scholarly journals An Auto-Adaptive Multi-Objective Strategy for Multi-Robot Exploration of Constrained-Communication Environments

2019 ◽  
Vol 9 (3) ◽  
pp. 573 ◽  
Author(s):  
Facundo Benavides ◽  
Caroline Ponzoni Carvalho Chanel ◽  
Pablo Monzón ◽  
Eduardo Grampín
Keyword(s):  
Main Part ◽  
Mobile Robotics ◽  
Complete Coverage ◽  
Multi Objective ◽  
Exploration Time ◽  
Robot Systems ◽  
Communication Environments ◽  
Autonomous Mobile Robotics ◽  
Selection Of ◽  
Multi Robot

The exploration problem is a fundamental subject in autonomous mobile robotics that deals with achieving the complete coverage of a previously unknown environment. There are several scenarios where completing exploration of a zone is a main part of the mission. Due to the efficiency and robustness brought by multi-robot systems, exploration is usually done cooperatively. Wireless communication plays an important role in collaborative multi-robot strategies. Unfortunately, the assumption of stable communication and end-to-end connectivity may be easily compromised in real scenarios. In this paper, a novel auto-adaptive multi-objective strategy is followed to support the selection of tasks regarding both exploration performance and connectivity level. Compared with others, the proposed approach shows effectiveness and flexibility to tackle the multi-robot exploration problem, being capable of decreasing the last of disconnection periods without noticeable degradation of the completion exploration time.

scholarly journals Collaborative Complete Coverage and Path Planning for Multi-Robot Exploration

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3709
Author(s):  
Huei-Yung Lin ◽  
Yi-Chun Huang
Keyword(s):  
Path Planning ◽  
Mobile Robotics ◽  
Mobile Robot Navigation ◽  
Selection Function ◽  
Complete Coverage ◽  
Goal Selection ◽  
And Performance ◽  
Collaborative Exploration ◽  
The Individual ◽  
Multi Robot

In mobile robotics research, the exploration of unknown environments has always been an important topic due to its practical uses in consumer and military applications. One specific interest of recent investigation is the field of complete coverage and path planning (CCPP) techniques for mobile robot navigation. In this paper, we present a collaborative CCPP algorithms for single robot and multi-robot systems. The incremental coverage from the robot movement is maximized by evaluating a new cost function. A goal selection function is then designed to facilitate the collaborative exploration for a multi-robot system. By considering the local gains from the individual robots as well as the global gain by the goal selection, the proposed method is able to optimize the overall coverage efficiency. In the experiments, our CCPP algorithms are carried out on various unknown and complex environment maps. The simulation results and performance evaluation demonstrate the effectiveness of the proposed collaborative CCPP technique.

Adaptive Division-of-Labor Control Algorithm for Multi-Robot Systems

2010 ◽  
Vol 22 (4) ◽  
pp. 514-525 ◽  
Author(s):  
Yusuke Ikemoto ◽  
◽  
Toru Miura ◽  
Hajime Asama ◽  
◽  
...  
Keyword(s):  
Division Of Labor ◽  
Performance Improvement ◽  
Group Performance ◽  
Homogeneous State ◽  
Adaptive Selection ◽  
Labor Control ◽  
Robot Systems ◽  
Multi Agent ◽  
Selection Of ◽  
Multi Robot

An advanced function for multi-robot systems is the division of labor. There are some studies proposing a multi-agent reinforcement learning method for a division of labor. However, it often requires much time to converge. Many studies focusing on division-of-labor control inspired biological phenomenon have been reported. In those methods, whether heterogeneous or homogeneous state is determined by self-organization, however, group performance improvement is not guaranteed because decentralized control is typically complicated. In this study, we propose adaptive division-of-labor control, enabling adaptive selection of homogeneous or heterogeneous group state. We demonstrate the adaptability of proposal method versus working conditions and address the performance improvement by mathematical analysis. To evaluate the effectiveness of the proposed method, we treat foraging by multi-robot systems and confirm that the robot group inevitably organizes the division of labor with group performance improvement in computer simulations.

scholarly journals Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer

2019 ◽  
Vol 9 (14) ◽  
pp. 2931 ◽  
Author(s):  
Kamalova ◽  
Navruzov ◽  
Qian ◽  
Lee
Keyword(s):  
Mobile Robotics ◽  
Sensor Data ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Objective Functions ◽  
Indoor Space ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
In The Beginning ◽  
Multi Robot

In this paper, we used multi-objective optimization in the exploration of unknown space. Exploration is the process of generating models of environments from sensor data. The goal of the exploration is to create a finite map of indoor space. It is common practice in mobile robotics to consider the exploration as a single-objective problem, which is to maximize a search of uncertainty. In this study, we proposed a new methodology of exploration with two conflicting objectives: to search for a new place and to enhance map accuracy. The proposed multiple-objective exploration uses the Multi-Objective Grey Wolf Optimizer algorithm. It begins with the initialization of the grey wolf population, which are waypoints in our multi-robot exploration. Once the waypoint positions are set in the beginning, they stay unchanged through all iterations. The role of updating the position belongs to the robots, which select the non-dominated waypoints among them. The waypoint selection results from two objective functions. The performance of the multi-objective exploration is presented. The trade-off among objective functions is unveiled by the Pareto-optimal solutions. A comparison with other algorithms is implemented in the end.

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 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.

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
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