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QRDPSO: A New Optimization Method for Swarm Robot Searching and Obstacle Avoidance in Dynamic Environments


Authors



Abstract

In this paper we show how the quantum-based particle swarm optimization (QPSO) method is adopted to derive a new derivation for robotics application in search and rescue simulations. The new derivation, called the Quantum Robot Darwinian PSO (QRDPSO) is inspired from another PSO-based algorithm, the Robot Darwinian PSO (RDPSO). This paper includes comprehensive details on the QRDPSO formulation and parameters control which show how the swarm overcomes communication constraints to avoid obstacles and achieve optimal solution. The results show the QRDPSO is an upgrade over RDPSO in terms of convergence speed, trajectory control, obstacle avoidance and connectivity performance of the swarm.


Keywords


Pages

Total Pages: 8

DOI
10.31209/2019.100000124


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Published

Online Article

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




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