A collaborative control protocol for agricultural robot routing with online adaptation

Dusadeerungsikul, Puwadol Oak; Nof, Shimon Y.

doi:10.1016/j.cie.2019.06.037

Cited by 32 publications

(10 citation statements)

References 24 publications

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“…The protocol of joint routing management of the robotic platform with online adaptation can be formed according to the methodology proposed by Dusadeerungsikul and Nof (2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Robotic Platform Concept for Rapid Soil Monitoring

Bykov¹,

Berezhnov²,

Leonov³

2020

Modern S&T Equipments and Problems in Agriculture

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This article presents the substantiation of application of a robotic platform for express monitoring of the main physical-chemical and agrochemical indicators of soils in field conditions. The concept of the robotic platform provides for its movement in the field along the trajectory given by the IV International Scientific and Practical Conference "Modern S&T Equipments and Problems in Agriculture" 29 program, collection of soil samples and immersion of probes with sensors in them. The main advantage of the proposed method is its high efficiency at low economic costs. Due to the large number of measurements per unit area of the field the estimation accuracy is quite high. At the same time, for one point of the field it is possible to make measurements in different soil horizons. The conceptual scheme of the robotic platform is offered, energy calculations are performed, specific brands of sensors and other devices are selected. The presented materials are the basis for designing, manufacturing and operation of this automated vehicle. The concept of robotic platform operation corresponds to modern digital technologies and precision farming system.

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“…The protocol of joint routing management of the robotic platform with online adaptation can be formed according to the methodology proposed by Dusadeerungsikul and Nof (2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Robotic Platform Concept for Rapid Soil Monitoring

Bykov¹,

Berezhnov²,

Leonov³

2020

Modern S&T Equipments and Problems in Agriculture

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“…To date, many different robots have been developed and/or deployed in both greenhouse environments (Arad et al, 2020; Mahmud et al, 2019; Schor et al, 2016; van Henten et al, 2002; Xiong et al, 2020) and field conditions (Åstrand & Baerveldt, 2002; Birrell et al, 2020; Nagasaka et al, 2009; Underwood et al, 2017). Growers are enthusiastic about using robotic technologies to reduce labor costs and increase profit margins in harvesting (Arad et al, 2020; Birrell et al, 2020; van Henten et al, 2002; Xiong et al, 2020), phenotyping (Narvaez et al, 2017; Underwood et al, 2017), transplanting (Nagasaka et al, 2009), weed control (Åstrand & Baerveldt, 2002), disease detection (Dusadeerungsikul & Nof, 2019; Schor et al, 2016), and so on.…”

Section: Introductionmentioning

confidence: 99%

Minimum‐time row transition control of a vision‐guided agricultural robot

2021

Journal of Field Robotics

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This paper presents a vision‐based, subspace optimal controller aiming to improve the row transition performance of an agricultural robot in a strawberry field. The contribution of this paper is twofold. First, only RGB cameras, instead of complicated sensor suites, are used for cross‐bed navigation and row alignment. Second, a real‐time adaptive dynamic programming‐based algorithm is designed for an optimal row transition. The conditions for row alignment are derived in an augmented pixel coordinate frame. Based on these conditions, a simple motion rule is utilized to reduce the search space dimension so that the proposed algorithm can be implemented in real‐time. Additionally, the inverse‐dynamics policy of the algorithm is updated using vision feedback at each control step to adapt to uncertainties. The proposed controller is tested in both simulations and field experiments. In a simulation comparison, the minimum‐time solution achieved using the proposed algorithm is 44.7 s, which is very close to that of a benchmark algorithm (44.4 s). However, the CPU time required by the proposed algorithm is only 4.3% of time needed by the benchmark algorithm. Twenty field experiments using the presented design were all successful in row transition, with a mean final alignment error of 0.5 cm.

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“…Stressed areas in the field 177 are first identified and, then, the shortest path for traversing 178 them is defined using a TSP-based solution. Similarly, the 179 identification of stressed areas is considered in [15] where 180 a TSP routing algorithm is combined with human inputs to 181 build a collaborative and adaptive framework. A TSP-based 182 walk problem, where the robot is required to repeatedly carry out a closed walk in a graph with weighted vertices.…”

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confidence: 99%

“…Based on the solution to (15), the nodes that each robot r j has to serve are determined by the variables xh, j , i.e., s i, j = 1 if edge êh terminates in v i , i.e., êh = (v s , v i ), with v i = v 0 and it is assigned to robot r j , i.e., xh, j = 1, s i, j = 0 otherwise. Concerning the route assigned to each robot r j , it is determined by the shortest paths associated with the variables xh, j = 1, i.e., x k, j = 1 with e k ∈ E if xh, j = 1 and a k,h, j = 1, x k, j = 0 otherwise.…”

mentioning

confidence: 99%

“…We can notice that the turning angle at node 814 v 1 is higher (3π/4) for the sub-optimal solution compared 815 to the optimal one (π/2), while it is the same (3π/4) at 816 node v 5 for both solutions. This is due to the fact that 817 the sub-optimal method selects the shortest paths ignoring the formulation based on the service graph in (15) with optimal cost Ĉ * , then the relative optimality gap can be bounded as follows…”

mentioning

confidence: 99%

See 1 more Smart Citation

Route Optimization in Precision Agriculture Settings: A Multi-Steiner TSP Formulation

Furchi

Lippi

Carpio

et al. 2023

IEEE Trans. Automat. Sci. Eng.

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In this work, we propose a route planning strategy 1 for heterogeneous mobile robots in Precision Agriculture (PA) 2 settings. Given a set of agricultural tasks to be performed 3 at specific locations, we formulate a multi-Steiner Traveling 4 Salesman Problem (TSP) to define the optimal assignment of 5 these tasks to the robots as well as the respective optimal paths 6 to be followed. The optimality criterion aims to minimize the total 7 time required to execute all the tasks, as well as the cumulative 8 execution times of the robots. Costs for travelling from one 9 location to another, for maneuvering and for executing the task 10 as well as limited energy capacity of the robots are considered. 11 In addition, we propose a sub-optimal formulation to mitigate the 12 computational complexity by leveraging the fact that generally 13 in PA settings only a few locations require agricultural tasks in a 14 certain period of interest compared to all possible locations in the 15 field. A formal analysis of the optimality gap between the optimal 16 and the sub-optimal formulations is provided. The effectiveness 17 of the approach is validated in a simulated orchard where three 18 heterogeneous aerial vehicles perform inspection tasks. 19 Note to Practitioners-This paper aims at providing an efficient 20 solution to PA needs by deploying a team of robots able 21 to perform agricultural tasks at given locations in large-scale 22 orchards. In particular, a novel general optimization problem is 23 proposed that, given a set of mobile and possibly heterogeneous 24 robots and a set of agricultural tasks to carry out, defines the 25 assignment of these tasks to the robots as well as the routes to 26 follow, while minimizing the total and the cumulative execution 27 times of the robots. Existing approaches for route optimization 28 in PA generally involves complete coverage of the field by one 29 or multiple robots and do not account for maneuvering costs 30 with general layouts of the field. We consider costs for travelling 31 from one location to another, for executing the task and for 32 maneuvering without any restriction on the layout of the plants 33 as well as we take into account the limited energy capacity of the 34 robots. We also provide a sub-optimal formulation which reduces 35 the computational burden by relaxing the optimization of the 36 maneuvering costs at the locations where agricultural tasks are 37 Manuscript

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A collaborative control protocol for agricultural robot routing with online adaptation

Cited by 32 publications

References 24 publications

Robotic Platform Concept for Rapid Soil Monitoring

Robotic Platform Concept for Rapid Soil Monitoring

Minimum‐time row transition control of a vision‐guided agricultural robot

Route Optimization in Precision Agriculture Settings: A Multi-Steiner TSP Formulation

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