Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs

Gabriel G. R. de Castro (), Guido S. Berger, Alvaro Cantieri, Marco Teixeira, José Lima, Ana I. Pereira and Milena F. Pinto
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Gabriel G. R. de Castro: Department of Electronics Engineering, Federal Center of Technological Education of Celso Suckow da Fonseca (CEFET/RJ), Rio de Janeiro 20271-204, Brazil
Guido S. Berger: Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
Alvaro Cantieri: Applied Robotics and Computation Laboratory—LaRCA, Federal Institute of Paraná, Pinhais 3100, Brazil
Marco Teixeira: Coordenação do Curso de Engenharia de Software, COENS, Universidade Tecnológica Federal do Paraná—UTFPR, Dois Vizinhos 85660-000, Brazil
José Lima: Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
Ana I. Pereira: Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
Milena F. Pinto: Department of Electronics Engineering, Federal Center of Technological Education of Celso Suckow da Fonseca (CEFET/RJ), Rio de Janeiro 20271-204, Brazil

Agriculture, 2023, vol. 13, issue 2, 1-25

Abstract: Unmanned aerial vehicles (UAV) are a suitable solution for monitoring growing cultures due to the possibility of covering a large area and the necessity of periodic monitoring. In inspection and monitoring tasks, the UAV must find an optimal or near-optimal collision-free route given initial and target positions. In this sense, path-planning strategies are crucial, especially online path planning that can represent the robot’s operational environment or for control purposes. Therefore, this paper proposes an online adaptive path-planning solution based on the fusion of rapidly exploring random trees (RRT) and deep reinforcement learning (DRL) algorithms applied to the generation and control of the UAV autonomous trajectory during an olive-growing fly traps inspection task. The main objective of this proposal is to provide a reliable route for the UAV to reach the inspection points in the tree space to capture an image of the trap autonomously, avoiding possible obstacles present in the environment. The proposed framework was tested in a simulated environment using Gazebo and ROS. The results showed that the proposed solution accomplished the trial for environments up to 300 m 3 and with 10 dynamic objects.

Keywords: aerial robots; multiple robots; path planning; dynamic environment; precision agriculture (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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