Fault Tolerant Automated Task Execution in a Multi-robot System

2015 ◽  
pp. 101-107
Author(s):  
Stanislaw Ambroszkiewicz ◽  
Waldemar Bartyna ◽  
Kamil Skarzynski ◽  
Marcin Stepniak
Keyword(s):  
Fault Tolerant ◽  
Task Execution ◽  
Robot System ◽  
Multi Robot

scholarly journals Cost-efficient inter-robot delivery for resource-constrained and interdependent multi-robot schedules

2019 ◽  
Vol 16 (1) ◽  
pp. 172988141982804 ◽  
Author(s):  
Yin Chen ◽  
Xinjun Mao ◽  
Shuo Yang ◽  
Qiuzhen Wang
Keyword(s):  
Cost Efficiency ◽  
Resource Constrained ◽  
Task Execution ◽  
Robot System ◽  
Sequential Execution ◽  
Constrained Environments ◽  
Cost Efficient ◽  
The Cost ◽  
Multi Robot

A multi-robot system in resource-constrained environments needs to obtain resources for task execution. Typically, resources can be fetched from fixed stations, which, however, can be costly and even impossible when fixed stations are unavailable, depleted or distant from task execution locations. We present a method that allows robots to acquire urgently required resources from those robots with superfluous residual resources, by conducting rendezvouses with these robots. We consider a scenario where tasks are organised into a schedule on each robot for sequential execution, with cross-schedule dependencies for inter-robot collaboration. We design an algorithm to systematically generate such rendezvouses for entire multi-robot system to increase the proportion of tasks whose resource demands are satisfied. We also design an algorithm that periodically reallocates tasks among robots to improve the cost-efficiency of schedules. Our experiment shows the synergetic effectiveness of both algorithms, when fixed stations are unavailable and all resources are fetched through inter-robot delivery. We also investigate the effectiveness of inter-robot delivery in scenarios where fixed stations are existent but distant from the locations of tasks.

scholarly journals Reinforcement-Learning-Based Asynchronous Formation Control Scheme for Multiple Unmanned Surface Vehicles

2021 ◽  
Vol 11 (2) ◽  
pp. 546
Author(s):  
Jiajia Xie ◽  
Rui Zhou ◽  
Yuan Liu ◽  
Jun Luo ◽  
Shaorong Xie ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Formation Control ◽  
Rapid Development ◽  
Gradient Algorithm ◽  
Robot System ◽  
Physical Relationship ◽  
Unmanned Surface Vehicles ◽  
Main Challenge ◽  
Control Scheme ◽  
Multi Robot

The high performance and efficiency of multiple unmanned surface vehicles (multi-USV) promote the further civilian and military applications of coordinated USV. As the basis of multiple USVs’ cooperative work, considerable attention has been spent on developing the decentralized formation control of the USV swarm. Formation control of multiple USV belongs to the geometric problems of a multi-robot system. The main challenge is the way to generate and maintain the formation of a multi-robot system. The rapid development of reinforcement learning provides us with a new solution to deal with these problems. In this paper, we introduce a decentralized structure of the multi-USV system and employ reinforcement learning to deal with the formation control of a multi-USV system in a leader–follower topology. Therefore, we propose an asynchronous decentralized formation control scheme based on reinforcement learning for multiple USVs. First, a simplified USV model is established. Simultaneously, the formation shape model is built to provide formation parameters and to describe the physical relationship between USVs. Second, the advantage deep deterministic policy gradient algorithm (ADDPG) is proposed. Third, formation generation policies and formation maintenance policies based on the ADDPG are proposed to form and maintain the given geometry structure of the team of USVs during movement. Moreover, three new reward functions are designed and utilized to promote policy learning. Finally, various experiments are conducted to validate the performance of the proposed formation control scheme. Simulation results and contrast experiments demonstrate the efficiency and stability of the formation control scheme.

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.

scholarly journals Optically tracked multi-robot system for keyhole neurosurgery

2011 ◽  
Author(s):  
Mirko Daniele Comparetti ◽  
Elena De Momi ◽  
Alberto Vaccarella ◽  
Matthias Riechmann ◽  
Giancarlo Ferrigno
Keyword(s):  
Robot System ◽  
Multi Robot
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