Cooperative Heterogeneous Robots for Autonomous Insects Trap Monitoring System in a Precision Agriculture Scenario

Berger, Guido S.; Teixeira, Marco Antônio; Cantieri, Álvaro Rogério; Lima, José; Pereira, Ana I.; Valente, António; Castro, Gabriel G. R. de; Pinto, Milena F.

doi:10.3390/agriculture13020239

Cited by 17 publications

(19 citation statements)

References 70 publications

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“…This work is part of a larger project that will be implemented in a similar olive-grown culture, but using heterogeneous collaborative robots to perform inspections. Another part of this main project has already been published in [74]. The present work represents a solution to some of the future investigations suggested in [74].…”

Section: Discussionmentioning

confidence: 80%

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

et al. 2023

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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 m3 and with 10 dynamic objects.

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Section: Discussionmentioning

confidence: 80%

“…Another part of this main project has already been published in [74]. The present work represents a solution to some of the future investigations suggested in [74].…”

Section: Discussionmentioning

confidence: 80%

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

et al. 2023

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“…Multiple unmanned systems, through the collaboration of multiple intelligent agents, can accomplish complex tasks that a single intelligent agent cannot handle alone. They have a wide range of applications in military [5], search and rescue [6], agriculture [7], and other fields. Most unmanned systems consist of the same type of intelligent agents, with individual functions that limit the system's capabilities.…”

Section: Introductionmentioning

confidence: 99%

IMUC: Edge–End–Cloud Integrated Multi-Unmanned System Payload Management and Computing Platform

Tang,

Zhong,

Zhang

et al. 2024

Drones

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Multi-unmanned systems are primarily composed of unmanned vehicles, drones, and multi-legged robots, among other unmanned robotic devices. By integrating and coordinating the operation of these robotic devices, it is possible to achieve collaborative multitasking and autonomous operations in various environments. In the field of surveying and mapping, the traditional single-type unmanned device data collection mode is no longer sufficient to meet the data acquisition tasks in complex spatial scenarios (such as low-altitude, surface, indoor, underground, etc.). Faced with the data collection requirements in complex spaces, employing different types of robots for collaborative operations is an important means to improve operational efficiency. Additionally, the limited computational and storage capabilities of unmanned systems themselves pose significant challenges to multi-unmanned systems. Therefore, this paper designs an edge–end–cloud integrated multi-unmanned system payload management and computing platform (IMUC) that combines edge, end, and cloud computing. By utilizing the immense computational power and storage resources of the cloud, the platform enables cloud-based online task management and data acquisition visualization for multi-unmanned systems. The platform addresses the high complexity of task execution in various scenarios by considering factors such as space, time, and task completion. It performs data collection tasks at the end terminal, optimizes processing at the edge, and finally transmits the data to the cloud for visualization. The platform seamlessly integrates edge computing, terminal devices, and cloud resources, achieving efficient resource utilization and distributed execution of computing tasks. Test results demonstrate that the platform can successfully complete the entire process of payload management and computation for multi-unmanned systems in complex scenarios. The platform exhibits low response time and produces normal routing results, greatly enhancing operational efficiency in the field. These test results validate the practicality and reliability of the platform, providing a new approach for efficient operations of multi-unmanned systems in surveying and mapping requirements, combining cloud computing with the construction of smart cities.

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“…In the literature, several reports have surveyed strategies for the cooperation of heterogeneous robots [1,2]. One intriguing interaction between heterogeneous robots is a UAV landing on an Unmanned Ground Vehicle (UGV), where the UGV is in motion, requiring the UAV to adjust its velocity to reach the landing spot dynamically [3].…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Multi-Robot Collaboration for Coverage Path Planning in Partially Known Dynamic Environments

de Castro,

Santos,

Andrade

et al. 2024

Machines

Self Cite

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This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots.

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Cooperative Heterogeneous Robots for Autonomous Insects Trap Monitoring System in a Precision Agriculture Scenario

Cited by 17 publications

References 70 publications

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

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

IMUC: Edge–End–Cloud Integrated Multi-Unmanned System Payload Management and Computing Platform

Heterogeneous Multi-Robot Collaboration for Coverage Path Planning in Partially Known Dynamic Environments

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