Accomplish multi-robot tasks via Petri net models

2015 ◽  
Author(s):  
Marius Kloetzer ◽  
Cristian Mahulea
Keyword(s):  
Petri Net ◽  
Robot Tasks ◽  
Multi Robot

scholarly journals Exploring Robot Connectivity and Collaborative Sensing in a High-School Enrichment Program

Robotics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Igor M. Verner ◽  
Dan Cuperman ◽  
Michael Reitman
Keyword(s):  
High School ◽  
Positive Contribution ◽  
Smart Transportation ◽  
Sensory Data ◽  
Robot Systems ◽  
Enrichment Program ◽  
Collaborative Sensing ◽  
Robot Tasks ◽  
Value Of Learning ◽  
Multi Robot

Education is facing challenges to keep pace with the widespread introduction of robots and digital technologies in industry and everyday life. These challenges necessitate new approaches to impart students at all levels of education with the knowledge of smart connected robot systems. This paper presents the high-school enrichment program Intelligent Robotics and Smart Transportation, which implements an approach to teaching the concepts and skills of robot connectivity, collaborative sensing, and artificial intelligence, through practice with multi-robot systems. The students used a simple control language to program Bioloid wheeled robots and utilized Phyton and Robot Operating System (ROS) to program Tello drones and TurtleBots in a Linux environment. In their projects, the students implemented multi-robot tasks in which the robots exchanged sensory data via the internet. Our educational study evaluated the contribution of the program to students’ learning of connectivity and collaborative sensing of robot systems and their interest in modern robotics. The students’ responses indicated that the program had a high positive contribution to their knowledge and skills and fostered their interest in the learned subjects. The study revealed the value of learning of internet of things and collaborative sensing for enhancing this contribution.

A Study on a Foraging Behavior of Interacting Simple Robots

2003 ◽  
Vol 7 (2) ◽  
pp. 108-114 ◽  
Author(s):  
Ken Sugawara ◽  
◽  
Masaki Sano ◽  
Toshinori Watanabe ◽  
◽  
...  
Keyword(s):  
Light Signal ◽  
Home Location ◽  
Considerable Research ◽  
Robot Systems ◽  
Point Field ◽  
Robot Tasks ◽  
Foraging Task ◽  
Robot Group ◽  
Simple Interaction ◽  
Multi Robot

Considerable research is currently being conducted in the area of multi-robot systems. The most remarkable characteristic of these types of systems is that the robots are able to work cooperatively to complete a task that a single robot cannot accomplish by itself. This characteristic is essential in the investigation of the effect of the number of robots in a given system. Out of the various possible multi-robot tasks, a foraging task was chosen for these experiments. The robots used in the experiments referenced by this paper had a simple interaction method with a light signal. The robots’ behavior in a one feeding point field was first discussed. This behavior was analyzed by both a robot simulation and a mathematical model. In the next experiment, numerous feeding points, equidistant from the home location, were arranged in the foraging field. The performance of the robots in this arrangement was then discussed. This report highlights the ordered behavior of the robot group, which greatly depends upon the number of robots and the strength of their interaction.

A Petri net based approach for multi-robot path planning

2013 ◽  
Vol 24 (4) ◽  
pp. 417-445 ◽  
Author(s):  
Marius Kloetzer ◽  
Cristian Mahulea
Keyword(s):  
Path Planning ◽  
Petri Net ◽  
Robot Path Planning ◽  
Robot Path ◽  
Multi Robot

Robot coalition formation against time-extended multi-robot tasks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Muhammad Usman Arif
Keyword(s):  
Coalition Formation ◽  
Design Methodology ◽  
Directed Acyclic Graph ◽  
Deadlock Detection ◽  
Solution Quality ◽  
Content Type ◽  
Hard Time ◽  
Robot Tasks ◽  
Multi Robot

PurposeMulti-robot coalition formation (MRCF) refers to the formation of robot coalitions against complex tasks requiring multiple robots for execution. Situations, where the robots have to participate in multiple coalitions over time due to a large number of tasks, are called Time-extended MRCF. While being NP-hard, time-extended MRCF also holds the possibility of resource deadlocks due to any cyclic hold-and-wait conditions among the coalitions. Existing schemes compromise on solution quality to form workable, deadlock-free coalitions through instantaneous or incremental allocations.Design/methodology/approachThis paper presents an evolutionary algorithm (EA)-based task allocation framework for improved, deadlock-free solutions against time-extended MRCF. The framework simultaneously allocates multiple tasks, allowing the robots to participate in multiple coalitions within their schedule. A directed acyclic graph–based representation of robot plans is used for deadlock detection and avoidance.FindingsAllowing the robots to participate in multiple coalitions within their schedule, significantly improves the allocation quality. The improved allocation quality of the EA is validated against two auction schemes inspired by the literature.Originality/valueTo the best of the author's knowledge, this is the first framework which simultaneously considers multiple MR tasks for deadlock-free allocation while allowing the robots to participate in multiple coalitions within their plans.

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