Trajectory Planning with Obstacles on the Example of Tomato Harvest

Boryga, Marek; Graboś, Andrzej; Kołodziej, Paweł; Gołacki, Krzysztof; Stropek, Zbigniew

doi:10.1016/j.aaspro.2015.12.026

Cited by 13 publications

(6 citation statements)

References 9 publications

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“…Specifically, these limitations include the path generation in fields with obstacles, in fields with un-even terrain, and in capacitated operations. Although there are existing methods for the generation of such paths (e.g., field with obstacles: [24,25], three-dimensional path planning: [26,27], capacitated operations [28,29]), there are optimization processes involved meaning that the generated paths can be also near-optimal solutions and thus biasing the objectiveness of the estimation of the field efficiency. In other words, the value of the estimated FTE will depend on the methodology followed.…”

Section: Discussionmentioning

confidence: 99%

Introduction of a New Index of Field Operations Efficiency

et al. 2020

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The evaluation and prediction of the agricultural machinery field efficiency is essential for agricultural operations management. Field efficiency is affected by unpredictable (e.g., machine breakdowns) and stochastic (e.g., yield) factors, and thus, it is generally provided by average norms. However, the average values and ranges of the field efficiency are of limited value when a decision has to be made on the selection of the appropriate machinery system for a specific operational set up. To this end, in this paper, a new index for field operability, the field traversing efficiency (FTE), a distance-based measure, is introduced and a dedicated tool for estimation of this measure is presented. In order to show the degree of the dependence of the FTE index on the operational features, a number of 864 scenarios derived from the consideration of six sample field shapes, three conventional fieldwork patterns, four driving directions, and twelve combinations of machine unit kinematics and implement width were evaluated by the developed tool. The test results showed that variation of FTE was up to 23% in the tested scenarios when using different operational setups.

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Section: Discussionmentioning

confidence: 99%

Introduction of a New Index of Field Operations Efficiency

et al. 2020

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“…More specifically, the gray-level picture is obtained by the weighted average method, and the putative target is matched with the elliptic template. When the center of the elliptic template is located in the region obtained by the Otsu algorithm, this ellipse is determined to be a tomato [20] , otherwise it is not a tomato. The motion of Joint 1 and Joint 2 is planned in the two-dimensional C-space based on the critical collision angles, and that of Joint 3 is planned from the starting point to the target point according to the motion points of Joint 1.…”

Section: Four-wheel Independent Steering Chassismentioning

confidence: 99%

Development of a tomato harvesting robot used in greenhouse

Wang

Zhao

Fan

et al. 2017

International Journal of Agricultural and Biological Engineering

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A tomato harvesting robot was developed in this study, which consisted of a four-wheel independent steering system, a 5-DOF harvesting system, a navigation system, and a binocular stereo vision system. The four-wheel independent steering system was capable of providing a low-speed steering control of the robot based on Ackerman steering geometry. The proportional-integral-derivative (PID) algorithm was used in the laser navigation control system. The Otsu algorithm and the elliptic template method were used for the automatic recognition of ripe tomatoes, and obstacle avoidance strategies were proposed based on the C-space method. The maximum average absolute error between the set angle and the actual angle was about 0.14°, and the maximum standard deviation was about 0.04°. The laser navigation system was able to rapidly and accurately track the path, with the deviation being less than 8 cm. The load bearing capacity of the mechanical arm was about 1.5 kg. The success rate of the binocular vision system in the recognition of ripe tomatoes was 99.3%. When the distance was less than 600 mm, the positioning error was less than 10 mm. The time needed for recognition of ripe tomatoes and pitching was about 15 s per tomato, with a success rate of about 86%. This study provides some insights into the development and application of tomato harvesting robot used in the greenhouse.

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“…Boryga et al 13 modeled and investigated the planning rectilinear-arc polynomial trajectory (PR-APT) approach for trajectory planning of a tomato harvesting mobile manipulator by considering few kinematic constraints like displacement, velocity and acceleration. To represent a trajectory, two rectilinear curve segments had been used.…”

Section: Introductionmentioning

confidence: 99%

“…Mirzaeinejad and Shafei 16 proposed a new solution which combines some prediction-based and G-A-based approaches for trajectory modeling and tracking control of a WMR. Each method discussed in literature [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] has its own limitations. Further, the conventional optimization methods [3][4][5][6][7][8][9][10][11][12][13][14][15][16] presented in the literature are not powerful enough to solve high-complexity robot trajectory optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Algorithms-Based Multi-Objective Optimal Mobile Robot Trajectory Planning

Sathiya

Chinnadurai²

2019

Robotica

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SummaryIn this research study, trajectory planning of mobile robot is accomplished using two techniques, namely, a new variant of multi-objective differential evolution (heterogeneous multi-objective differential evolution) and popular elitist non-dominated sorting genetic algorithm (NSGA-II). For this research problem, a wheeled mobile robot with differential drive is considered. A practical, feasible and optimal trajectory between two locations in the presence of obstacles is determined through the proposed algorithms. A safer path is obtained by optimizing certain objectives (travel time and actuators effort) taking into account the limitations of mobile robot’s geometric, kinematic and dynamic parameters. Robot motion is represented by a cubic NURBS trajectory curve. The capability of the proposed optimization techniques is analyzed through numerical simulations. Results ensure that the proposed techniques are more desirable for this problem.

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Trajectory Planning with Obstacles on the Example of Tomato Harvest

Cited by 13 publications

References 9 publications

Introduction of a New Index of Field Operations Efficiency

Introduction of a New Index of Field Operations Efficiency

Development of a tomato harvesting robot used in greenhouse

Evolutionary Algorithms-Based Multi-Objective Optimal Mobile Robot Trajectory Planning

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