Fast Return Path Planning for Agricultural Autonomous Terrestrial Robot in a Known Field
The agricultural sector is becoming more critical than ever in view of the expected overpopulation of the Earth. The introduction of robotic solutions in this field is an increasingly researched topic to make the most of the Earth's resources, thus going to avoid the problems of wear and tear of the human body due to the harsh agricultural work, and open the possibility of a constant careful processing 24 hours a day. This project is realized for a terrestrial autonomous robot aimed to navigate in an orchard collecting fallen peaches below the trees. When it receives the signal indicating the low battery, it has to return to the docking station where it will replace its battery and then return to the last work point and resume its routine. Considering a preset path in orchards with tree rows with variable length by which the robot goes iteratively using the algorithm D*. In case of low battery, the D* algorithm is still used to determine the fastest return path to the docking station as well as to come back from the docking station to the last work point. MATLAB simulations were performed to analyze the flexibility and adaptability of the developed algorithm. The simulation results show an enormous potential for adaptability, particularly in view of the irregularity of orchard field, since it is not flat and undergoes modifications over time from fallen branch as well as from other obstacles and constraints. The D* algorithm determines the best route in spite of the irregularity of the terrain. Moreover, in this work, it will be shown a possible solution to improve the initial points tracking and reduce time between movements.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2571817Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 262
 P. Bhattacharya and M. L. Gavrilova. “Roadmap-based path planning and Using the voronoi diagram for a clearance-based shortest path. Robotics & Automation Magazine”, pp. 58-66, 2008.
 A. Bechar, C. Vigneault, “Agricultural robots for field operations: Concepts and components”, Biosystems Engineering, 149, pp. 94-111, 2016.
 I. A. Hameed, A. la Cour-Harbo and O. L. Osen. “Side-to-side 3D coverage path planning approach for agricultural robots to minimize skip/overlap areas between swaths”, Robotics and Autonomous Systems, 76, pp. 36-45, 2016.
 Y. Nagasaka, N. Umeda, Y. Kanetai, K. Taniwaki and Y. Sasaki. “Autonomous guidance for rice transplanting using global positioning and gyroscopes”, Computers and Electronics in Agriculture, 43(3), pp. 223-234, 2004.
 B. Thuilot, C. Cariou, P. Martinet, and M. Berducat, “Automatic guidance of a farm tractor relying on a single CP-DGPS”, Autonomous Robots, 13(1), pp. 53-71, 2002.
 J. M. Bengochea-Guevara, J. Conesa-Mu~noz, D. Andu´ jar and A. Ribeiro. “Merge fuzzy visual servoing and GPS-based planning to obtain a proper navigation behavior for a small crop-inspection robot”, Sensors (Basel), 16(3), 2016.
 Z. Zhong-Xiang, C. Jun, Y. Toyofumi, T. Ryo, S. Zheng-he, M. En-rong, “Path tracking control of autonomous agricultural mobile robots”, Journal of Zhejiang University-SCIENCE A, 8(10), pp. 1596–1603, 2007.
 H. Mousazadeh, “A technical review on navigation systems of agricultural autonomous off-road vehicles”, Journal of Terramechanics, 50(3), pp. 211-232, 2013.
 Q. Zhang and H. Qiu, “A dynamic path search algorithm for tractor automatic navigation”, Transactions of ASAE, 47, pp. 639–46, 2004.
 M. Kise, N. Noguchi, K. Ishii and H. Terao “Enhancement of turning accuracy by path planning for robot tractor”, in Proc. of the Automation Technology for Off-Road Equipment Conference, Chicago, Illinois, USA, pp. 398–404, July 26-27, 2002.
 M. Kise, N. Noguchi, K. Ishii, H. Terao, “The development of the autonomous tractor with steering controller applied by optimal control”, in Proc. of the Automation Technology for Off-Road Equipment Conference, Chicago, Illinois, USA, pp. 367–73, July 26-27, 2002.
 T. Korthals, M. Kragh, P. Christiansen, H. Karstoft, R. N. Jørgensen and U. Rückert, “Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation”, Frontiers in Robot and AI, pp. 5-6, 2018.
 R. Mannadiar and I. Rekleitis, “Optimal Coverage of a Known Arbitrary Environment”, in Proc. of 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, USA, 3-7 May, 2010.
 M. Rekleitis, A. P. New, E. S. Rankin, and H. Choset, “Efficient Boustrophedon Multi-Robot Coverage: an algorithmic approach”, Annals of Mathematics and Artificial Intelligence, 52(2–4), pp 109–142, 2008.
 Peter Corke, “Robotics, Vision and Control - Fundamental Algorithms in MATLAB®”, Springer Tracts in Advanced Robotics Springer, 2011.
 W. Zeng, R. L. Church, "Finding shortest paths on real road networks: the case for A*", International Journal of Geographical Information Science, 23(4), pp. 531–543, 2009.
 G. Song, H. Wang, J. Zhang, and T. Meng, “Automatic docking system for recharging home surveillance robots”, IEEE Transactions on Consumer Electronics, 57(2), pp. 431-433, 2011.