Reactive navigation system based on H∞ control system and LiDAR readings on corn crops

Velasquez, Andrés Eduardo Baquero; Higuti, Vitor Akihiro Hisano; Guerrero, Henry Borrero; Gasparino, Mateus Valverde; Magalhães, Daniel Varela; Aroca, Rafael Vidal; Becker, Marcelo

doi:10.1007/s11119-019-09672-8

Cited by 18 publications

(12 citation statements)

References 22 publications

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“…Figure 1 b shows the Hokuyo UTM-30LX, a 2D LIDAR sensor manufactured by Hokuyo (Osaka, Japan) mounted in the mobile robot APR-03. The UTM-30-LX model is a compact (60 × 60 × 87 mm) and lightweight (210 g) LIDAR device widely used in robotic applications [ 27 ]. This indoor 2D LIDAR is composed of a fixed 905 nm semiconductor laser diode and a stepper motor to rotate a reflecting mirror that rotates the laser beam around the device which operates as a Class 1 laser device (safe under normal conditions of use).…”

Section: Methodsmentioning

confidence: 99%

Mobile Robot Self-Localization with 2D Push-Broom LIDAR in a 2D Map

Palacín

Martínez

Rubies

et al. 2020

Sensors

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This paper proposes mobile robot self-localization based on an onboard 2D push-broom (or tilted-down) LIDAR using a reference 2D map previously obtained with a 2D horizontal LIDAR. The hypothesis of this paper is that a 2D reference map created with a 2D horizontal LIDAR mounted on a mobile robot or in another mobile device can be used by another mobile robot to locate its location using the same 2D LIDAR tilted-down. The motivation to tilt-down a 2D LIDAR is the direct detection of holes or small objects placed on the ground that remain undetected for a fixed horizontal 2D LIDAR. The experimental evaluation of this hypothesis has demonstrated that self-localization with a 2D push-broom LIDAR is possible by detecting and deleting the ground and ceiling points from the scan data, and projecting the remaining scan points in the horizontal plane of the 2D reference map before applying a 2D self-location algorithm. Therefore, an onboard 2D push-broom LIDAR offers self-location and accurate ground supervision without requiring an additional motorized device to change the tilt of the LIDAR in order to get these two combined characteristics in a mobile robot.

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Section: Methodsmentioning

confidence: 99%

Mobile Robot Self-Localization with 2D Push-Broom LIDAR in a 2D Map

Palacín

Martínez

Rubies

et al. 2020

Sensors

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“…Hence, a dense and cluttered environment is expected for CSS crops, especially in later growth stages. In corn crops, some works with 2D LiDAR‐based row‐following systems can be found (Hiremath, van der Heijden, van Evert, Stein, & TerBraak, ; Troyer, Pitla & Nutter, ; Velasquez et al, ).…”

Section: Related Workmentioning

confidence: 99%

“…Velasquez et al () presents a LiDAR‐based row‐following system tested on a simulated corn crop (corn plants in pots arranged in straight rows). To generate the distances to rows, it considered a large set of the readings inside a reasonable big region of interest (ROI) without checking if such points pertained to the row.…”

Section: Related Workmentioning

confidence: 99%

Under canopy light detection and ranging‐based autonomous navigation

Higuti

Velasquez

Magalhães

et al. 2018

Journal of Field Robotics

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This paper describes a light detection and ranging (LiDAR)-based autonomous navigation system for an ultralightweight ground robot in agricultural fields. The system is designed for reliable navigation under cluttered canopies using only a 2D Hokuyo UTM-30LX LiDAR sensor as the single source for perception. Its purpose is to ensure that the robot can navigate through rows of crops without damaging the plants in narrow row-based and high-leaf-cover semistructured crop plantations, such as corn (Zea mays) and sorghum (Sorghum bicolor). The key contribution of our work is a LiDAR-based navigation algorithm capable of rejecting outlying measurements in the point cloud due to plants in adjacent rows, low-hanging leaf cover or weeds. The algorithm addresses this challenge using a set of heuristics that are designed to filter out outlying measurements in a computationally efficient manner, and linear least squares are applied to estimate within-row distance using the filtered data.Moreover, a crucial step is the estimate validation, which is achieved through a heuristic that grades and validates the fitted row-lines based on current and previous information. The proposed LiDAR-based perception subsystem has been extensively tested in production/breeding corn and sorghum fields. In such variety of highly cluttered real field environments, the robot logged more than 6 km of autonomous run in straight rows. These results demonstrate highly promising advances to LiDARbased navigation in realistic field environments for small under-canopy robots.

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“…In general, this block expects the CT E as input and outputs a desired turn rate. In hElvis III and hElvis IV, this block is either a H∞ controller using the frontal Ackermann geometry reduced to the bicycle model or a PID controller (VELASQUEZ, 2019). For TS2017 and TS2018, it is a PID controller (HIGUTI, 2018).…”

Section: Control Stagementioning

confidence: 99%

“…Table 8 summarizes the autonomous experiments, which may be part of two sets: 1) JFR and reported by External People with results published in Higuti, Velasquez, Magalhaes, Becker and Chowdhary (2018); 2) PA with results published in Velasquez, Higuti, Guerrero, Gasparino, Magalhães, Aroca and Becker (2019). Note that success rate, the measure of autonomously finishing the desired segment within crop, is around 89%.…”

Section: Perception Lidar (Lidar-only Perception Subsystem Version)mentioning

confidence: 99%

2D LiDAR-based perception for under canopy autonomous scouting of small ground robots within narrow lanes of agricultural fields

Higuti¹

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Lightweight ground robots have been increasingly showing their potential to tackle agricultural tasks that require time-consuming human labor thus limited in detail or area coverage. A task that benefits several modern agricultural practices is scouting (walking though the field). However, a reliable autonomous navigation is still a challenge. A robot needs to deal with the agricultural field dynamism and unpredictable obstacles, e.g. humans and machines. In particular, corn and sorghum present an additional issue due to the standard way of cultivation: Narrow sub-meter lanes that become even less visible on later stages due to dense coverage and spreading of leaves. This condition heavily influences the sensors by provoking frequent occlusions, misreadings and other situations out of their working range. In such context, three questions arise: 1) Can the unexplored potential of Light Detection and Ranging (LiDAR) sensor suffice to interpret a narrow lane crop environment without artificial landmarks? 2) Does the search of a best line representation for crop rows really represent the problem? 3) How can lateral distance estimation be improved through sensor fusion? To answer these three questions, Perception LiDAR (PL) has been developed to estimate lateral distance to crop rows using a 2D LiDAR as the core sensor. An Extended Kalman Filter enables the use of embedded odometry and inertial measurements. Manual ground truth has shown that the task of finding best line representation from LiDAR data is challenging, and it could have as low as 54% of line estimates within 0.05 m error. Nonetheless, the proposed method has enabled 72 km of autonomous operation of multiple robots. Due to unreliable RTK-GNSS signal under canopy, PL outputs significantly outperform the GNSS-based positioning, which may not even be in the current lane. The extensive field testing happened in multiple corn and sorghum fields between 2017 and 2020. For continuous lane, i.e. at least one of the side rows exists, the success rate to finish the desired segment of autonomous navigation is 89.76%. The higher performance for LiDAR input with significant less presence of objects other than stalks, and also the failure concentration on sensor occlusion and gaps, both situations with low to none visible rows, strongly indicates that the problem is not best line fitting, but rather a classification one where the end goal is to find stalks. In summary, although PL does not provide a fully intervention-free for within crop rows navigation, its current capabilities relieve operators from the tedious task of manually driving the robot the whole time and pave the way towards a fully autonomous agricultural robot.

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Reactive navigation system based on H∞ control system and LiDAR readings on corn crops

Cited by 18 publications

References 22 publications

Mobile Robot Self-Localization with 2D Push-Broom LIDAR in a 2D Map

Mobile Robot Self-Localization with 2D Push-Broom LIDAR in a 2D Map

Under canopy light detection and ranging‐based autonomous navigation

2D LiDAR-based perception for under canopy autonomous scouting of small ground robots within narrow lanes of agricultural fields

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