Field coverage and weed mapping by UAV swarms

Abstract

The demands from precision agriculture (PA) for high-quality information at the individual plant level require to re-think the approaches exploited to date for remote sensing as performed by unmanned aerial vehicles (UAVs). A swarm of collaborating UAVs may prove more efficient and economically viable compared to other solutions. To identify the merits and limitations of a swarm intelligence approach to remote sensing, we propose here a decentralised multi-agent system for a field coverage and weed mapping problem, which is efficient, intrinsically robust and scalable to different group sizes. The proposed solution is based on a reinforced random walk with inhibition of return, where the information available from other agents (UAVs) is exploited to bias the individual motion pattern. Experiments are performed to demonstrate the efficiency and scalability of the proposed approach under a variety of experimental conditions, accounting also for limited communication range and different routing protocols.

Survey on Coverage Path Planning with Unmanned Aerial Vehicles

“…Real-time search methods are considered in simulations and a Node Counting algorithm is applied in real flights by Nattero et al [36]. A reinforced random walk strategy is introduced by Albani et al [71] for field coverage and weed mapping, where the UAVs prefer to explore the areas ahead of its current position aligned with the momentum vector. A cellular automata approach is proposed by Zelenka and Kasanicky [73] and Zelenka and Kasanicky [74], but presents problems regarding the pheromone degradation and lack of evaporation.…”

“…Thus biologically-inspired approaches consisting of algorithms based on fundamental aspects of natural intelligence have emerged, such as behavioral autonomy and social interaction, evolution and learning [70]. Considering the CPP problem with aerial vehicles, several authors have explored different approaches in the literature, including real-time search methods [36], random walk [71], cellular systems [72][73][74], evolutionary computation [75,76], and swarm intelligence [77][78][79]. Coverage with uncertainty considering information points is also addressed [80][81][82][83][84][85].…”

“…An approach for field coverage and weed mapping is presented by Albani et al [71]. The proposed approach consists of an exploration strategy that uses a reinforced random walk to detect the existence and the amount of weeds using UAV swarms.…”

Remote sensing of river corridors: A review of current trends and future directions

“…UAV swarms can either be controlled using group decision making or individual agent response (Howden, ), with coverage being either “distributed” into defined zones of operation or “free” for optimum coverage through parallel decision making (San Juan, Santos, & Andujar, ). Applications for swarm mapping have included surveillance missions, search and rescue operations, weed mapping, and oil spill mapping (Albani, Nardi, & Trianni, ; Howden, ; Nigam, Bieniawski, Kroo, & Vian, ; Odonkor, Ball, & Chowdhury, ; Pitre, Li, & Delbalzo, ; San Juan et al, ). However, studies remain focussed on using simulations to test either algorithms (Almeida, Hildmann, & Solmaz, ; Chen, Ye, & Li, ; Yang, Ji, Yang, Li, & Li, ; Zhao et al, ) or data processing techniques (Casbeer, Kingston, Beard, & McLain, ; Ruiz, Caballero, & Merino, ).…”

Detecting Invasive Insects with Unmanned Aerial Vehicles

“…Similar to our proposed system of mapping insects in a field, other related research topics include aerial mapping of rice crops [22]. Similar agricultural mapping techniques involve using swarms of UAVs to map weeds in large fields [1] and terrain surveying of disjointed fields aided by UAVs and path planning [34]. Lastly, UAVs can provide a system for detection and estimation, such as using the vehicles to provide vital data on plant stress [3].…”