Motion Coordination of Multiple Unicycle Robotic Vehicles under Operational Constraints in Obstacle-Cluttered Workspaces

Author(s):  
Charalampos P. Bechlioulis ◽  
Panagiotis Vlantis ◽  
Kostas J. Kyriakopoulos
1999 ◽  
Author(s):  
Mark A. Ericson ◽  
Robert S. Bolia ◽  
W. Todd Nelson ◽  
Richard L. McKinley

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3416
Author(s):  
Gheorghe Dumitrașcu ◽  
Michel Feidt ◽  
Ştefan Grigorean

This paper develops simplifying entropic models of irreversible closed cycles. The entropic models involve the irreversible connections between external and internal main operational parameters with finite physical dimensions. The external parameters are the mean temperatures of external heat reservoirs, the heat transfers thermal conductance, and the heat transfer mean log temperatures differences. The internal involved parameters are the reference entropy of the cycle and the internal irreversibility number. The cycle’s design might use four possible operational constraints in order to find out the reference entropy. The internal irreversibility number allows the evaluation of the reversible heat output function of the reversible heat input. Thus the cycle entropy balance equation to design the trigeneration cycles only through external operational parameters might be involved. In designing trigeneration systems, they must know the requirements of all consumers of the useful energies delivered by the trigeneration system. The conclusions emphasize the complexity in designing and/or optimizing the irreversible trigeneration systems.


2020 ◽  
Vol 4 (OOPSLA) ◽  
pp. 1-30
Author(s):  
Rupak Majumdar ◽  
Nobuko Yoshida ◽  
Damien Zufferey
Keyword(s):  

1992 ◽  
Vol 2 (2) ◽  
pp. 181-193 ◽  
Author(s):  
Brian H. Wilcox

2004 ◽  
Vol 126 (4) ◽  
pp. 891-895 ◽  
Author(s):  
J. L. Dohner and ◽  
G. R. Eisler ◽  
B. J. Driessen ◽  
J. Hurtado

A control algorithm has been developed and experimentally validated for guiding swarms of robotic vehicles to acoustic targets. This novel algorithm uses pressure measurements from a set of sensors, each attached to a vehicle of the swarm, to deduce energy flows from the environment, and to move in the direction of maximum reflected intensity while controlling constraints between vehicles. The algorithm was validated using a collective of eight hand-placed microphones in an open-space area with a 50-m separation between an emitter and scatterer.


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