Investigation into Swarm-based Cooperative Behaviour in Execution of Open Field Agricultural Tasks

JANANI, Alireza (2018). Investigation into Swarm-based Cooperative Behaviour in Execution of Open Field Agricultural Tasks. Doctoral, Sheffield Hallam University.

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Abstract

Because of the significant drop in the number of farmers and increase in the earth population, the use of autonomous farming units including unmanned tractors is becoming more and more popular. However, relying on a single autonomous farming unit to carry out the entire task on a large field is inefficient. Using multiple autonomous tractors bring more efficiency, however, without cooperation this attempt will fail (Mataric et al., 1995). This cooperation can be achieved by an appropriate task allocation and coordination mechanism between the participating units. The current trend in this field is to use direct forms of communication in any form of directional or broadcasting meaningful messages among the group. The messages assist the group to identify the state of the task, assigned workload, collision and congestion avoidance, and etc. These forms of approaches are fast and efficient when units are within the communicating signal range. In this thesis, we aim to investigate the feasibility of cooperative execution of open field farming task including spraying and ploughing while inter-team interaction is other than direct communication methods. For every task, an algorithm is suggested and an appropriate mathematical model is presented. Then, using ROS Stage simulation environment, each algorithm is implemented and multiple tests are conducted. Finally, the simulation results and the correspondent mathematical results are compared and appropriate modifications are suggested.

Item Type:	Thesis (Doctoral)
Contributors:	Thesis advisor - Penders, Jacques
Additional Information:	Director of studies: Jacques Penders "No PQ harvesting"
Research Institute, Centre or Group - Does NOT include content added after October 2018:	Sheffield Hallam Doctoral Theses
Identification Number:	https://doi.org/10.7190/shu-thesis-24182
Depositing User:	Colin Knott
Date Deposited:	05 Mar 2019 14:48
Last Modified:	26 Apr 2021 13:30
URI:	https://shura.shu.ac.uk/id/eprint/24182

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