Investigation of Multi-Robots Food Foraging Efficiency with an Artificial Pheromone System

Mou, Guanjie (2019) Investigation of Multi-Robots Food Foraging Efficiency with an Artificial Pheromone System. MRes thesis, University of Lincoln.

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Investigation of Multi-Robots Food Foraging Efficiency with an Artificial Pheromone System
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Item Type:Thesis (MRes)
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Abstract

In nature, the pheromone released by social insects is crucial for communication, which
has become a rich inspiration source of swarm robotics. By utilising the virtual
pheromone in physical swarm robot system, we can coordinate individuals and simulate
behaviours of social insects. This thesis aims to investigate two influences, i.e., the
leader and the wind effects on multi-robots’ food foraging efficiency in an artificial
pheromone system, wherein the pheromone is represented by light spots or trails on a
TV screen. To investigate the leader effect, we remotely controlled a robot agent as a
leader to guide other wandering agents to reach a food source with persistent virtual
pheromone and then aggregate around it; the released pheromone by the leader could
be sensed by other mates so that triggering following behaviour. We compare the
aggregation efficiency with the scenarios without a leader robot agent. After that, we
simulated wind effects on the virtual pheromone affecting its evaporation and diffusion.
The experimental results demonstrate that without interacting with the leader, the
aggregation efficiency is highly depending on start positions of follower agents within
each experiment. The potential of using the leader interaction with the other robots can
improve the swarm efficiency under the same experimental setting. Moreover, the
experimenting results of wind effects on the artificial pheromone system and the food
foraging simulation demonstrate the wind has the power to influence the food foraging
efficiency, which cannot be ignored. This research indicates that the leader and the wind
effects are important factors affecting the pheromone-based swarm efficiency.

Divisions:College of Science > School of Computer Science
ID Code:39310
Deposited On:23 Dec 2019 16:21

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