strong coupling and other characteristics. Because the two-wheeled robot can realize the complex action that the multi-wheeled robot can not realize, it is of great theoretical and practical significance to study them deeply. In this paper, a systematic study is carried out to solve the problems of poor stability and poor weight value of artificial experience selection controller in the practical application of self-balancing robot. Based on the linear quadratic optimal controller and the self-balancing robot model, a model based on ant colony algorithm is proposed to optimize the stability of the robot. After that, on the basis of ensuring the safety and stability of the robot, the environmental map information in the early stage of robot path planning is not well divided and the path optimization by a single algorithm in the later stage is not good. An improved beehive grid method is proposed to optimize the environmental information in the early stage and a hybrid algorithm based on genetic ant colony is used to solve the problem of optimal path in the later stage, which makes the system stable and collision-free. The main research work of this paper is as follows: (1) in this paper, a model based on ant colony algorithm to optimize the stability of robot is proposed, which can select the parameter weights suitable for the controller and overcome the uncertainty and consumption of empirical selection parameters. In this model, the performance index function of the optimal controller is optimized for the weight matrix parameters of the state quantity, and the solution value of the controller is obtained by the optimal weight matrix result, and finally the stability index result of the mobile robot is determined. This optimization method is better than the traditional manual parameter selection method, and not only optimizes the stability of the robot. It also reduces the risk coefficient of the application of related systems in practice. (2) in this paper, an improved hive grid method is proposed to deal with the environmental information in the early stage of path optimization, which combines natural biological phenomena and geometry theory. The shortcomings of the traditional grid method in dealing with map information are overcome, such as taking up too much feasible space and increasing the collision rate of the robot, and the problem of discontinuity in the coding of the hive grid method is improved, and the coding rules can not be controlled according to the position of the starting point and the end point. In this method, the adaptive coding method is adopted to flexibly operate the setting of map information, so that the grid has continuity, which is more conducive to the search of the optimal path, and effectively reduces the path length and path search time of the robot. Improve the safety of the robot. (3) in dealing with the problem of searching the optimal path in the later stage of path planning, ant colony and genetic algorithm are selected as the research basis of the hybrid method, and the advantages and disadvantages of the two methods are compared and analyzed by experiments. A hybrid algorithm based on genetic ant colony is used to optimize the path, which overcomes the problem that the number of steady-state iterations and the path length in steady state are not ideal when the traditional path planning uses a single optimization algorithm to search the path, and by setting 30 times, With 80 iterations and 150 iterations, the processing results of multi-hybrid algorithm are better than those of single algorithm.
【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP242
本文編號：2497101
[Abstract]:In recent years, the level of mobile robot technology has been developed by leaps and bounds, which is not only widely used in production and life, but also has a great impact on industry, national defense and national economy in many countries. It can be used instead of artificial in dangerous (toxic, radiation, etc.), complex environment, can be engaged in the space environment (space, deep sea, etc.). Two-wheeled self-balancing robot is widely used in mobile robots. It is an intelligent machine system which integrates environmental perception, dynamic decision-making and planning. It combines the instability and multivariable of multi-stage inverted pendulum. Nonlinear and strong coupling and other characteristics. Because the two-wheeled robot can realize the complex action that the multi-wheeled robot can not realize, it is of great theoretical and practical significance to study them deeply. In this paper, a systematic study is carried out to solve the problems of poor stability and poor weight value of artificial experience selection controller in the practical application of self-balancing robot. Based on the linear quadratic optimal controller and the self-balancing robot model, a model based on ant colony algorithm is proposed to optimize the stability of the robot. After that, on the basis of ensuring the safety and stability of the robot, the environmental map information in the early stage of robot path planning is not well divided and the path optimization by a single algorithm in the later stage is not good. An improved beehive grid method is proposed to optimize the environmental information in the early stage and a hybrid algorithm based on genetic ant colony is used to solve the problem of optimal path in the later stage, which makes the system stable and collision-free. The main research work of this paper is as follows: (1) in this paper, a model based on ant colony algorithm to optimize the stability of robot is proposed, which can select the parameter weights suitable for the controller and overcome the uncertainty and consumption of empirical selection parameters. In this model, the performance index function of the optimal controller is optimized for the weight matrix parameters of the state quantity, and the solution value of the controller is obtained by the optimal weight matrix result, and finally the stability index result of the mobile robot is determined. This optimization method is better than the traditional manual parameter selection method, and not only optimizes the stability of the robot. It also reduces the risk coefficient of the application of related systems in practice. (2) in this paper, an improved hive grid method is proposed to deal with the environmental information in the early stage of path optimization, which combines natural biological phenomena and geometry theory. The shortcomings of the traditional grid method in dealing with map information are overcome, such as taking up too much feasible space and increasing the collision rate of the robot, and the problem of discontinuity in the coding of the hive grid method is improved, and the coding rules can not be controlled according to the position of the starting point and the end point. In this method, the adaptive coding method is adopted to flexibly operate the setting of map information, so that the grid has continuity, which is more conducive to the search of the optimal path, and effectively reduces the path length and path search time of the robot. Improve the safety of the robot. (3) in dealing with the problem of searching the optimal path in the later stage of path planning, ant colony and genetic algorithm are selected as the research basis of the hybrid method, and the advantages and disadvantages of the two methods are compared and analyzed by experiments. A hybrid algorithm based on genetic ant colony is used to optimize the path, which overcomes the problem that the number of steady-state iterations and the path length in steady state are not ideal when the traditional path planning uses a single optimization algorithm to search the path, and by setting 30 times, With 80 iterations and 150 iterations, the processing results of multi-hybrid algorithm are better than those of single algorithm.
【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP242
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