Stochastic C-GNet Environment Modeling and Path Planning Optimization in a Narrow and Long Space

Abstract

This study proposes a novel method of optimal path planning in stochastic constraint network scenarios. We present a dynamic stochastic grid network model containing semienclosed narrow and long constraint information according to the unstructured environment of an underground or mine tunnel. This novel environment modeling (stochastic constraint grid network) computes the most likely global path in terms of a defined minimum traffic cost for a roadheader in such unstructured environments. Designing high-dimensional constraint vector and traffic cost in nodes and arcs based on two- and three-dimensional terrain elevation data in a grid network, this study considers the walking and space constraints of a roadheader to construct the network topology for the traffic cost value weights. The improved algorithm of variation self-adapting particle swarm optimization is proposed to optimize the regional path. The experimental results both in the simulation and in the actual test model settings illustrate the performance of the described approach, where a hybrid, centralized-distributed modeling method with path planning capabilities is used.

Seamless Integration Between Online and Face-to-Face

“…ThisflippedChinesecourseexemplifiesthedesignofalanguagecourseasacomplexsystem where foundational meaning, through online activities with scaffolding, and use, through FTF interactions,fittogether.WhenitisurgentlyneededtofindouthowonlineandFTFcanbeseamlessly integrated (Yang et al, 2018), our design and subsequent study are the first to demonstrate that seamlessintegrationofboththeonlineandFTFcomponentswhichiscriticalforstudents'language developmentanduse.Thestudentsengageinamoremeaningfulandsatisfactorylanguagelearning experiencewhentheseexperiencesaredesignedwiththeunderstandingthatlanguagelearningis acomplexsystemwithindividualandsocialspacesthatrequireeffectivescaffoldingandfeedback (Mercer&Howe,2012;Sarem&Shirzadi,2014).TheflippedChinesecoursemadelearningmore flexible, allowed students opportunities to practice speaking and writing through interactive and contextualizedtaskswithpeersandtheinstructor,allowedindividualattentionfromtheinstructor, andpromotedbetterunderstandinganduseofallfourlanguageskillsaswellasdeepeningcultural knowledge.EachonlinecomponentandeachFTFactivitynotonlyplayedacentralroleinenhancing learners'individuallearning,buttheseworkedtogethertoseamlesslytransitionstudentsintoadynamic andengagingcollaborativespacewheretheuseofthelanguageinvolvedmorecomplexskillsand activities.However,thereisstillaneedforfineadjustmentstomaketheinstructionalvideosshorter andmoreinteractive,thewritingpracticemoreengaging,andtheculturevideosmorerelevantto theFTFactivities.…”

Path Correction of the Boom Road‐Header in Coal Mining Based on State Estimation

“…From equation (18), it is obvious that if kk y � 1 is taken, then _ V � − k x x 2 e − kk θ sin 2 θ e ≤ 0 , where _ V � 0 only when x e � 0 and θ e � 0. erefore, P e is limited and expected to converge to zero as long as x e ⟶ 0 and θ e ⟶ 0 which are driven by the equation (13) and its simplified form, i.e., equation (16).…”

“…Wang and Zhang [12] used fuzzy control strategy based on the modeling of the hydraulic driving system of the road-header. Yang et al [13] proposed an optimal path planning in stochastic constraint network scenario for the road-header underground. However, slip compensation was barely discussed in both of the mentioned references.…”

Pose Detection and Automatic Deviation Correction Control Strategy of Crawler Walking Equipment in Coal Mines

“…The trajectory tracking controller is designed based on Lyapunov function, and the trajectory tracking problem of roadheader is solved by the backstepping method. J. Yang [26] modelled the random constraint environment of the correct path of the roadheader and classified the orientation correction problem of the roadheader as a general random network problem. The road random mesh network model is defined, and the genetic mutation particle swarm optimization algorithm is used to optimize the target path.…”