scholarly journals Two-Layer Path Planning for Multi-Area Coverage by a Cooperated Ground Vehicle and Drone System

2020 ◽  
Author(s):  
Yangsheng Xia ◽  
Chao Chen ◽  
Jianmai Shi ◽  
Yao Liu ◽  
Guohui Li
Keyword(s):  
Path Planning ◽  
Simulation Experiment ◽  
Feasible Solution ◽  
Programming Model ◽  
Neighborhood Search ◽  
Large Neighborhood Search ◽  
Ground Vehicle ◽  
Random Instances ◽  
Scanning Path

A novel two-layer path planning method for a cooperated ground vehicle (GV) and drone system is investigated, where the GV acts as the mobile platform of the drone and is used to conduct multiple area covering tasks collaboratively. The GV takes the drone to visit a set of discrete areas, while the drone takes off from the GV at potential nodes around each area and scans each area for collecting information. The drone can be recharged in the GV during the time when it travels between different areas. The objective is to optimize the drone’s scanning path for all areas’ coverage and the GV’s travel path for visiting all areas. A 0-1 integer programming model is developed to formulate the problem. A two-stage heuristic based on cost saving strategy is designed to quickly construct a feasible solution, then the Adaptive Large Neighborhood Search (ALNS) algorithm is employed to improve the quality of the solution. A simulation experiment based on the parks in Changsha, China, is presented to illustrate the application of the method. Random instances are designed to further test the performance of the proposed algorithm.

scholarly journals Two-Layer Path Planning for Multi-Area Coverage by a Cooperated Ground Vehicle and Drone System

2020 ◽  
Author(s):  
Yangsheng Xia ◽  
Chao Chen ◽  
Jianmai Shi ◽  
Yao Liu ◽  
Guohui Li
Keyword(s):  
Path Planning ◽  
Simulation Experiment ◽  
Feasible Solution ◽  
Programming Model ◽  
Neighborhood Search ◽  
Large Neighborhood Search ◽  
Ground Vehicle ◽  
Random Instances ◽  
Scanning Path

A novel two-layer path planning method for a cooperated ground vehicle (GV) and drone system is investigated, where the GV acts as the mobile platform of the drone and is used to conduct multiple area covering tasks collaboratively. The GV takes the drone to visit a set of discrete areas, while the drone takes off from the GV at potential nodes around each area and scans each area for collecting information. The drone can be recharged in the GV during the time when it travels between different areas. The objective is to optimize the drone’s scanning path for all areas’ coverage and the GV’s travel path for visiting all areas. A 0-1 integer programming model is developed to formulate the problem. A two-stage heuristic based on cost saving strategy is designed to quickly construct a feasible solution, then the Adaptive Large Neighborhood Search (ALNS) algorithm is employed to improve the quality of the solution. A simulation experiment based on the parks in Changsha, China, is presented to illustrate the application of the method. Random instances are designed to further test the performance of the proposed algorithm.

scholarly journals An adaptive large neighborhood search heuristic for the planar storage location assignment problem: application to stowage planning for Roll-on Roll-off ships

2020 ◽  
Vol 26 (6) ◽  
pp. 885-912
Author(s):  
Jone R. Hansen ◽  
Kjetil Fagerholt ◽  
Magnus Stålhane ◽  
Jørgen G. Rakke
Keyword(s):  
Programming Model ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Large Neighborhood Search ◽  
Network Graph ◽  
Adaptive Large Neighborhood Search ◽  
Large Neighborhood ◽  
Storage Location ◽  
Stowage Planning ◽  
Stowage Plan

Abstract This paper considers a generalized version of the planar storage location problem arising in the stowage planning for Roll-on/Roll-off ships. A ship is set to sail along a predefined voyage where given cargoes are to be transported between different port pairs along the voyage. We aim at determining the optimal stowage plan for the vehicles stored on a deck of the ship so that the time spent moving vehicles to enable loading or unloading of other vehicles (shifting), is minimized. We propose a novel mixed integer programming model for the problem, considering both the stowage and shifting aspect of the problem. An adaptive large neighborhood search (ALNS) heuristic with several new destroy and repair operators is developed. We further show how the shifting cost can be effectively evaluated using Dijkstra’s algorithm by transforming the stowage plan into a network graph. The computational results show that the ALNS heuristic provides high quality solutions to realistic test instances.

scholarly journals Optimizing Lock Operations and Ship Arrivals through Multiple Locks on Inland Waterways

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Hongxu Guan ◽  
Yanmin Xu ◽  
Longhao Li ◽  
Xin Huang
Keyword(s):  
Optimization Problem ◽  
Programming Model ◽  
Waiting Times ◽  
Optimization Method ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Large Neighborhood Search ◽  
Inland Waterways ◽  
Information Availability ◽  
Inland Waterway

Locks are important components of a waterway system. To improve the efficiency of inland waterway transport, it is important to ensure ships passing locks without having to spend unnecessary waiting times at lock entrances. Meanwhile, with the trends towards digitalized and smart waterways, it is also worth investigating how the information availability could contribute to optimizing lock operations and ship arrivals on inland waterways. Therefore, this paper proposes an optimization method to schedule ships’ arrivals and their placements in locks on inland waterways, based on a mixed-integer programming model, and solves the optimization problem with large neighborhood search based heuristics. The optimization objective is threefold: first, optimizing the arrival sequence of ships at the locks; second, maximize the utilization of each lockage operation; and third, reducing the overall time that each ship spends from entering the waterway area till leaving the last lock on the waterway. Simulations are carried out to evaluate the performance of the proposed method.

scholarly journals Multidepot UAV Routing Problem with Weapon Configuration and Time Window

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Tianren Zhou ◽  
Jiaming Zhang ◽  
Jianmai Shi ◽  
Zhong Liu ◽  
Jincai Huang
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Mixed Integer Linear Programming ◽  
Time Window ◽  
Programming Model ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Large Neighborhood Search ◽  
Routing Problem ◽  
Routing Strategy ◽  
Increasing Trend

In recent wars, there is an increasing trend that unmanned aerial vehicles (UAVs) are utilized to conduct military attacking missions. In this paper, we investigate a novel multidepot UAV routing problem with consideration of weapon configuration in the UAV and the attacking time window of the target. A mixed-integer linear programming model is developed to jointly optimize three kinds of decisions: the weapon configuration strategy in the UAV, the routing strategy of target, and the allocation strategy of weapons to targets. An adaptive large neighborhood search (ALNS) algorithm is proposed for solving the problem, which is tested by randomly generated instances covering the small, medium, and large sizes. Experimental results confirm the effectiveness and robustness of the proposed ALNS algorithm.

scholarly journals A path planning method for a surface inspection system based on two-dimensional laser profile scanner

2019 ◽  
Vol 16 (4) ◽  
pp. 172988141986246 ◽  
Author(s):  
Lina Li ◽  
De Xu ◽  
Linkai Niu ◽  
Yuan Lan ◽  
Xiaoyan Xiong
Keyword(s):  
Path Planning ◽  
Three Dimensional ◽  
Neighborhood Search ◽  
Inspection System ◽  
Two Dimensional ◽  
Surface Data ◽  
Scanning Path ◽  
Scanning Path Planning ◽  
Robot Position ◽  
Orientation Determination

In this article, a method for two-dimensional scanning path planning based on robot is proposed. In this method, a section division algorithm based on neighborhood search method for scanning orientation determination is firstly produced. The scanning paths which meet constraints of the system are then generated. Finally, the experiment is carried out on robot-based scanning platform. The two-dimensional data from scanner and the robot position are combined to form three-dimensional surface data of measured workpiece. The experiment results verify the effectiveness of proposed method.

Optimum Path Planning for Mechanical Manipulators

1981 ◽  
Vol 103 (2) ◽  
pp. 142-151 ◽  
Author(s):  
J. Y. S. Luh ◽  
C. S. Lin
Keyword(s):  
Path Planning ◽  
Feasible Solution ◽  
Computing Time ◽  
Inequality Constraints ◽  
Programming Algorithm ◽  
Planning Problem ◽  
Linear Inequality Constraints ◽  
Nonlinear Inequality ◽  
Angular Velocities ◽  
Nonlinear Inequality Constraints

To assure a successful completion of an assigned task without interruption, such as the collision with fixtures, the hand of a mechanical manipulator often travels along a preplanned path. An advantage of requiring the path to be composed of straight-line segments in Cartesian coordinates is to provide a capability for controlled interaction with objects on a moving conveyor. This paper presents a method of obtaining a time schedule of velocities and accelerations along the path that the manipulator may adopt to obtain a minimum traveling time, under the constraints of composite Cartesian limit on linear and angular velocities and accelerations. Because of the involvement of a linear performance index and a large number of nonlinear inequality constraints, which are generated from physical limitations, the “method of approximate programming (MAP)” is applied. Depending on the initial choice of a feasible solution, the iterated feasible solution, however, does not converge to the optimum feasible point, but is often entrapped at some other point of the boundary of the constraint set. To overcome the obstacle, MAP is modified so that the feasible solution of each of the iterated linear programming problems is shifted to the boundaries corresponding to the original, linear inequality constraints. To reduce the computing time, a “direct approximate programming algorithm (DAPA)” is developed, implemented and shown to converge to optimum feasible solution for the path planning problem. Programs in FORTRAN language have been written for both the modified MAP and DAPA, and are illustrated by a numerical example for the purpose of comparison.

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