Abstract
Coverage path planning (CPP) is a fundamental agricultural field task required for autonomous navigation systems. It is also important for resource management, increasingly demanding in terms of reducing costs and environmental polluting agents as well as increasing productivity. Additional problems arise when this task involves irregular agricultural terrains where the crop follows non-uniform configurations and extends over steep rocky slopes. For mountain vineyards, finding the optimal path to cover a restricted set of terraces, some of them with dead ends and with other constraints due to terrain morphology, is a great challenge. The problem involves other variables to be taken into account such as speed, direction and orientation of the vehicle, fuel consumption and tank capacities for chemical products. This article presents a decision graph-based approach, to solve a Rural Postman Coverage like problem using A* and Dijkstra algorithms simultaneously to find the optimal sequence of terraces that defines a selected partial coverage area of the vineyard. The decision structure is supported by a graph that contains all the information of the Digital Terrain Model (DTM) of the vineyard. In this first approach, optimality considers distance, cost and time requirements. The optimal solution was represented in a graphical user OpenGL application developed to support the path planning process. Based on the results, it was possible to prove the applicability of this approach for any vineyards which extend like routes. Near optimal solutions based on other specific criteria could also be considered for future work.
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References
Galaceran, E., Carreras, M.: A Survey on coverage path planning for robotics. Robotics and Autonomous Systems 61(12), 1258–1276 (2013)
Oksanen, T., Visala, A.: Coverage path planning algorithm for agricultural field machines. Journal of Field Robotics 26(8), 651–668 (2009)
Andresen, T., Bianchi, F., Curado, M.J.: The Alto Douro Wine Region Geeenway. Landscape and Urban Planning 68(2–3), 289–303 (2004)
Contente, O., Aranha, J., Martinho, J., Ferreira, P., Lau, N., Morais, R.: 3D digital maps for vineyard autonomous robot navigation. In: Advances in Artificial Intelligence - Proceedings EPIA- XVI Portuguese Conference on Artificial Intelligence (2009)
Contente, O., Aranha, J., Martinho, J., Morgado, J., Reis, M., Ferreira, P., Morais, R., Lau, N.: 3D map and DGPS validation for a vineyard autonomous navigation system. In: CONTROLO 2014 - Proc. of the 11th Port. Conf. on Autom. Control. LNEE, vol. 321, pp. 617–625. Springer, Heidelberg (2015)
Contente, O., Lau, N., Morgado J., Morais, R.: Vineyard skeletonization for autonomous robot navigation. In: 2015 IEEE International Conference on Autonomous Robot Systems and Competition (ICARSC), pp. 50–55 (2015)
Choset, H.: Coverage for Robotics a Survey of Recent Results. Annals of Mathematics and Artificial Intelligence 31(1–4), 113–126 (2001)
Yang, S.X., Luo, C.: A Neural Network Approach to Complete Coverage Path Planning. IEEE Transactions on System, Man and Cybernetics, Part B: Cybernetics 34(1), 718–724 (2004)
Hameed, I.A., Bochtis, D., Sorensen, C.A.: An Optimized Field Coverage Planning Approach for Navigation of Agricultural Robots in Fields Involving Obstacle Areas. International Journal of Advanced Robotic Systems 10(231), 1–9 (2013)
Hameed, I.A.: Intelligent Coverage Path Planning for Agricultural Robots and Autonomous Machines on Three-Dimensional Terrain. Journal of Intelligent and Robotic Systems 74(3–4), 965–983 (2014)
Zhou, K., Jensena, A.L., Srensena, C.G., Busatob, P., Bothtisa, D.D.: Agricultural operations planning in fileds with multiple obstacle areas. Computers and Electronics in Agriculture 109, 12–22 (2014)
Noguchi, N., Terao, H.: Path planning of an agricultural mobile robot by neural network and genetic algorithm. Computers and Electronics in Agriculture 18(2–3), 187–204 (1997)
Garcia, M.A., Montiela, O., Castillo, O., Seplveda, R., Melinb, P.: Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation. Applied Soft Computing 9(3), 1102–1110 (2009)
Bochtis, D.D., Vougioukas, S.G., Griepentrog, H.W.: A Mission Planner for an Autonomous Tractor. Transactions of the ASABE 52(5), 1429–1440 (2009)
Ryerson, A.E.F., Zhang, Q.: Vehicle Path Planning for Complete Field Coverage Using Genetic Algorithms. Agricultural Engineering International: the CIGR E-Journal, 9 (2007)
Bochtis, D.D.: Planning and control of a fleet of agricultural machines for optimal management of field operations. Greece: Aristotle University. PH. D. Thesis (2008)
Bochtis, D.D., Vougioukas, S.G.: Minimising the non-working distance travelled by machines operating in a headland field pattern. Biosystems Engineering 101(1), 1–12 (2008)
Bakhtiari, A.A., Navid, H., Mehri, J., Bochtis, D.D: Optimal route planning of agricultural field operations using ant colony optimization. Agricultural Engineering International: CIGR Journal 13(4), 1–16 (2011)
Conesa-Muoz, J., Bengochea-Guevara, J.M., Andujar, D., Ribeiro, A.: Efficient distribution of a fleet of heterogeneous vehicles in agriculture: a practical approach to multi-path planning. In: 2015 IEEE International Conference on Autonomous Robot Systems and Competition (ICARS), pp. 56–61 (2015)
Sedgewick, R.: Permutation Generation Methods. ACM Computing Surveys 9(2), 137–164 (1997)
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Contente, O., Lau, N., Morgado, F., Morais, R. (2016). A Path Planning Application for a Mountain Vineyard Autonomous Robot. In: Reis, L., Moreira, A., Lima, P., Montano, L., Muñoz-Martinez, V. (eds) Robot 2015: Second Iberian Robotics Conference. Advances in Intelligent Systems and Computing, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-319-27146-0_27
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DOI: https://doi.org/10.1007/978-3-319-27146-0_27
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