Mission design for a group of autonomous guided vehicles

Xidias, Elias K.; Azariadis, Philip

doi:10.1016/j.robot.2010.10.003

Cited by 48 publications

(38 citation statements)

References 17 publications

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“…This is because integrated scheduling and path planning form a very challenging NP optimization problem. 14 In recent years, Xidias and his coworkers 12,15,16 utilized the concept of Bump-surfaces 17 to solve this integration problem.…”

Section: Introductionmentioning

confidence: 99%

A novel scheduling method for automated guided vehicles in workshop environments

Wang

Zhang

2019

International Journal of Advanced Robotic Systems

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At present, the commonly used task scheduling methods of automated guided vehicle do not fully consider the influence of power consumption, the workshop environment, and other factors, resulting in the disparity between scheduling methods and practical applications. This article contributes to filling this gap by modifying the model and algorithm that can meet the real-time application in the factory. First, a scheduling model is established according to both the number of depots and the automated guided vehicle's battery consumption, so that the result of task allocation is more reasonable. Then, according to the area, distribution, shape characteristics of obstacles, and the number of depots contained in the environment, this article derives a new coefficient which is constructed as the weighted value of the distance between workstations to improve the robustness of the model. Finally, the modified genetic algorithm is used to obtain the scheduling results. The simulation results show the effectiveness and the rationality of the proposed method.

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Section: Introductionmentioning

confidence: 99%

A novel scheduling method for automated guided vehicles in workshop environments

Wang

Zhang

2019

International Journal of Advanced Robotic Systems

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“…The simulation results show the efficiency and the effectiveness of the proposed approach to determining a suboptimal tour for multi-goal motion planning in complex environments cluttered with obstacles. Xidias [23] proposed a generic approach for the integration of vehicle routing and scheduling, and motion planning for a group of autonomous guided vehicles, and their method realized the time-optimum and collision-free paths for all autonomous guided vehicles. These papers give us some inspiration for the autonomous task planning of small satellites.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Task Autonomous Planning Method for Small Satellites

et al. 2018

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Existing on-board planning systems do not apply to small satellites with limited onboard computer capacity and on-board resources. This study aims to investigate the problem of autonomous task planning for small satellites. Based on the analysis of the problem and its constraints, a model of task autonomous planning was implemented. According to the long-cycle task planning requirements, a framework of rolling planning was proposed, including a rolling window and planning unit in the solution, and we proposed an improved genetic algorithm (IGA) for rolling planning. This algorithm categorized each individual based on the compliance of individuals with a time partial order constraint and resource constraint, and designed an appropriate crossover operator and mutation operator for each type of individual. The experimental result showed that the framework and algorithm can not only respond quickly to observation tasks, but can produce effective planning programs to ensure the successful completion of observation tasks.

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“…Obtaining the optimal solutions for NP-hard problems is computationally challenging issue and difficult to solve in practice. Generally, proposed solutions for mission route planning approach can be categorized into three main groups: grid-based methods, graph based strategies, and artificial intelligence based techniques [5]. The grid-based strategies are inefficient in cases where the workspace is very large or complex because the large numbers of cells render such solutions intractable.…”

Section: Introductionmentioning

confidence: 99%

Optimal route planning with prioritized task scheduling for AUV missions

MahmoudZadeh

Powers

Sammut

et al. 2015

2015 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS)

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Abstract-This paper presents a solution to Autonomous Underwater Vehicles (AUVs) large scale route planning and task assignment joint problem. Given a set of constraints (e.g., time) and a set of task priority values, the goal is to find the optimal route for underwater mission that maximizes the sum of the priorities and minimizes the total risk percentage while meeting the given constraints. Making use of the heuristic nature of genetic and swarm intelligence algorithms in solving NP-hard graph problems, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are employed to find the optimum solution, where each individual in the population is a candidate solution (route). To evaluate the robustness of the proposed methods, the performance of the all PS and GA algorithms are examined and compared for a number of Monte Carlo runs. Simulation results suggest that the routes generated by both algorithms are feasible and reliable enough, and applicable for underwater motion planning. However, the GA-based route planner produces superior results comparing to the results obtained from the PSO based route planner.

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Mission design for a group of autonomous guided vehicles

Cited by 48 publications

References 17 publications

A novel scheduling method for automated guided vehicles in workshop environments

A novel scheduling method for automated guided vehicles in workshop environments

An Efficient Task Autonomous Planning Method for Small Satellites

Optimal route planning with prioritized task scheduling for AUV missions

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