Abstract
Precision agriculture represents a very promising domain for swarm robotics, as it deals with expansive fields and tasks that can be parallelised and executed with a collaborative approach. Weed monitoring and mapping is one such problem, and solutions have been proposed that exploit swarms of unmanned aerial vehicles (UAVs). With this paper, we move one step forward towards the deployment of UAV swarms in the field. We present the implementation of a collective behaviour for weed monitoring and mapping, which takes into account all the processes to be run onboard, including machine vision and collision avoidance. We present simulation results to evaluate the efficiency of the proposed system once that such processes are considered, and we also run hardware-in-the-loop simulations which provide a precise profiling of all the system components, a necessary step before final deployment in the field.
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Acknowledgments
This work has been supported by SAGA (Swarm Robotics for Agricultural Applications), an experiment founded by the European project ECHORD++ (GA: 601116). Dario Albani and Daniele Nardi acknowledge partial support from the European project FLOURISH (GA: 644227).
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A Appendix - Additional Experiments
A Appendix - Additional Experiments
In this section we present additional data coming from some more experiments performed within this study. In particular, we present results for coverage and mapping time obtained by varying the number of robots involved in the simulation. As expected, both the mapping and the coverage problem benefit from the increased density of agents. We also observe that such increase in performance does not scale linearly due to non-beneficial interactions between the agents (i.e. issues related to overcrowding). Last, we observe a “right shift” in the minima of the mapping time \(t_m\) when the number of agents in the swarm increases. This is expected, as a larger number of agents increases the repulsion force acting on the single UAV, thus requiring higher attraction forces from the beacons to be effective (Figs. 5, 6 and 7).
Comparison of the coverage and mapping time for N = 50 robots. Each cell in the heatmap represents the average of 150 runs.
Comparison of the coverage and mapping time for N = 75 robots. Each cell in the heatmap represents the average of 150 runs.
Comparison of the coverage and mapping time for N = 100 robots. Each cell in the heatmap represents the average of 150 runs.
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Albani, D., Manoni, T., Arik, A., Nardi, D., Trianni, V. (2019). Field Coverage for Weed Mapping: Toward Experiments with a UAV Swarm. In: Compagnoni, A., Casey, W., Cai, Y., Mishra, B. (eds) Bio-inspired Information and Communication Technologies. BICT 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 289. Springer, Cham. https://doi.org/10.1007/978-3-030-24202-2_10
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