This paper investigates an improved genetic algorithm on multiple automated guided vehicle (multi-AGV) path planning. The innovations embody in two aspects. First, three-exchange crossover heuristic operators are used to produce more optimal offsprings for getting more information than with the traditional two-exchange crossover heuristic operators in the improved genetic algorithm. Second, dou...

We treat the problem of risk-aware control for stochastic shortest path (SSP) on Markov decision processes (MDP). Typically, expectation is considered SSP, which however oblivious to incurred risk. present an alternative view, instead optimizing conditional value-at-risk (CVaR), established risk measure. both chains as well MDP and introduce, through novel insights, two algorithms, based linear...

The shortest path (SP) problem concerns with finding the shortest path from a specific origin to a specified destination in a given network while minimizing the total cost associated with the path. This problem has widespread applications. Important applications of the SP problem include vehicle routing in transportation systems particularly in the field of in-vehicle Route Guidance System (RGS...

This paper focuses on the combined maintenance and routing optimisation problem in a multi-period environment that consists of scheduling operations for set geographically distributed machines, subject to non-deterministic failures with technicians perform preventive corrective operations. study uses two-step approach based column generation. The first step model determines optimal time until n...

This paper explores the problem of energy-efficient shortest path planning on off-road, natural, real-life terrain for unmanned ground vehicles (UGVs). We present a greedy algorithm based composite metric routing approach that combines energy consumption and distance path. In our work, we consider Terramechanics between UGV soil to account wheel sinkage effect, in addition slope deformation lim...

The aim of disseminating this research article is to showcase a novel path planner method, which is shown to be an efficient offline path planner in terms of its capacity for analyzing workspace robot maneuvering skills and constructing collision free trajectories that yield the shortest possible path from initial point toward goal configuration. The determined route is considered to be adequat...

In this paper, a hybrid method based on A* algorithm and genetic algorithm in grid map is proposed to solve the optimum path planning for mobile robots. In our scheme, A* algorithm is utilized for searching a constrained shortest path quickly, and the global optimal path is obtained by using genetic algorithm to optimize the path. The method makes the path node is not limited to the center of t...