Priority-based speed control strategy for automated guided vehicle path planning in automated container terminals

2020 ◽  
Vol 42 (16) ◽  
pp. 3079-3090 ◽  
Author(s):  
Meisu Zhong ◽  
Yongsheng Yang ◽  
Shu Sun ◽  
Yamin Zhou ◽  
Octavian Postolache ◽  
...  
Keyword(s):  
Path Planning ◽  
Control Strategy ◽  
Search Algorithm ◽  
Waiting Times ◽  
Optimal Path ◽  
Speed Control ◽  
Container Terminals ◽  
Automated Guided Vehicles ◽  
Operating Efficiency ◽  
Continuous Increase

With the continuous increase in labour costs and the demands of the supply chain, improving the efficiency of automated container terminals has been a key factor in the development of ports. Automated guided vehicles (AGVs) are the main means of horizontal transport in such terminals, and problems in relation to their use such as vehicle conflict, congestion and waiting times have become very serious, greatly reducing the operating efficiency of the terminals. In this article, we model the minimum driving distance of AGVs that transport containers between quay cranes (QCs) and yard cranes (YCs). AGVs are able to choose the optimal path from pre-planned paths by testing the overlap rate and the conflict time. To achieve conflict-free AGV path planning, a priority-based speed control strategy is used in conjunction with the Dijkstra depth-first search algorithm to solve the model. The simulation experiments show that this model can effectively reduce the probability of AGVs coming into conflict, reduce the time QCs and YCs have to wait for their next task and improve the operational efficiency of AGV horizontal transportation in automated container terminals.

Optimal Path Planning of Redundant Cooperative Robots Under Equality and Inequality Constraints

2003 ◽  
Author(s):  
Ali Hosseini ◽  
Mehdi Keshmiri
Keyword(s):  
Path Planning ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Minimum Principle ◽  
Optimal Path ◽  
Inequality Constraints ◽  
Optimal Path Planning ◽  
Nonlinear Inequality ◽  
Nonlinear Inequality Constraints ◽  
Cooperative Manipulators

Using kinematic resolution, the optimal path planning for two redundant cooperative manipulators carrying a solid object on a desired trajectory is studied. The optimization problem is first solved with no constraint. Consequently, the nonlinear inequality constraints, which model obstacles, are added to the problem. The formulation has been derived using Pontryagin Minimum Principle and results in a Two Point Boundary Value Problem (TPBVP). The problem is solved for a cooperative manipulator system consisting of two 3-DOF serial robots jointly carrying an object and the results are compared with those obtained from a search algorithm. Defining the obstacles in workspace as functions of joint space coordinates, the inequality constrained optimization problem is solved for the cooperative manipulators.

scholarly journals Sensor-network-based navigation of delivery robot for baggage handling in international airport

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142094473
Author(s):  
Kefei Shen ◽  
Chen Li ◽  
Difei Xu ◽  
Weihong Wu ◽  
He Wan
Keyword(s):  
Path Planning ◽  
Time Window ◽  
Optimal Path ◽  
Automated Guided Vehicles ◽  
Ultra Wideband ◽  
Maintenance Cost ◽  
Optimal Path Planning ◽  
International Airport ◽  
Baggage Handling ◽  
Planning Algorithm

Automated guided vehicles (AGVs) have been regarded as a promising means for the future delivery industry by many logistic companies. Several AGV-based delivery systems have been proposed, but they generally have drawbacks in delivering and locating baggage by magnet line, such as the high maintenance cost, and it is hard to change the trajectory of AGV. This article considers using multi-AGVs as delivery robots to coordinate and sort baggage in the large international airport. This system has the merit of enlarging the accuracy of baggage sorting and delivering. Due to the inaccurate transportation efficiency, a time-dependent stochastic baggage delivery system is proposed and a stochastic model is constructed to characterize the running priority and optimal path planning for multi-AGVs according to the flight information. In the proposed system, ultra-wideband technology is applied to realize precisely positioning and navigation for multi-AGVs in the baggage distribution center. Furthermore, the optimal path planning algorithm based on time-window rules and rapidly exploring random tree algorithm is considered to avoid collision and maneuverability constraints and to determine whether the running path for each AGV is feasible and optimal. Computer simulations are conducted to demonstrate the performance of the proposed method.

scholarly journals Research on Cooperative Scheduling of Automated Quayside Cranes and Automatic Guided Vehicles in Automated Container Terminal

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Qianru Zhao ◽  
Shouwen Ji ◽  
Dong Guo ◽  
Xuemin Du ◽  
Hongxuan Wang
Keyword(s):  
Search Algorithm ◽  
Container Terminal ◽  
Container Terminals ◽  
Automated Guided Vehicles ◽  
Total Energy Consumption ◽  
Two Stage ◽  
Taboo Search ◽  
Automatic Guided Vehicles ◽  
Collaborative Scheduling ◽  
Taboo Search Algorithm

According to previous research studies, automated quayside cranes (AQCs) and automated guided vehicles (AGVs) in automated container terminals have a high potential synergy. In this paper, a collaborative scheduling model for AQCs and AGVs is established and the capacity limitation of the transfer platform on AQCs is considered in the model. The minimum total energy consumption of automated quayside cranes (AQCs) and Automatic Guided Vehicles (AGVs) is taken as the objective function. A two-stage taboo search algorithm is adopted to solve the problem of collaborative scheduling optimization. This algorithm integrates AQC scheduling and AGV scheduling. The optimal solution to the model is obtained by feedback from the two-stage taboo search process. Finally, the Qingdao Port is taken as an example of a data experiment. Ten small size test cases are solved to evaluate the performance of the proposed optimization methods. The results show the applicability of the two-stage taboo search algorithm since it can find near-optimal solutions, precisely and accurately.

Navigational path planning for a vision-based mobile robot

Robotica ◽  
1989 ◽  
Vol 7 (1) ◽  
pp. 49-63 ◽  
Author(s):  
Ronald C. Arkin
Keyword(s):  
Path Planning ◽  
Free Space ◽  
Search Algorithm ◽  
Optimal Path ◽  
Space Representation ◽  
Transition Zones ◽  
Recursive Decomposition ◽  
Robot Architecture ◽  
Multi Level ◽  
Sensor Navigation

SUMMARYThe Autonomous Robot Architecture (AuRA) provides multi-level representation and planning capabilities. This paper addresses the task of navigational path-planning, which provides the robot with a path guaranteed to be free of collisions with any modeled obstacles. Knowledge supporting visual perception can also be embedded, facilitating the actual path traversal by the vehicle.A multi-level representation and architecture to support multi-sensor navigation (predominantly visual) are described. A hybrid vertex-graph free-space representation based upon the decomposition of free space into convex regions capable for use in both indoor and limited outdoor navigation is discussed. This “meadow map” is produced via the recursive decomposition of the initial bounding area of traversability and its associated modeled obstacles. Of particular interest is the ability to handle diverse terrain types (sidewalks, grass, gravel, etc.) “Transition zones” ease the passage of the robot from one terrain type to another.The navigational planner that utilizes the data available in the above representational scheme is described. An A* search algorithm incorporates appropriate cost functions for multi-terrain navigation. Consideration is given to just what constitutes an “optimal” path in this context.

scholarly journals Multi-objective automated guided vehicle scheduling based on MapReduce framework

2021 ◽  
Vol 16 (1) ◽  
pp. 37-46
Author(s):  
W. Shi ◽  
D.B. Tang ◽  
P. Zou
Keyword(s):  
Control Strategy ◽  
Material Handling ◽  
Speed Control ◽  
Automated Guided Vehicles ◽  
Material Handling System ◽  
Mapreduce Framework ◽  
Handling System ◽  
Multi Objective ◽  
Scheduling Model ◽  
Conflict Problem

During material handling processes, automated guided vehicles (AGVs) pose a path conflict problem. To solve this problem, we proposed a multi-objective scheduling model based on total driving distance and waiting time, and used the A* path planning algorithm to search the shortest path of AGV. By using a speed control strategy, we were able to detect the overlap path and the conflict time. Additionally, we adopted an efficient MapReduce framework to improve the speed control strategy execution efficiency. At last, a material handling system of smart electrical connectors workshop was discussed to verify the scheduling model and the speed control strategy combined with the MapReduce framework is feasible and effective to reduce the AGV path conflict probability. The material handling system could be applied in workshop to replace manual handling and to improve production efficiency.

scholarly journals Modeling a Driver’s Directional and Longitudinal Speed Control Based on Racing Track Features

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Jianfeng Wang ◽  
Weihua Li ◽  
Jun Li ◽  
Yiqun Liu ◽  
Baoyu Song ◽  
...  
Keyword(s):  
Control Strategy ◽  
Search Algorithm ◽  
Speed Control ◽  
Control Model ◽  
Operating Conditions ◽  
Driver Model ◽  
Operation Model ◽  
Driving System ◽  
Point Search ◽  
Feedback Control Strategy

This study firstly analyses the driver’s manipulation behaviour and relates the different components of the driver model. Then, a model controlling the driver directions is built according to the prediction-follower theory with the aim of improving the point search algorithm. A model of the driving system of an electric vehicle is used to establish the longitudinal speed control model of the driver by using a feedforward-PID feedback control strategy. Our approach is to release the coupling between direction and speed control and build an integrated model that includes the direction and speed for an arbitrary path. Finally, the characteristics of an actual racing track are considered to establish the fastest driver control model. We simulated the typical operating conditions of our driver operation model. The simulation confirmed the effectiveness of the improved predictive point search algorithm and the integrated driver model to control the direction and speed for an arbitrary path.

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