Accesso libero

Research on Control Algorithm of Visual Servo Grasping Operating System for Unmanned Aerial Vehicle Robot Arm for Transmission Line Inspection

Shi Ru

Shi Ru

State Grid Heilongjiang Electric Power Company Limited Extrahigh Voltage CompanyHarbin, China
Cerca questo autore su
Sciendo|Google Scholar
Ru, Shi
, 
Bowen Zhang

Bowen Zhang

Northeast Branch of State Grid Corporation of ChinaShenyang, China
Cerca questo autore su
Sciendo|Google Scholar
Zhang, Bowen
, 
Jinfu Han

Jinfu Han

State Grid Heilongjiang Electric Power Company Limited Extrahigh Voltage CompanyHarbin, China
Cerca questo autore su
Sciendo|Google Scholar
Han, Jinfu
, 
Bo Guan

Bo Guan

State Grid Heilongjiang Electric Power Company Limited Extrahigh Voltage CompanyHarbin, China
Cerca questo autore su
Sciendo|Google Scholar
Guan, Bo
 e 
Wei Yang

Wei Yang

State Grid Heilongjiang Electric Power Company Limited Extrahigh Voltage CompanyHarbin, China
Cerca questo autore su
Sciendo|Google Scholar
Yang, Wei
  
22 set 2025
Research on Control Algorithm of Visual Servo Grasping Operating System for Unmanned Aerial Vehicle Robot Arm for Transmission Line Inspection's Cover Image
INFORMAZIONI SU QUESTO ARTICOLO
Articolo precedente
Articolo Successivo
Cita
Scarica la copertina
Pubblicato online: 22 set 2025
Ricevuto: 12 gen 2025
Accettato: 25 apr 2025
DOI: https://doi.org/10.2478/amns-2025-0955
Parole chiave
© 2025 Shi Ru et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

PBVS basic process
PBVS basic process

Figure 2.

Distributed data fusion is the optimal position estimation
Distributed data fusion is the optimal position estimation

Figure 3.

Owa distributed data fusion process
Owa distributed data fusion process

Figure 4.

RRT algorithm planning
RRT algorithm planning

Figure 5.

RRT-Connect algorithm planning
RRT-Connect algorithm planning

Figure 6.

Search time
Search time

Figure 7.

Path length
Path length

Figure 8.

Error convergence curve
Error convergence curve

Figure 9.

Characteristic trajectory
Characteristic trajectory

The static clutter scene captures performance comparisons

Contrast model GSR SSR PE (cm) GPS
IBVS 0.49 0.69 0.34,1.87,0.36 0.845
Deep MPCVS 0.73 0.78 0.34,1.81,0.28 0.829
PID-IBVS 0.78 0.71 0.32,1.86,0.27 0.856
SIFT 0.78 0.84 0.25,1.84,0.27 0.913
KOVIS 0.91 0.90 0.23,1.49,0.34 0.956
This method 0.93 0.95 0.19,1.11,0.18 0.975
Lingua:
Inglese
Frequenza di pubblicazione:
1 volte all'anno
Argomenti della rivista:
Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro
Feed RSS della rivista