Cooperative Motion Control and Sensing Architecture in Compliant Framed Modular Mobile Robots

2007 ◽  
Vol 23 (5) ◽  
pp. 1095-1101 ◽  
Author(s):  
Xiaorui Zhu ◽  
Youngshik Kim ◽  
R. Merrell ◽  
M.A. Minor
1996 ◽  
Vol 8 (5) ◽  
pp. 395-395 ◽  
Author(s):  
Hajime Asama ◽  

Distributed Robotic Systems are focused on as a new strategy to realize flexible, robust and fault-tolerant robotic systems. In conferences and symposia held recently, the number of papers related to the Distributed Robotic Systems has increased rapidly1,2,3) which shows this area has become one of the most interesting subjects in robotics. The Distributed Robotic Systems require a broad area of interdisciplinary technologies related not only to robotics and computer engineering (especially distributed artificial intelligence and artificial life), but also to biology and psychology. Distributed Robotic Systems can be defined as robot systems which are composed of various types and levels of units, such as cells, modules, agents and robots. One category of papers included in this volume is a robot with a distributed architecture, where modular structure is adopted and/or the robot system is controlled by many CPUs in a distributed manner. Cellular robotic systems are included in this category4). Another category of the papers is cooperative motion control of multiple robots. Coordinated control of multiple manipulators and cooperative motion control by multiple mobile robots using communication are discussed in these papers. The new elemental technologies are also presented, which are required for realization of advanced cooperative motion control of multiple autonomous mobile robots in this volume. The last category of the papers is self-organization of distributed robotic systems. Though the Journal of Robotics and MecharQnics has already published the special issues on the self-organization system,5,6) the latest progress is also presented in this volume. The papers belonging to this category are directed to swarm/collective intelligence in multi-robot cooperation issues. I believe this special issue will inspire the reader's interests in the Distributed Robotic Systems and accelerate the growth of this new arising interdisciplinary research area. References: 1)H.Asama, T.Fukuda, T.Arai and I.Endo eds., Distributed Autonomous Robotic Systems, Springer-Verlag, Tokyo, (1994). 2) H.Asama, T.Fukuda, T.Arai and I.Endo eds.,Distributed Autonomous Robotic Systems 2 , Springer-Verlag, Tokyo, (1996). 3) Robotics Society of Japan, Advanced Robotics 10,6, (1996). 4) T.Fukuda and T.Ueyama, Cellullar Robotics and Micro Robotic Systems,World Scientific, Singapore, (1994). 5) Fuji Technology Press Ltd., Journal of Robotics and Mechatronics,4,2,(1992). 6) Fuji Technology Press Ltd., Journal of Robotics and Mechatronics,4,3,(1992).


Author(s):  
Fumikazu MINAMIYAMA ◽  
Hidetsugu KOGA ◽  
Kentaro KOBAYASHI ◽  
Masaaki KATAYAMA

2010 ◽  
Vol 7 ◽  
pp. 109-117
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov ◽  
B.S. Yudintsev

The article deals with the development of a high-speed sensor system for a mobile robot, used in conjunction with an intelligent method of planning trajectories in conditions of high dynamism of the working space.


Author(s):  
Kazuhiro Kosuge ◽  
Tomohiro Oosumi ◽  
Hajime Asama ◽  
Teruo Fujii ◽  
Hayato Kaetsu

Symmetry ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1372
Author(s):  
Liang Zhang ◽  
Jongwon Kim ◽  
Jie Sun

Four-wheel Mecanum mobile robots (FWMRs) are widely used in transportation because of their omnidirectional mobility. However, the FWMR trades off energy efficiency for flexibility. To efficiently predict the energy consumption of the robot movement processes, this paper proposes a power consumption model for the omnidirectional movement of an FWMR. A power consumption model is of great significance for reducing the power consumption, motion control, and path planning of robots. However, FWMRs are highly maneuverable, meaning their control is complicated and their energy modeling is extremely complex. The speed, distance, path, and power consumption of the robot can vary greatly depending on the control method. This energy model was mathematically implemented in MATLAB and validated by our laboratory’s Mecanum wheel robot. The prediction accuracy of the model was over 95% through simulation and experimental verification.


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