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.

scholarly journals Decision-Making Under Uncertainty in Multi-Agent and Multi-Robot Systems: Planning and Learning

2018 ◽  
Author(s):  
Christopher Amato
Keyword(s):  
Decision Making Under Uncertainty ◽  
High Quality ◽  
Learning Methods ◽  
Robust Solutions ◽  
Systems Planning ◽  
Robot Systems ◽  
Multi Agent ◽  
Robot Tasks ◽  
Multi Agent Planning ◽  
Multi Robot

Multi-agent planning and learning methods are becoming increasingly important in today's interconnected world. Methods for real-world domains, such as robotics, must consider uncertainty and limited communication in order to generate high-quality, robust solutions. This paper discusses our work on developing principled models to represent these problems and planning and learning methods that can scale to realistic multi-agent and multi-robot tasks.

scholarly journals Resilient Task Allocation in Heterogeneous Multi-Robot Systems

2021 ◽  
Vol 6 (2) ◽  
pp. 1327-1334
Author(s):  
Siddharth Mayya ◽  
Diego S. D'antonio ◽  
David Saldana ◽  
Vijay Kumar
Keyword(s):  
Task Allocation ◽  
Robot Systems ◽  
Multi Robot

scholarly journals Decentralized Task and Path Planning for Multi-Robot Systems

2021 ◽  
Vol 6 (3) ◽  
pp. 4337-4344
Author(s):  
Yuxiao Chen ◽  
Ugo Rosolia ◽  
Aaron D. Ames
Keyword(s):  
Path Planning ◽  
Robot Systems ◽  
Multi Robot

scholarly journals Research Progress on Synergistic Technologies of Agricultural Multi-Robots

2021 ◽  
Vol 11 (4) ◽  
pp. 1448
Author(s):  
Wenju Mao ◽  
Zhijie Liu ◽  
Heng Liu ◽  
Fuzeng Yang ◽  
Meirong Wang
Keyword(s):  
Path Planning ◽  
Formation Control ◽  
Research Progress ◽  
Hybrid Architecture ◽  
Labor Costs ◽  
Robot System ◽  
System Architectures ◽  
Research Results ◽  
Robot Systems ◽  
Multi Robot

Multi-robots have shown good application prospects in agricultural production. Studying the synergistic technologies of agricultural multi-robots can not only improve the efficiency of the overall robot system and meet the needs of precision farming but also solve the problems of decreasing effective labor supply and increasing labor costs in agriculture. Therefore, starting from the point of view of an agricultural multiple robot system architectures, this paper reviews the representative research results of five synergistic technologies of agricultural multi-robots in recent years, namely, environment perception, task allocation, path planning, formation control, and communication, and summarizes the technological progress and development characteristics of these five technologies. Finally, because of these development characteristics, it is shown that the trends and research focus for agricultural multi-robots are to optimize the existing technologies and apply them to a variety of agricultural multi-robots, such as building a hybrid architecture of multi-robot systems, SLAM (simultaneous localization and mapping), cooperation learning of robots, hybrid path planning and formation reconstruction. While synergistic technologies of agricultural multi-robots are extremely challenging in production, in combination with previous research results for real agricultural multi-robots and social development demand, we conclude that it is realistic to expect automated multi-robot systems in the future.

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