scholarly journals Combining a Probabilistic Sampling Technique and Simple Heuristics to Solve the Dynamic Path Planning Problem

2009 ◽  
Author(s):  
Nicolas A. Barriga ◽  
Mauricio Solar ◽  
Mauricio Araya-López
Keyword(s):  
Path Planning ◽  
Sampling Technique ◽  
Planning Problem ◽  
Probabilistic Sampling ◽  
Dynamic Path Planning ◽  
Dynamic Path ◽  
Path Planning Problem ◽  
Simple Heuristics

scholarly journals Fleet management system for mobile robots in healthcare environments

2021 ◽  
Vol 14 (1) ◽  
pp. 55
Author(s):  
Eduardo Guzmán Ortiz ◽  
Beatriz Andres ◽  
Francisco Fraile ◽  
Raul Poler ◽  
Ángel Ortiz Bas
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Mobile Robots ◽  
Hospital Environment ◽  
Fleet Management ◽  
Planning Problem ◽  
Dynamic Path Planning ◽  
Healthcare Environments ◽  
Dynamic Path ◽  
Path Planning Problem

Purpose: The purpose of this paper is to describe the implementation of a Fleet Management System (FMS) that plans and controls the execution of logistics tasks by a set of mobile robots in a real-world hospital environment. The FMS is developed upon an architecture that hosts a routing engine, a task scheduler, an Endorse Broker, a controller and a backend Application Programming Interface (API). The routing engine handles the geo-referenced data and the calculation of routes; the task scheduler implements algorithms to solve the task allocation problem and the trolley loading problem using Integer Linear Programming (ILP) model and a Genetic Algorithm (GA) depending on the problem size. The Endorse Broker provides a messaging system to exchange information with the robotic fleet, while the controller implements the control rules to ensure the execution of the work plan. Finally, the Backend API exposes some FMS to external systems.Design/methodology/approach: The first part of the paper, focuses on the dynamic path planning problem of a set of mobile robots in indoor spaces such as hospitals, laboratories and shopping centres. A review of algorithms developed in the literature, to address dynamic path planning, is carried out; and an analysis of the applications of such algorithms in mobile robots that operate in real in-door spaces is performed. The second part of the paper focuses on the description of the FMS, which consists of five integrated tools to support the multi-robot dynamic path planning and the fleet management.Findings: The literature review, carried out in the context of path planning problem of multiple mobile robots in in-door spaces, has posed great challenges due to the environment characteristics in which robots move. The developed FMS for mobile robots in healthcare environments has resulted on a tool that enables to: (i) interpret of geo-referenced data; (ii) calculate and recalculate dynamic path plans and task execution plans, through the implementation of advanced algorithms that take into account dynamic events; (iii) track the tasks execution; (iv) fleet traffic control; and (v)  to communicate with one another external systems.Practical implications: The proposed FMS has been developed under the scope of ENDORSE project that seeks to develop safe, efficient, and integrated indoor robotic fleets for logistic applications in healthcare and commercial spaces. Moreover, a computational analysis is performed using a virtual hospital floor-plant.Originality/value: This work proposes a novel FMS, which consists of integrated tools to support the mobile multi-robot dynamic path planning in a real-world hospital environment. These tools include: a routing engine that handles the geo-referenced data and the calculation of routes. A task scheduler that includes a mathematical model to solve the path planning problem, when a low number of robots is considered. In order to solve large size problems, a genetic algorithm is also implemented to compute the dynamic path planning with less computational effort. An Endorse broker to exchanges information between the robotic fleet and the FMS in a secure way. A backend API that provides interface to manage the master data of the FMS, to calculate an optimal assignment of a set of tasks to a group of robots to be executed on a specific date and time, and to add a new task to be executed in the current shift. Finally, a controller to ensures that the robots execute the tasks that have been assigned by the task scheduler.

scholarly journals Global Dynamic Path Planning Fusion Algorithm Combining Jump-A* Algorithm and Dynamic Window Approach

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 19632-19638
Author(s):  
Lisang Liu ◽  
Jinxin Yao ◽  
Dongwei He ◽  
Jian Chen ◽  
Jing Huang ◽  
...  
Keyword(s):  
Path Planning ◽  
A Algorithm ◽  
Fusion Algorithm ◽  
Global Dynamic ◽  
Dynamic Path Planning ◽  
Dynamic Path ◽  
Window Approach

Dynamic Path Planning Algorithm Based on Chaos Genetic Vehicle Navigation

2011 ◽  
Vol 142 ◽  
pp. 12-15
Author(s):  
Ping Feng
Keyword(s):  
Path Planning ◽  
Search Algorithm ◽  
Traffic Condition ◽  
Planning Time ◽  
Dynamic Path Planning ◽  
Short Period ◽  
Planning Algorithm ◽  
Dynamic Path ◽  
Path Planning Algorithm ◽  
Chaos Genetic Algorithm

The paper puts forward the dynamic path planning algorithm based on improving chaos genetic algorithm by using genetic algorithms and chaos search algorithm. In the practice of navigation, the algorithm can compute at the best path to meet the needs of the navigation in such a short period of planning time. Furthermore,this algorithm can replan a optimum path of the rest paths after the traffic condition in the sudden.

Research on pull-type multi-AGV system dynamic path optimization based on time window

2021 ◽  
pp. 095440702098271
Author(s):  
Hongying Shan ◽  
Chuang Wang ◽  
Cungang Zou ◽  
Mengyao Qin
Keyword(s):  
Path Planning ◽  
Time Window ◽  
Planning Problem ◽  
Dijkstra Algorithm ◽  
Before And After ◽  
Common Planning ◽  
Planning Algorithm ◽  
Dynamic Path ◽  
Set Up ◽  
Path Planning Algorithm

This paper is a study of the dynamic path planning problem of the pull-type multiple Automated Guided Vehicle (multi-AGV) complex system. First, based on research status at home and abroad, the conflict types, common planning algorithms, and task scheduling methods of different AGV complex systems are compared and analyzed. After comparing the different algorithms, the Dijkstra algorithm was selected as the path planning algorithm. Secondly, a mathematical model is set up for the shortest path of the total driving path, and a general algorithm for multi-AGV collision-free path planning based on a time window is proposed. After a thorough study of the shortcomings of traditional single-car planning and conflict resolution algorithms, a time window improvement algorithm for the planning path and the solution of the path conflict covariance is established. Experiments on VC++ software showed that the improved algorithm reduces the time of path planning and improves the punctual delivery rate of tasks. Finally, the algorithm is applied to material distribution in the OSIS workshop of a C enterprise company. It can be determined that the method is feasible in the actual production and has a certain application value by the improvement of the data before and after the comparison.

Sign in / Sign up

Export Citation Format

Share Document