SAGA Drone Swarms in the Field Vito Trianni vito.trianni@istc.cnr.it Workshop on Small UAVs for Precision Agriculture May the 13th, 2018 Villa Salvati, Pianello Vallesina, Monte Roberto, Ancona, Italy
http://laral.istc.cnr.it/saga
Why swarms for PA? • Parallelise operations ➝ higher efficiency • Collaborative monitoring ➝ higher accuracy • Redundant systems ➝ higher robustness • Decentralised algorithms ➝ higher scalability (group/farm size)
The SAGA project Hardware Onboard Enhancement Vision Swarm-level Control
UAV hardware • UAV based on the Avular Curiosity platform payload of 1kg, 10’ flight time • RTK-GPS, double IMU • double real time control cores (Cortex M4F) • • Enhanced for swarm operations UWB positioning and communication • 2.4GHz XBee radio link • Raspberry Pi RGB camera for onboard vision • and high-level control http://www.avular.com
Onboard weed recognition altitude: 3m altitude: 10m
Onboard weed recognition altitude: 3m altitude: 10m
SAGA in a nutshell Hardware enables: Onboard vision enables: • communication among UAVs • low-altitude weed classification • high-level control and • high-altitude density estimation onboard vision Swarm-level control: • collaborative weed mapping • decentralised UAV deployment
Collaborative Weed Mapping
Collaborative Weed Mapping • Full coverage of a cultivated field to inspect for weeds • Collaboratively map weed presence minimising classification errors • Aim at robustness , efficiency and scalability • Deal with environmental heterogeneities • Proposed solution: reinforced random walks (RRW) • Comparison with optimal ‘ sweeping ’ strategy Albani, D., Nardi, D., & Trianni, V. (2017). Field Coverage and Weed Mapping by UAV Swarms To be presented at the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017), Vancouver, Canada, Sept. 2017
RRW for Field Coverage • The field is divided into cells to be visited • Agents perform a correlated random walk Random selection among those cells • that are closer and not yet visited Preferential choice of forward semi-plane • p ∈ [0 , 1[ Persistence controlled by parameter • • Neighbour agents repel each other σ a ∈ [0 , 50] Repulsion controlled by parameter • Provision of an additional directional bias •
RRW for Field Coverage • Without repulsion among agents, high persistence leads to lower coverage time • Small groups are not strongly affected by repulsion • The larger the agent density, the stronger the repulsion • Persistence is detrimental for large densities and strong repulsion
RRW for Weed Mapping • Introduction of communication with limited range Range controlled by • R c ∈ [5 , ∞ [ parameter • Tests performed with varying re-broadcasting protocols • Agents can place “beacons” to attract other agents Attraction controlled by • σ b ∈ [4 , 32] parameter Weed mapping efficiency increases •
Decentralised UAV deployment
Decentralised UAV deployment • Onboard vision and autonomous control allow for non-uniform coverage
Decentralised UAV deployment • Onboard vision and autonomous control allow for non-uniform coverage High-altitude estimation of weed density •
Decentralised UAV deployment • Onboard vision and autonomous control allow for non-uniform coverage High-altitude estimation of weed density • Low-altitude collaborative weed mapping •
Decentralised UAV deployment • Onboard vision and autonomous control allow for non-uniform coverage High-altitude estimation of weed density • Low-altitude collaborative weed mapping • • Attention should be focused only to those areas that contain weed patches
Decentralised UAV deployment • Onboard vision and autonomous control allow for non-uniform coverage High-altitude estimation of weed density • Low-altitude collaborative weed mapping • • Attention should be focused only to those areas that contain weed patches • The problem translates to utility-dependent UAV deployment
Decentralised UAV deployment • Onboard vision and autonomous control allow for non-uniform coverage High-altitude estimation of weed density • Low-altitude collaborative weed mapping • • Attention should be focused only to those areas that contain weed patches • The problem translates to utility-dependent UAV deployment • Solution exploits a design-pattern for decentralised collective decision making
Decentralised UAV deployment • UAVs explore and estimate the utility of areas during high-altitude/low-resolution inspection • UAVs perform low-altitude/high-resolution inspection only for high-utility areas • The utility of areas varies through time as a function of the mapping effort • UAVs get recruited to areas of high utility • UAVs are inhibited from monitoring areas when other areas of high utility need attention (cross-inhibition) • there are too many teammates (self-inhibition) • Albani, D., Manoni, T., Nardi, D., & Trianni, V. (2018). Dynamic UAV Swarm Deployment for Non-Uniform Coverage (pp. 1–9). Presented at the AAMAS '18: Proceedings of the 2018 International Conference on Autonomous Agents and Multiagent Systems.
uncommitted: deployed to an area: • high-altitude inspection • low-altitude mapping • estimate area utility • recruit/inhibit teammates U A C B deployment: abandonment: • spontaneous (utility-driven) • spontaneous (task completed) • interactive (recruitment) • interactive (inhibition)
Models and Simulations • We study a model of area utility dynamics subject to collaborative mapping • We identify optimal parameterisations for area inspection depending on UAV collaboration and potential interferences We determine the optimal number N ★ of agents for efficient monitoring • • We study a coupled model of deployment and utility dynamics • We translate model prescriptions into a multi-agent implementation • We introduce the ratio r between interactive and spontaneous transitions, and study its effects on deployment
N ★ N ★ N ★ N ★ Time (s) Time (s)
Summing up • Collaborative field monitoring and mapping provides parallel operation (efficiency) and collaboration (accuracy) • robustness and scalability: group size can vary in real time • • Decentralised deployment and re-deployment provides ability to focus only on areas of high interest • ability to enforce utility-responsive strategies •
Beyond SAGA • Swarm robotics is a promising approach for the agricultural sector Extensive field tests to support the concept • Determine the legal and economic framework that make • swarm solutions profitable • Collaborative perception to improve detection accuracy beyond simple scenarios Exploit perception at different time and from different perspectives • Determine optimal strategies for information foraging to maximise accuracy • • Collaboration between UAVs and ground rovers forming a heterogeneous swarm
Thanks for your attention
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