本文選題：機(jī)器人 + 路徑規(guī)劃�。� 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著科學(xué)技術(shù)的進(jìn)步,機(jī)器人學(xué)得到了長(zhǎng)足的發(fā)展,機(jī)器人可以將人類從繁重的重復(fù)勞動(dòng)中解脫出來(lái),從工業(yè)領(lǐng)域到大眾生活,機(jī)器人發(fā)揮著越來(lái)越重要的作用。路徑規(guī)劃是機(jī)器人學(xué)的核心內(nèi)容之一,得到了眾多學(xué)者的深入研究,具有非常重要的意義,本文主要研究了一種基于神經(jīng)網(wǎng)絡(luò)和混合粒子群算法相結(jié)合的移動(dòng)機(jī)器人路徑規(guī)劃方法。在環(huán)境信息表示方面,研究了多層前向網(wǎng)絡(luò)、hopfield神經(jīng)網(wǎng)絡(luò)、ART神經(jīng)網(wǎng)絡(luò)等在機(jī)器人路徑規(guī)劃方面的應(yīng)用,由于多層前向網(wǎng)絡(luò)在表示障礙物信息時(shí)計(jì)算簡(jiǎn)單,易于并行,并且無(wú)需訓(xùn)練權(quán)值,結(jié)合未知環(huán)境下路徑規(guī)劃的特點(diǎn),最后確定用多層前向網(wǎng)絡(luò)表示環(huán)境信息。課題使用了一種混合粒子群算法DHPSO進(jìn)行子路徑規(guī)劃。針對(duì)慣性權(quán)重隨迭代次數(shù)遞減的標(biāo)準(zhǔn)粒子群算法(SPSO)全局收斂性強(qiáng)但是收斂速度慢和壓縮因子粒子群算法(PSOCF)全局收斂性弱但是收斂速度很快的特點(diǎn),提出了一種雙種群混合交叉粒子群算法DHPSO,種群一和種群二分別使用SPSO和PSOCF的方法進(jìn)行迭代,每隔一定的迭代次數(shù),種群一將自身的較優(yōu)粒子交換給種群二。DHPSO結(jié)合了SPSO和PSOCF的優(yōu)點(diǎn),不僅全局收斂性較強(qiáng),同時(shí)具有很快的收斂速度。最后,在MATLABR2016a實(shí)驗(yàn)平臺(tái)上對(duì)粒子群算法和路徑規(guī)劃進(jìn)行了仿真實(shí)驗(yàn),在單模態(tài)函數(shù)和多模態(tài)函數(shù)下的仿真實(shí)驗(yàn)證明了DHPSO算法的優(yōu)越性。在路徑規(guī)劃的實(shí)驗(yàn)仿真方面,進(jìn)行了多種環(huán)境下的路徑規(guī)劃,包括簡(jiǎn)單和復(fù)雜靜態(tài)環(huán)境下的路徑規(guī)劃、環(huán)境中存在動(dòng)態(tài)障礙物情況下的路徑規(guī)劃以及二維編碼情況下的路徑規(guī)劃。同時(shí)進(jìn)行了路徑規(guī)劃方面的幾點(diǎn)思考,包括評(píng)價(jià)函數(shù)的選擇、坐標(biāo)轉(zhuǎn)換以及粒子群算法在路徑規(guī)劃方面的一些需要留意的地方等。路徑規(guī)劃的仿真實(shí)驗(yàn)結(jié)果說(shuō)明了總的路徑規(guī)劃的有效性,具有一定的實(shí)用價(jià)值。
[Abstract]:With the progress of science and technology, robotics has made great progress. Robot can extricate mankind from the heavy repeated work, from the industrial field to public life, robot plays an increasingly important role. Path planning is one of the core contents of robotics and has been deeply studied by many scholars. In this paper, a path planning method for mobile robot based on neural network and hybrid particle swarm optimization is studied. In the field of environmental information representation, the application of multi-layer forward network (ART) neural network in robot path planning is studied. Because the multi-layer forward network is simple to compute when representing obstacle information, it is easy to parallel. And without training weights, combined with the characteristics of path planning in unknown environment, the multi-layer forward network is used to represent the environment information. In this paper, a hybrid particle swarm optimization (DHPSO) algorithm is used for subpath planning. For the standard particle swarm optimization (SPSO) algorithm with decreasing inertial weight with the iterative times, the global convergence is strong but the convergence rate is slow, and the compression factor particle swarm optimization algorithm has the characteristics of weak global convergence and fast convergence rate. In this paper, a two-population hybrid crossover particle swarm optimization (DHPSO) algorithm is proposed. Population one and population two are iterated by SPSO and PSOCF, respectively, every certain number of iterations. Population one exchanges its own better particles with population II. DHPSO combines the advantages of SPSO and PSOCF. It not only has strong global convergence, but also has a fast convergence rate. Finally, the particle swarm optimization (PSO) algorithm and path planning are simulated on the MATLABR2016a platform, and the superiority of the DHPSO algorithm is proved by the simulation experiments under the single-mode function and the multi-mode function. In the aspect of experimental simulation of path planning, path planning is carried out in many environments, including simple and complex static environment. Path planning in the presence of dynamic obstacles and in two-dimensional coding. At the same time, some thoughts on path planning are given, including the selection of evaluation function, coordinate transformation and some points needing attention in the aspect of path planning based on particle swarm optimization. The simulation results of path planning show that the overall path planning is effective and has some practical value.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP242;TP183

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