scholarly journals HPPRM: Hybrid Potential Based Probabilistic Roadmap Algorithm for Improved Dynamic Path Planning of Mobile Robots

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 221743-221766
Author(s):  
Ankit A. Ravankar ◽  
Abhijeet Ravankar ◽  
Takanori Emaru ◽  
Yukinori Kobayashi
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Probabilistic Roadmap ◽  
Dynamic Path Planning ◽  
Dynamic Path

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 Dynamic Path Planning for Mobile Robots with Cellular Learning Automata

2016 ◽  
Vol 10 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Judhi Santoso ◽  
◽  
Bambang Riyanto ◽  
Widyawardhana Adiprawita ◽  
◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Learning Automata ◽  
Dynamic Path Planning ◽  
Dynamic Path ◽  
Cellular Learning Automata

scholarly journals Real-Time Dynamic Path Planning of Mobile Robots: A Novel Hybrid Heuristic Optimization Algorithm

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 188 ◽  
Author(s):  
Qing Wu ◽  
Zeyu Chen ◽  
Lei Wang ◽  
Hao Lin ◽  
Zijing Jiang ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Real Time ◽  
Potential Field ◽  
Optimization Method ◽  
Artificial Potential Field ◽  
Planning Time ◽  
Time Dynamic ◽  
Dynamic Path Planning ◽  
Dynamic Path

Mobile robots are becoming more and more widely used in industry and life, so the navigation of robots in dynamic environments has become an urgent problem to be solved. Dynamic path planning has, therefore, received more attention. This paper proposes a real-time dynamic path planning method for mobile robots that can avoid both static and dynamic obstacles. The proposed intelligent optimization method can not only get a better path but also has outstanding advantages in planning time. The algorithm used in the proposed method is a hybrid algorithm based on the beetle antennae search (BAS) algorithm and the artificial potential field (APF) algorithm, termed the BAS-APF method. By establishing a potential field, the convergence speed is accelerated, and the defect that the APF is easily trapped in the local minimum value is also avoided. At the same time, by setting a security scope to make the path closer to the available path in the real environment, the effectiveness and superiority of the proposed method are verified through simulative results.

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