TY  - CONF
ID  - SisLab2247
UR  - http://www.springer.com/gp/book/9783319490724
A1  - Hoang, Anh Quy
A1  - Pham, Minh Trien
Y1  - 2017///
N2  - Odor source localization is a problem of great importance. Two mainstream methods among numerous proposed ones are probabilistic algorithms and bio-inspired algorithms. Compared to probabilistic algorithms, biomimetic approaches are much less intensive in term of computational cost. Thus, despite their slightly worse performance, biomimetic approaches have received much more attention. In this paper, a novel method based on a bio-inspired algorithm - Particle Swarm Optimization (PSO) - is proposed for a multi-robot system (MRS). The proposed algorithm makes use of wind information and immediate odor gradient to enhance the performance of the MRS. A mechanism based on Artificial Potential Field (APF) is utilized to ensure non-collision movement of the robots. This method is tested by simulation on Matlab. Data for the test scenarios, all in large scales, are generated using Fluent. Nearly 2000 runs are carried out and the simulation results confirm the proposed algorithm?s effectiveness.
PB  - Springer
VL  - 538
SN  - 2194-5357
TI  - Swarm Intelligence-Based Approach for Macroscopic Scale Odor Source Localization Using Multi-robot System
SP  - 593
M2  - Vietnam
AV  - restricted
EP  - 602
T2  - International Conference on Advances in Information and Communication Technology
ER  -