Faster R-CNN-based Decision Making in a Novel Adaptive Dual-Mode Robotic Anchoring System

Shahin, Shahrooz; Sadeghian, Rasoul; Sareh, Sina

doi:10.1109/icra48506.2021.9560735

Cited by 3 publications

(2 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another sector where CNNs are used for decision making is for robots. An adaptive anchoring module is presented that enables robots to improve their mobility and manipulation capabilities [63]. In agricultural sectors, the decision making by CNNs is not that widespread.…”

Section: Spot Sprayingmentioning

confidence: 99%

Precision Chemical Weed Management Strategies: A Review and a Design of a New CNN-Based Modular Spot Sprayer

et al. 2022

View full text Add to dashboard Cite

Site-specific weed control offers a great potential for herbicide savings in agricultural crops without causing yield losses and additional weed management costs in the following years. Therefore, precision weed management is an efficient tool to meet the EU targets for pesticide reduction. This review summarizes different commercial technologies and prototypes for precision patch spraying and spot spraying. All the presented technologies have in common that they consist of three essential parts. (1) Sensors and classifiers for weed/crop detection, (2) Decision algorithms to decide whether weed control is needed and to determine a suitable type and rate of herbicide. Usually, decision algorithms are installed on a controller and (3) a precise sprayer with boom section control or single nozzle control. One point that differs between some of the techniques is the way the decision algorithms classify. They are based on different approaches. Green vegetation can be differentiated from soil and crop residues based on spectral information in the visible and near-infrared wavebands (“Green on Brown”). Those sensors can be applied for real-time on/off control of single nozzles to control weeds before sowing after conservation tillage and in the inter-row area of crops. More sophisticated imaging algorithms are used to classify weeds in crops (“Green on Green”). This paper will focus on Convolutional Neural Networks (CNN) for plant species identification. Alternatively, the position of each crop can be recorded during sowing/planting and afterward herbicides can be targeted to single weeds or larger patches of weeds if the economic weed threshold is exceeded. With a standardized protocol of data communication between sensor, controller and sprayer, the user can combine different sensors with different sprayers. In this review, an ISOBUS communication protocol is presented for a spot sprayer. Precision chemical weed control can be realized with tractor-mounted sprayers and autonomous robots. Commercial systems for both classes will be introduced and their economic and environmental benefits and limitations will be highlighted. Farmers ask for robust systems with less need for maintenance and flexible application in different crops.

show abstract

Section: Spot Sprayingmentioning

confidence: 99%

Precision Chemical Weed Management Strategies: A Review and a Design of a New CNN-Based Modular Spot Sprayer

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In order to avoid this, the anchoring system should adapt effective methods to attach to different contact surfaces taking into account their surface properties. This requires different modes of attachment to be deployable independent of one another, as opposed to their simultaneous deployment [1,21].…”

Section: Autonomous Decision Making In a Bioinspired Adaptive Robotic...mentioning

confidence: 99%

Autonomous Decision Making in a Bioinspired Adaptive Robotic Anchoring Module

Sadeghian

Sareh

Shahin

et al. 2021

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Self Cite

View full text Add to dashboard Cite

This paper proposes a bioinspired adaptive anchoring module that can be integrated into robots to enhance their mobility and manipulation abilities. The design of the module is inspired by the structure of the mouth in Chilean lamprey (Mordacia lapicida) where a combination of suction and several arrays of teeth with different sizes around the mouth opening is used for catching preys and anchoring onto them. The module can deploy a suitable mode of attachment, via teeth or vacuum suction, to different contact surfaces in response to the textural properties of those surfaces. In order to make a decision on the suitable mode of attachment, an original dataset of 500 images of outdoor and indoor surfaces was used to train a visual surface examination model using YOLOv3; a virtually real-time object detection algorithm. The mean average precision of the trained model was calculated to be 91%. We have conducted a series of pull-out tests to characterize the module's strength of attachments. The results of the experiments indicate that the anchoring module can withstand an applied detachment force of up to 70N and 30N when attached using teeth and vacuum suction, respectively.

show abstract