Coverage Control with Multiple Ground Robots for Precision Agriculture

Davoodi, Mohammadreza; Velni, Javad Mohammadpour; Li, Changying

doi:10.1115/1.2018-jun-4

Cited by 19 publications

(10 citation statements)

References 11 publications

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“…Using the result of the Voronoi partitioning, the

i

th vehicle is responsible for covering the states (associated graph vertices) in its sub‐graph

g_{r_{i}}

. Then, as explained in references [14,27], the total cost is formulated as follows:

H (p, g_{r}) = \sum_{i = 1}^{M} \sum_{q \in g_{r_{i}}} τ_{MathClass-open(s_{p_{i}}, s_{q} MathClass-close)} ϕ (q),

where

ϕ (q)

is the priority value corresponding to state

q

13 . As the field is converted into a graph, the goal states are given higher priority values and lowest values are assigned to states that are far from the important states.…”

Section: Problem Statement and Proposed Solution Methodsmentioning

confidence: 99%

“…In the very near future, a large number of farm workers will receive significant assistance from automated ground vehicles and drones 11 . There are also a number of studies focusing on multi‐robot‐based distributed coverage, path planning, and automated navigation for agricultural applications 12–14 . Although such collaboration can improve farm management and crop monitoring, the underlying systems are not fully autonomous due to the lack of a detailed map of the farm containing information of the plant rows and important areas.…”

Section: Introductionmentioning

confidence: 99%

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A reinforcement learning‐based approach for modeling and coverage of an unknown field using a team of autonomous ground vehicles

Faryadi

Velni

2020

Int J Intell Syst

Self Cite

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“…Using the result of the Voronoi partitioning, the

i

th vehicle is responsible for covering the states (associated graph vertices) in its sub‐graph

g_{r_{i}}

. Then, as explained in references [14,27], the total cost is formulated as follows:

H (p, g_{r}) = \sum_{i = 1}^{M} \sum_{q \in g_{r_{i}}} τ_{MathClass-open(s_{p_{i}}, s_{q} MathClass-close)} ϕ (q),

where

ϕ (q)

is the priority value corresponding to state

q

13 . As the field is converted into a graph, the goal states are given higher priority values and lowest values are assigned to states that are far from the important states.…”

Section: Problem Statement and Proposed Solution Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A reinforcement learning‐based approach for modeling and coverage of an unknown field using a team of autonomous ground vehicles

Faryadi

Velni

2020

Int J Intell Syst

Self Cite

View full text Add to dashboard Cite

“…Although there is a great amount of applications related to multi-robot deployment [8][9][10], many of them rely on a pre-established communication network or external localization service. This would limit the application of these systems to tasks where the existence of communication networks or GPS cannot be assumed.…”

Section: State Of the Artmentioning

confidence: 99%

“…To find this constant matrix A, we could find the matrix A n to find the n-th term. From the previous equation, the amount of existing agents in each state for a given time can be known, obtaining the expressions w(n) and b(n) from the matrix A n , which is presented in Equation (9). This equation calculates the number of agents for each state with the parameters used in the next experimental simulations (negligible collision rate of α 1 = 0 and an aggregation drone rate of α 3 = 0.01).…”

Section: Obtaining the Energy Swarm Consumptionmentioning

confidence: 99%

Energy-Efficient Swarm Behavior for Indoor UAV Ad-Hoc Network Deployment

et al. 2018

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Building an ad-hoc network in emergency situations can be crucial as a primary tool or even when used prior to subsequent operations. The use of mini and micro Unmanned Aerial Vehicles (UAVs) is increasing because of the wide range of possibilities they offer. Moreover, they have been proven to bring sustainability to many applications, such as agriculture, deforestation and wildlife conservation, among others. Therefore, creating a UAV network for an unknown environment is an important task and an active research field. In this article, a mobility model for the creation of ad-hoc networks using UAVs will be presented. This model will be based on pheromones for robust navigation. We will focus mainly on developing energy-efficient behavior, which is essential for this type of vehicle. Although there are in the literature several models of mobility for ad-hoc network creation, we find that either they are not adapted to the specific energy requirements of UAVs or the proposed motion models are unrealistic or not sufficiently robust for final implantation. We will present and analyze the operation of a distributed swarm behavior able to create an ad-hoc network. Then, an analytical model of the swarm energy consumption will be proposed. This model will provide a mechanism to effectively predict the energy consumption needed for the deployment of the network prior to its implementation. Determining the use of the mobility behavior is a requirement to establish and maintain a communication channel for the required time. Finally, this analytical model will be experimentally validated and compared to the Random Waypoint (RWP) mobility strategy.

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“…Wheeled mobile robots have been one of the most studied robotic platforms due to their applicability and motion capabilities. In the field of multi-agent systems (MAS), control schemes for collaborative wheeled robots have been developed for environmental sensing [ 1 ], task allocation for search and rescue [ 2 ], coverage control for precision agriculture [ 3 ] and object transportation [ 4 ], among others. For such applications, different control protocols have been reported in the literature, using centralized methods that depend on a global coordinate system as a first attempt, e.g., [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Formation Tracking Control and Obstacle Avoidance of Unicycle-Type Robots Guaranteeing Continuous Velocities

Martínez

Becerra

Gómez‐Gutiérrez

2021

Sensors

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In this paper, we addressed the problem of controlling the position of a group of unicycle-type robots to follow in formation a time-varying reference avoiding obstacles when needed. We propose a kinematic control scheme that, unlike existing methods, is able to simultaneously solve the both tasks involved in the problem, effectively combining control laws devoted to achieve formation tracking and obstacle avoidance. The main contributions of the paper are twofold: first, the advantages of the proposed approach are not all integrated in existing schemes, ours is fully distributed since the formulation is based on consensus including the leader as part of the formation, scalable for a large number of robots, generic to define a desired formation, and it does not require a global coordinate system or a map of the environment. Second, to the authors’ knowledge, it is the first time that a distributed formation tracking control is combined with obstacle avoidance to solve both tasks simultaneously using a hierarchical scheme, thus guaranteeing continuous robots velocities in spite of activation/deactivation of the obstacle avoidance task, and stability is proven even in the transition of tasks. The effectiveness of the approach is shown through simulations and experiments with real robots.

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Coverage Control with Multiple Ground Robots for Precision Agriculture

Cited by 19 publications

References 11 publications

A reinforcement learning‐based approach for modeling and coverage of an unknown field using a team of autonomous ground vehicles

A reinforcement learning‐based approach for modeling and coverage of an unknown field using a team of autonomous ground vehicles

Energy-Efficient Swarm Behavior for Indoor UAV Ad-Hoc Network Deployment

Formation Tracking Control and Obstacle Avoidance of Unicycle-Type Robots Guaranteeing Continuous Velocities

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