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TZID:Asia/Hong_Kong
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DTSTART:19791021T023000
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BEGIN:VEVENT
DTSTAMP:20260330T141602Z
UID:CBA8E69A-4068-49AA-A86E-00D1E9045972
DTSTART;TZID=Asia/Hong_Kong:20260327T150000
DTEND;TZID=Asia/Hong_Kong:20260327T163000
DESCRIPTION:A Particle Swarm Optimizer (PSO) and mobile robot swarm are two
  widely studied subjects. Many applications emerge separately while the si
 milarity between them is rarely explored. When a solution space is a certa
 in region in reality\, a robot swarm can replace a particle one to explore
  the optimal solution by performing PSO. In this way\, a mobile robot swar
 m should be able to efficiently explore an area just like a particle swarm
  and uninterruptedly work even under the shortage of robots or in the case
  of unexpected failure of robots. Furthermore\, the moving distances of ro
 bots are highly constrained because energy of robots is limited and so is 
 their operation time. Inspired by such requirements\, this presentation di
 scusses a Moving-distance-minimized PSO for a mobile robot swarm to minimi
 ze the total moving distance of its robots while performing optimization a
 nd collaboration. The distances between the current robot positions and th
 e particle ones in the next generation are utilized to derive paths for ro
 bots such that the total distance that all robots move is minimized\, henc
 e minimizing the energy and time for a robot swarm to locate the optima. E
 xperimental results on optimizing benchmark functions show the advantage o
 f the proposed method over the standard PSO. By adopting it\, the moving d
 istance of robots can be reduced by more than 40% while offering the same 
 optimization effects. The implication is enormous since all population-bas
 ed optimization algorithms can be potentially benefited from such replacem
 ent of their individuals with mobile robots\, thus leading to their moving
 -distance-minimized variants.\n\nCo-sponsored by: Wuhan University of Tech
 nology\n\nSpeaker(s): \, Mengchu Zhou\n\nRoom: 416\, Bldg: Eastern Teachin
 g Building \, Heping Avenue 1178\, Yujiatou Campus\, Wuhan University of T
 echnology\, Wuhan\, Hebei\, China\, 430063
LOCATION:Room: 416\, Bldg: Eastern Teaching Building \, Heping Avenue 1178\
 , Yujiatou Campus\, Wuhan University of Technology\, Wuhan\, Hebei\, China
 \, 430063
ORGANIZER:liwf_cn@126.com
SEQUENCE:35
SUMMARY:Managing Mobile Robot Swarm with Particle Swarm Optimizer
URL;VALUE=URI:https://events.vtools.ieee.org/m/550438
X-ALT-DESC:Description: <br /><p class="MsoNormal">A Particle Swarm Optimiz
 er (PSO) and mobile robot swarm are two widely studied subjects. Many appl
 ications emerge separately while the similarity between them is rarely exp
 lored. When a solution space is a certain region in reality\, a robot swar
 m can replace a particle one to explore the optimal solution by performing
  PSO. In this way\, a mobile robot swarm should be able to efficiently exp
 lore an area just like a particle swarm and uninterruptedly work even unde
 r the shortage of robots or in the case of unexpected failure of robots. F
 urthermore\, the moving distances of robots are highly constrained because
  energy of robots is limited and so is their operation time. Inspired by s
 uch requirements\, this presentation discusses a Moving-distance-minimized
  PSO for a mobile robot swarm to minimize the total moving distance of its
  robots while performing optimization and collaboration. The distances bet
 ween the current robot positions and the particle ones in the next generat
 ion are utilized to derive paths for robots such that the total distance t
 hat all robots move is minimized\, hence minimizing the energy and time fo
 r a robot swarm to locate the optima. Experimental results on optimizing b
 enchmark functions show the advantage of the proposed method over the stan
 dard PSO. By adopting it\, the moving distance of robots can be reduced by
  more than 40% while offering the same optimization effects. The implicati
 on is enormous since all population-based optimization algorithms can be p
 otentially benefited from such replacement of their individuals with mobil
 e robots\, thus leading to their moving-distance-minimized variants.</p>
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