A virtual force interaction scheme for multi-robot environment monitoring

Ji, Kang; Zhang, Bin; Zhi, Yuan; Cheng, Hui; Yu, D. W.

doi:10.1016/j.robot.2021.103967

Cited by 9 publications

(4 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mathematical model of the spiral equation represents the rotational flight between all vultures and one of the two best vultures. Equations ( 17) and ( 18) express the rotating flight (18).…”

Section: Forth Phasementioning

confidence: 99%

“…In the current iteration, BV(i) is obtained using Equation ( 6) and is then used in Equations ( 17) and (18) to update the vulture's location. The sine and cosine functions are denoted by "sin" and "cos", respectively.…”

Section: Forth Phasementioning

confidence: 99%

“…This novel approach aims to optimize the construction of finite maps during multi-robot exploration missions. HVCME effectively addresses the limitations of previous methods [18][19][20][21][22], such as inefficiency and incomplete exploration, by prioritizing adjacent cells around a robot and employing the foraging behavior of African vultures for search space optimization. Consequently, this leads to a substantial enhancement in exploration efficiency and accuracy, proving beneficial for applications in search and rescue operations, surveillance, and environmental monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…10. If |R| < 1 and |R| ≥ 0.5, then update the location of the vulture using Equation (15) if EP 2 ≥ rand EP2 , otherwise use Equation (18). 11.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Hybrid Vulture-Coordinated Multi-Robot Exploration: A Novel Algorithm for Optimization of Multi-Robot Exploration

2023

View full text Add to dashboard Cite

Exploring unknown environments using multiple robots has numerous applications in various fields but remains a challenging task. This study proposes a novel hybrid optimization method called Hybrid Vulture-Coordinated Multi-Robot Exploration (HVCME), which combines Coordinated Multi-Robot Exploration (CME) and African Vultures Optimization Algorithm (AVOA) to optimize the construction of a finite map in multi-robot exploration. We compared HVCME with four other similar algorithms using three performance measures: run time, percentage of the explored area, and the number of times the method failed to complete a run. The experimental results show that HVCME outperforms the other four methods, demonstrating its effectiveness in optimizing the construction of a finite map in an unknown indoor environment.

show abstract

Section: Forth Phasementioning

confidence: 99%

Section: Forth Phasementioning

confidence: 99%