scholarly journals SmartPATH: An Efficient Hybrid ACO-GA Algorithm for Solving the Global Path Planning Problem of Mobile Robots

10.5772/58543 ◽  
2014 ◽  
Vol 11 (7) ◽  
pp. 94 ◽  
Author(s):  
Imen Châari ◽  
Anis Koubâa ◽  
Sahar Trigui ◽  
Hachemi Bennaceur ◽  
Adel Ammar ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Planning Problem ◽  
Global Path Planning ◽  
Ga Algorithm ◽  
Path Planning Problem

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.

A novel path planning method of mobile robots based on an improved bat algorithm

2020 ◽  
pp. 095440622096314
Author(s):  
Zhongbin Wang ◽  
Ziqing Wu ◽  
Lei Si ◽  
Kuangwei Tong ◽  
Chao Tan
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Fitness Function ◽  
Bat Algorithm ◽  
Cauchy Distribution ◽  
Planning Problem ◽  
Lévy Flight ◽  
Levy Flight ◽  
Global Path Planning ◽  
Search Stage

In order to solve the global path planning problem of mobile robots, an improved bat algorithm based on inertial weight and Levy flight is proposed in this paper. The linear inertial weights are used to prevent the algorithm from converging prematurely and the Levy flight is introduced in the global search stage to change the flight direction of the bat individuals. Furthermore, in the local search stage, the random exploration mechanism in Cauchy Distribution is utilized to enhance the local mining ability of the algorithm and search for the local optimal values. Then, some simulations are provided to verify the superiority of the improved bat algorithm to other optimization algorithms. Finally, the improved bat algorithm is applied in the global path planning, and the environment model and fitness function construction are reasonably established. The results indicate the feasibility and effectiveness of proposed algorithm in solving path planning problems.

scholarly journals Path-Planning for Mobile Robots Using a Novel Variable-Length Differential Evolution Variant

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 357
Author(s):  
Alejandro Rodríguez-Molina ◽  
José Solís-Romero ◽  
Miguel Gabriel Villarreal-Cervantes ◽  
Omar Serrano-Pérez ◽  
Geovanni Flores-Caballero
Keyword(s):  
Path Planning ◽  
Differential Evolution ◽  
Mobile Robots ◽  
Vector Optimization ◽  
Vector Optimization Problem ◽  
Variable Length ◽  
Planning Problem ◽  
Novel Variant ◽  
Planning Methods ◽  
Path Planning Problem

Mobile robots are currently exploited in various applications to enhance efficiency and reduce risks in hard activities for humans. The high autonomy in those systems is strongly related to the path-planning task. The path-planning problem is complex and requires in its formulation the adjustment of path elements that take the mobile robot from a start point to a target one at the lowest cost. Nevertheless, the identity or the number of the path elements to be adjusted is unknown; therefore, the human decision is necessary to determine this information reducing autonomy. Due to the above, this work conceives the path-planning as a Variable-Length-Vector optimization problem (VLV-OP) where both the number of variables (path elements) and their values must be determined. For this, a novel variant of Differential Evolution for Variable-Length-Vector optimization named VLV-DE is proposed to handle the path-planning VLV-OP for mobile robots. VLV-DE uses a population with solution vectors of different sizes adapted through a normalization procedure to allow interactions and determine the alternatives that better fit the problem. The effectiveness of this proposal is shown through the solution of the path-planning problem in complex scenarios. The results are contrasted with the well-known A* and the RRT*-Smart path-planning methods.

scholarly journals Complex Environment Path Planning for Unmanned Aerial Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5250
Author(s):  
Jing Zhang ◽  
Jiwu Li ◽  
Hongwei Yang ◽  
Xin Feng ◽  
Geng Sun
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Urban Environments ◽  
Planning Problem ◽  
Pruning Algorithm ◽  
Aerial Vehicles ◽  
Global Path Planning ◽  
Narrow Passage ◽  
Local Path ◽  
Path Planning Problem

Flying safely in complex urban environments is a challenge for unmanned aerial vehicles because path planning in urban environments with many narrow passages and few dynamic flight obstacles is difficult. The path planning problem is decomposed into global path planning and local path adjustment in this paper. First, a branch-selected rapidly-exploring random tree (BS-RRT) algorithm is proposed to solve the global path planning problem in environments with narrow passages. A cyclic pruning algorithm is proposed to shorten the length of the planned path. Second, the GM(1,1) model is improved with optimized background value named RMGM(1,1) to predict the flight path of dynamic obstacles. Herein, the local path adjustment is made by analyzing the prediction results. BS-RRT demonstrated a faster convergence speed and higher stability in narrow passage environments when compared with RRT, RRT-Connect, P-RRT, 1-0 Bg-RRT, and RRT*. In addition, the path planned by BS-RRT through the use of the cyclic pruning algorithm was the shortest. The prediction error of RMGM(1,1) was compared with those of ECGM(1,1), PCGM(1,1), GM(1,1), MGM(1,1), and GDF. The trajectory predicted by RMGM(1,1) was closer to the actual trajectory. Finally, we use the two methods to realize path planning in urban environments.

scholarly journals A Comparative Study of Various Intelligent Algorithms based Path Planning for Mobile Robots

2019 ◽  
Vol 25 (6) ◽  
pp. 83-100
Author(s):  
Muna Mohammed Jawad ◽  
Esraa Adnan Hadi
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Kinematic Model ◽  
Planning Problem ◽  
Minimum Path ◽  
First Case ◽  
Path Planning Problem ◽  
Better Than

In general, path-planning problem is one of most important task in the field of robotics. This paper describes the path-planning problem of mobile robot based on various metaheuristic algorithms. The suitable collision free path of a robot must satisfies certain optimization criteria such as feasibility, minimum path length, safety and smoothness and so on. In this research, various three approaches namely, PSO, Firefly and proposed hybrid FFCPSO are applied in static, known environment to solve the global path-planning problem in three cases. The first case used single mobile robot, the second case used three independent mobile robots and the third case applied three follow up mobile robot.  Simulation results, which carried out using MATLAB 2014 environment, show the validity of the kinematic model for Nonholonomic mobile robot and demonstration that the proposed algorithm perform better than original PSO and FF algorithms under the same environmental constraints by providing the smoothness velocity and shortest path for each mobile robot.                                                                                

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