Autonomous and Safe Navigation of Mobile Robots in Vineyard with Smooth Collision Avoidance

Ravankar, Abhijeet; Ravankar, Ankit A.; Rawankar, Arpit; Hoshino, Yohei

doi:10.3390/agriculture11100954

Cited by 21 publications

(8 citation statements)

References 41 publications

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“…The algorithm enables safe robot navigation in vineyards by deftly handling dynamic obstacles, such as moving people, and ensuring that the robot's positions remain smooth without sudden stops or sharp turns. Tests in both simulations and actual vineyards have confirmed its efficiency, suggesting its potential for smooth integration into vineyard robots [59]. Machine learning and computer vision can also be combined to enable autonomous navigation of vineyards using only camera sensors.…”

mentioning

confidence: 88%

WiCHORD+: A Scalable, Sustainable, and P2P Chord-Based Ecosystem for Smart Agriculture Applications

Balatsouras,

Karras,

Karras

et al. 2023

Sensors

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In the evolving landscape of Industry 4.0, the convergence of peer-to-peer (P2P) systems, LoRa-enabled wireless sensor networks (WSNs), and distributed hash tables (DHTs) represents a major advancement that enhances sustainability in the modern agriculture framework and its applications. In this study, we propose a P2P Chord-based ecosystem for sustainable and smart agriculture applications, inspired by the inner workings of the Chord protocol. The node-centric approach of WiCHORD+ is a standout feature, streamlining operations in WSNs and leading to more energy-efficient and straightforward system interactions. Instead of traditional key-centric methods, WiCHORD+ is a node-centric protocol that is compatible with the inherent characteristics of WSNs. This unique design integrates seamlessly with distributed hash tables (DHTs), providing an efficient mechanism to locate nodes and ensure robust data retrieval while reducing energy consumption. Additionally, by utilizing the MAC address of each node in data routing, WiCHORD+ offers a more direct and efficient data lookup mechanism, essential for the timely and energy-efficient operation of WSNs. While the increasing dependence of smart agriculture on cloud computing environments for data storage and machine learning techniques for real-time prediction and analytics continues, frameworks like the proposed WiCHORD+ appear promising for future IoT applications due to their compatibility with modern devices and peripherals. Ultimately, the proposed approach aims to effectively incorporate LoRa, WSNs, DHTs, cloud computing, and machine learning, by providing practical solutions to the ongoing challenges in the current smart agriculture landscape and IoT applications.

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mentioning

confidence: 88%

WiCHORD+: A Scalable, Sustainable, and P2P Chord-Based Ecosystem for Smart Agriculture Applications

Balatsouras,

Karras,

Karras

et al. 2023

Sensors

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show abstract

“…The problem they solve is the difficulty of labeling training data for AI algorithms. Similarly, references [ 31 , 32 , 33 , 34 , 35 ] are about environmental classification, not navigation.…”

Section: Related Workmentioning

confidence: 99%

Hyperspectral Imaging for Mobile Robot Navigation

Jakubczyk

Siemiątkowska

Więckowski

et al. 2022

Sensors

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The article presents the application of a hyperspectral camera in mobile robot navigation. Hyperspectral cameras are imaging systems that can capture a wide range of electromagnetic spectra. This feature allows them to detect a broader range of colors and features than traditional cameras and to perceive the environment more accurately. Several surface types, such as mud, can be challenging to detect using an RGB camera. In our system, the hyperspectral camera is used for ground recognition (e.g., grass, bumpy road, asphalt). Traditional global path planning methods take the shortest path length as the optimization objective. We propose an improved A* algorithm to generate the collision-free path. Semantic information makes it possible to plan a feasible and safe path in a complex off-road environment, taking traveling time as the optimization objective. We presented the results of the experiments for data collected in a natural environment. An important novelty of this paper is using a modified nearest neighbor method for hyperspectral data analysis and then using the data for path planning tasks in the same work. Using the nearest neighbor method allows us to adjust the robotic system much faster than using neural networks. As our system is continuously evolving, we intend to examine the performance of the vehicle on various road surfaces, which is why we sought to create a classification system that does not require a prolonged learning process. In our paper, we aimed to demonstrate that the incorporation of a hyperspectral camera can not only enhance route planning but also aid in the determination of parameters such as speed and acceleration.

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“…Thus, light detection and ranging (LiDAR), inertial measurement unit (IMU), and Global Navigation Satellite System (GNSS) devices have been used to assess proximities during motion, especially in autonomous driving fields [10,11]. Some studies [12][13][14] have proposed collision avoidance methods using these sensors and their combinations. Additionally, machine-learning pathplanning techniques have been investigated for collision avoidance [15].…”

Section: Introductionmentioning

confidence: 99%

A robust collision prediction and detection method based on neural network for autonomous delivery robots

Seo

Jung

2023

ETRI Journal

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For safe last-mile autonomous robot delivery services in complex environments, rapid and accurate collision prediction and detection is vital. This study proposes a suitable neural network model that relies on multiple navigation sensors. A light detection and ranging technique is used to measure the relative distances to potential collision obstacles along the robot's path of motion, and an accelerometer is used to detect impacts. The proposed method tightly couples relative distance and acceleration time-series data in a complementary fashion to minimize errors. A long short-term memory, fully connected layer, and SoftMax function are integrated to train and classify the rapidly changing collision countermeasure state during robot motion. Simulation results show that the proposed method effectively performs collision prediction and detection for various obstacles.

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Autonomous and Safe Navigation of Mobile Robots in Vineyard with Smooth Collision Avoidance

Cited by 21 publications

References 41 publications

WiCHORD+: A Scalable, Sustainable, and P2P Chord-Based Ecosystem for Smart Agriculture Applications

WiCHORD+: A Scalable, Sustainable, and P2P Chord-Based Ecosystem for Smart Agriculture Applications

Hyperspectral Imaging for Mobile Robot Navigation

A robust collision prediction and detection method based on neural network for autonomous delivery robots

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