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

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

doi:10.3390/agriculture13020354

Cited by 31 publications

(15 citation statements)

References 73 publications

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“…For example, in [12] the chosen discount factor is close to 1. Li et al [57] and Hu and Wang [53] chose a value of 0.9, and Castro et al [66] and Panetsos et al [60] chose a discount factor value of 0.99.…”

Section: Value-function-based Algorithmsmentioning

confidence: 99%

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A Survey of Offline- and Online-Learning-Based Algorithms for Multirotor Uavs

Sönmez,

Rutherford,

Valavanis

2024

Drones

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Multirotor UAVs are used for a wide spectrum of civilian and public domain applications. Their navigation controllers include onboard sensor suites that facilitate safe, autonomous or semi-autonomous multirotor flight, operation, and functionality under nominal and detrimental conditions and external disturbances, even when flying in uncertain and dynamically changing environments. During the last decade, given the available computational power, different learning-based algorithms have been derived, implemented, and tested to navigate and control, among other systems, multirotor UAVs. Learning algorithms have been and are used to derive data-driven based models, to identify parameters, to track objects, to develop navigation controllers, and to learn the environments in which multirotors operate. Learning algorithms combined with model-based control techniques have proven beneficial when applied to multirotors. This survey summarizes the research published since 2015, dividing algorithms, techniques, and methodologies into offline and online learning categories and then further classifying them into machine learning, deep learning, and reinforcement learning sub-categories. An integral part and focus of this survey is on online learning algorithms as applied to multirotors, with the aim to register the type of learning techniques that are either hard or almost hard real-time implementable, as well as to understand what information is learned, why, how, and how fast. The outcome of the survey offers a clear understanding of the recent state of the art and of the type and kind of learning-based algorithms that may be implemented, tested, and executed in real time.

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Section: Value-function-based Algorithmsmentioning

confidence: 99%

“…Path planning is effectively used in several areas that include precision agriculture. Castro et al [66] worked on adaptive path planning using DRL to inspect insect traps on olive trees. The proposed path planning algorithm includes two parts: the rapidly exploring random tree (RRT) algorithm and a DQN algorithm.…”

Section: Value-function-based Algorithmsmentioning

confidence: 99%

A Survey of Offline- and Online-Learning-Based Algorithms for Multirotor Uavs

Sönmez,

Rutherford,

Valavanis

2024

Drones

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“…However, the online adaptive methodology dynamically adjusts the path in real-time. This part of the methodology combines Rapidly Exploring Random Trees (RRT) and Deep Reinforcement Learning (DRL) techniques, similar to Castro et al [12]. Different from this mentioned work, the neural network model and the filters associated with the outputs are the same for the UAVs and UGV.…”

Section: Main Contributionsmentioning

confidence: 99%

“…UAVs are a promising solution applicable across various domains, including search and rescue [7,8], inspection [9], Industry 4.0 [10], and remote sensing [11], among other fields. Over the years, these robots have proven to be valuable tools for exploring complex and dynamic environments [12,13]. Their adaptability to tasks of different levels of complexity, capacity to adjust to dynamic surroundings, and agility for maneuvering make them versatile tools for different applications [14,15].…”

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

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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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“…(2020) improved the RRT algorithm to solve the path planning problem of orchard spraying robots. Castro et al. (2023) proposed an online adaptive path planning solution fusing RRT and deep reinforcement learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Improving path planning for mobile robots in complex orchard environments: the continuous bidirectional Quick-RRT* algorithm

Ye,

Li,

2024

Front. Plant Sci.

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Efficient obstacle-avoidance path planning is critical for orchards with numerous irregular obstacles. This paper presents a continuous bidirectional Quick-RRT* (CBQ-RRT*) algorithm based on the bidirectional RRT (Bi-RRT) and Quick-RRT* algorithms and proposes an expansion cost function that evaluates path smoothness and length to overcome the limitations of the Quick-RRT* algorithm for non-holonomic mobile robot applications. To improve the zigzag between dual trees caused by the dual-tree expansion of the Bi-RRT algorithm, CBQ-RRT* proposes the CreateConnectNode optimization method, which effectively solves the path smoothness problem at the junction of dual trees. Simulations conducted on the ROS platform showed that the CBQ-RRT* outperformed the unidirectional Quick-RRT* in terms of efficiency for various orchard layouts and terrain conditions. Compared to Bi-RRT*, CBQ-RRT* reduced the average path length and maximum heading angle by 8.5% and 21.7%, respectively. In addition, field tests confirmed the superior performance of the CBQ-RRT*, as evidenced by an average maximum path lateral error of 0.334 m, a significant improvement over Bi-RRT* and Quick-RRT*. These improvements demonstrate the effectiveness of the CBQ-RRT* in complex orchard environments.

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

Cited by 31 publications

References 73 publications

A Survey of Offline- and Online-Learning-Based Algorithms for Multirotor Uavs

A Survey of Offline- and Online-Learning-Based Algorithms for Multirotor Uavs

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

Improving path planning for mobile robots in complex orchard environments: the continuous bidirectional Quick-RRT* algorithm

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