Adaptive Metaheuristic-Based Methods for Autonomous Robot Path Planning: Sustainable Agricultural Applications
| Autor: | Farzad Kiani, Amir Seyyedabbasi, Sajjad Nematzadeh, Fuat Candan, Taner Çevik, Fateme Aysin Anka, Giovanni Randazzo, Stefania Lanza, Anselme Muzirafuti |
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| Přispěvatelé: | İstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü, Farzad Kiani / 0000-0002-0354-9344, Amir Seyyedabbasi / 0000-0001-5186-4499, Kiani, Farzad, Anka, Fateme Aysin, Seyyedabbasi, Amir, Amir Seyyedabbasi / 57202833910, Farzad Kiani / 36662461100 |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Technology
smart agriculture QH301-705.5 QC1-999 THINGS drone photogrammetry autonomous robots remote sensing smart agriculture climate change environmental protection drone photogrammetry path planning internet of things environmental monitoring UNMANNED AERIAL VEHICLES remote sensing General Materials Science Biology (General) OPTIMIZATION INTERNET Instrumentation path planning QD1-999 environmental protection environmental monitoring Fluid Flow and Transfer Processes Process Chemistry and Technology Physics General Engineering autonomous robots climate change internet of things Engineering (General). Civil engineering (General) Computer Science Applications Chemistry TA1-2040 |
| Zdroj: | Applied Sciences; Volume 12; Issue 3; Pages: 943 Applied Sciences, Vol 12, Iss 943, p 943 (2022) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app12030943 |
| Popis: | The increasing need for food in recent years means that environmental protection and sustainable agriculture are necessary. For this, smart agricultural systems and autonomous robots have become widespread. One of the most significant and persistent problems related to robots is 3D path planning, which is an NP-hard problem, for mobile robots. In this paper, efficient methods are proposed by two metaheuristic algorithms (Incremental Gray Wolf Optimization (I-GWO) and Expanded Gray Wolf Optimization (Ex-GWO)). The proposed methods try to find collision-free optimal paths between two points for robots without human intervention in an acceptable time with the lowest process costs and efficient use of resources in large-scale and crowded farmlands. Thanks to the methods proposed in this study, various tasks such as tracking crops can be performed efficiently by autonomous robots. The simulations are carried out using three methods, and the obtained results are compared with each other and analyzed. The relevant results show that in the proposed methods, the mobile robots avoid the obstacles successfully and obtain the optimal path cost from source to destination. According to the simulation results, the proposed method based on the Ex-GWO algorithm has a better success rate of 55.56% in optimal path cost. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. WOS:000755331700001 Q2 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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